Introduction: Surgical 5/6 nephrectomy and adenine-induced kidney failure in rats are frequently used models of progressive renal failure. In both models, rats develop significant morphological changes in the kidneys and quantification of these changes can be used to measure the efficacy of prophylactic or therapeutic approaches. In this study, the Aperio Genie Pattern Recognition technology, along with the Positive Pixel Count, Nuclear and Rare Event algorithms were used to quantify histological changes in both rat renal failure models. Methods: Analysis was performed on digitized slides of whole kidney sagittal sections stained with either hematoxylin and eosin or immunohistochemistry with an anti-nestin antibody to identify glomeruli, regenerating tubular epithelium, and tubulointerstitial myofibroblasts. An anti-polymorphonuclear neutrophil (PMN) antibody was also used to investigate neutrophil tissue infiltration. Results: Image analysis allowed for rapid and accurate quantification of relevant histopathologic changes such as increased cellularity and expansion of glomeruli, renal tubular dilatation, and degeneration, tissue inflammation, and mineral aggregation. The algorithms provided reliable and consistent results in both control and experimental groups and presented a quantifiable degree of damage associated with each model. Conclusion: These algorithms represent useful tools for the uniform and reproducible characterization of common histomorphologic features of renal injury in rats.

Background: The use of adjuvant tamoxifen therapy in the treatment of estrogen receptor (ER) expressing breast carcinomas represents a major advance in personalized cancer treatment. Because there is no benefit (and indeed there is increased morbidity and mortality) associated with the use of tamoxifen therapy in ER-negative breast cancer, its use is restricted to women with ER expressing cancers. However, correctly classifying cancers as ER positive or negative has been challenging given the high reported false negative test rates for ER expression in surgical specimens. In this paper I model practice recommendations using published information from clinical trials to address the question of whether there is a false negative test rate above which it is more efficacious to forgo ER testing and instead treat all patients with tamoxifen regardless of ER test results. Methods: I used data from randomized clinical trials to model two different hypothetical treatment strategies: (1) the current strategy of treating only ER positive women with tamoxifen and (2) an alternative strategy where all women are treated with tamoxifen regardless of ER test results. The variables used in the model are literature-derived survival rates of the different combinations of ER positivity and treatment with tamoxifen, varying true ER positivity rates and varying false negative ER testing rates. The outcome variable was hypothetical 10-year survival. Results: The model predicted that there will be a range of true ER rates and false negative test rates above which it would be more efficacious to treat all women with breast cancer with tamoxifen and forgo ER testing. This situation occurred with high true positive ER rates and false negative ER test rates in the range of 20-30%. Conclusions: It is hoped that this model will provide an example of the potential importance of diagnostic error on clinical outcomes and furthermore will give an example of how the effect of that error could be modeled using real-world data from clinical trials.

The extent to which histopathology pattern recognition image analysis (PRIA) agrees with microscopic assessment has not been established. Thus, a commercial PRIA platform was evaluated in two applications using whole-slide images. Substantial agreement, lacking significant constant or proportional errors, between PRIA and manual morphometric image segmentation was obtained for pulmonary metastatic cancer areas (Passing/Bablok regression). Bland-Altman analysis indicated heteroscedastic measurements and tendency toward increasing variance with increasing tumor burden, but no significant trend in mean bias. The average between-methods percent tumor content difference was -0.64. Analysis of between-methods measurement differences relative to the percent tumor magnitude revealed that method disagreement had an impact primarily in the smallest measurements (tumor burden <3%). Regression-based 95% limits of agreement indicated substantial agreement for method interchangeability. Repeated measures revealed concordance correlation of >0.988, indicating high reproducibility for both methods, yet PRIA reproducibility was superior (C.V.: PRIA = 7.4, manual = 17.1). Evaluation of PRIA on morphologically complex teratomas led to diagnostic agreement with pathologist assessments of pluripotency on subsets of teratomas. Accommodation of the diversity of teratoma histologic features frequently resulted in detrimental trade-offs, increasing PRIA error elsewhere in images. PRIA error was nonrandom and influenced by variations in histomorphology. File-size limitations encountered while training algorithms and consequences of spectral image processing dominance contributed to diagnostic inaccuracies experienced for some teratomas. PRIA appeared better suited for tissues with limited phenotypic diversity. Technical improvements may enhance diagnostic agreement, and consistent pathologist input will benefit further development and application of PRIA.

Introduction: We aim to determine to what degree whole-slide images (WSI) can be compressed without impacting the ability of the pathologist to distinguish benign from malignant tissues. An underlying goal is to demonstrate the utility of a visual discrimination model (VDM) for predicting observer performance. Materials and Methods: A total of 100 regions of interest (ROIs) from a breast biopsy whole-slide images at five levels of JPEG 2000 compression (8:1, 16:1, 32:1, 64:1, and 128:1) plus the uncompressed version were shown to six pathologists to determine benign versus malignant status. Results: There was a significant decrease in performance as a function of compression ratio (F = 14.58, P < 0.0001). The visibility of compression artifacts in the test images was predicted using a VDM. Just-noticeable difference (JND) metrics were computed for each image, including the mean, median, ≥90th percentiles, and maximum values. For comparison, PSNR (peak signal-to-noise ratio) and Structural Similarity (SSIM) were also computed. Image distortion metrics were computed as a function of compression ratio and averaged across test images. All of the JND metrics were found to be highly correlated and differed primarily in magnitude. Both PSNR and SSIM decreased with bit rate, correctly reflecting a loss of image fidelity with increasing compression. Observer performance as measured by the Receiver Operating Characteristic area under the curve (ROC Az) was nearly constant up to a compression ratio of 32:1, then decreased significantly for 64:1 and 128:1 compression levels. The initial decline in Az occurred around a mean JND of 3, Minkowski JND of 4, and 99th percentile JND of 6.5. Conclusion: Whole-slide images may be compressible to relatively high levels before impacting WSI interpretation performance. The VDM metrics correlated well with artifact conspicuity and human performance.

Background: It is expected that health information technology (HIT) will deliver a safer, more efficient and effective health care system. The aim of this study was to undertake a qualitative and video-ethnographic examination of the impact of information technologies on work processes in the reception area of a Microbiology Department, to ascertain what changed, how it changed and the impact of the change. Materials and Methods: The setting for this study was the microbiology laboratory of a large tertiary hospital in Sydney. The study consisted of qualitative (interview and focus group) data and observation sessions for the period August 2005 to October 2006 along with video footage shot in three sessions covering the original system and the two stages of the Cerner implementation. Data analysis was assisted by NVivo software and process maps were produced from the video footage. Results: There were two laboratory information systems observed in the video footage with computerized provider order entry introduced four months later. Process maps highlighted the large number of pre data entry steps with the original system whilst the newer system incorporated many of these steps in to the data entry stage. However, any time saved with the new system was offset by the requirement to complete some data entry of patient information not previously required. Other changes noted included the change of responsibilities for the reception staff and the physical changes required to accommodate the increased activity around the data entry area. Conclusions: Implementing a new HIT is always an exciting time for any environment but ensuring that the implementation goes smoothly and with minimal trouble requires the administrator and their team to plan well in advance for staff training, physical layout and possible staff resource reallocation.

Handheld computing has had many applications in medicine, but relatively few in pathology. Most reported uses of handhelds in pathology have been limited to experimental endeavors in telemedicine or education. With recent advances in handheld hardware and software, along with concurrent advances in whole-slide imaging (WSI), new opportunities and challenges have presented themselves. This review addresses the current state of handheld hardware and software, provides a history of handheld devices in medicine focusing on pathology, and presents future use cases for such handhelds in pathology.

Introduction: An automated system for differential white blood cell (WBC) counting based on morphology can make manual differential leukocyte counts faster and less tedious for pathologists and laboratory professionals. We present an automated system for isolation and classification of WBCs in manually prepared, Wright stained, peripheral blood smears from whole slide images (WSI). Methods: A simple, classification scheme using color information and morphology is proposed. The performance of the algorithm was evaluated by comparing our proposed method with a hematopathologist's visual classification. The isolation algorithm was applied to 1938 subimages of WBCs, 1804 of them were accurately isolated. Then, as the first step of a two-step classification process, WBCs were broadly classified into cells with segmented nuclei and cells with nonsegmented nuclei. The nucleus shape is one of the key factors in deciding how to classify WBCs. Ambiguities associated with connected nuclear lobes are resolved by detecting maximum curvature points and partitioning them using geometric rules. The second step is to define a set of features using the information from the cytoplasm and nuclear regions to classify WBCs using linear discriminant analysis. This two-step classification approach stratifies normal WBC types accurately from a whole slide image. Results: System evaluation is performed using a 10-fold cross-validation technique. Confusion matrix of the classifier is presented to evaluate the accuracy for each type of WBC detection. Experiments show that the two-step classification implemented achieves a 93.9% overall accuracy in the five subtype classification. Conclusion: Our methodology achieves a semiautomated system for the detection and classification of normal WBCs from scanned WSI. Further studies will be focused on detecting and segmenting abnormal WBCs, comparison of 20× and 40× data, and expanding the applications for bone marrow aspirates.

Background: In 2007, our healthcare system established a clinical fellowship program in pathology informatics. In 2011, the program benchmarked its structure and operations against a 2009 white paper "Program requirements for fellowship education in the subspecialty of clinical informatics," endorsed by the Board of the American Medical Informatics Association (AMIA) that described a proposal for a general clinical informatics fellowship program. Methods: A group of program faculty members and fellows compared each of the proposed requirements in the white paper with the fellowship program's written charter and operations. The majority of white paper proposals aligned closely with the rules and activities in our program and comparison was straightforward. In some proposals, however, differences in terminology, approach, and philosophy made comparison less direct, and in those cases, the thinking of the group was recorded. After the initial evaluation, the remainder of the faculty reviewed the results and any disagreements were resolved. Results: The most important finding of the study was how closely the white paper proposals for a general clinical informatics fellowship program aligned with the reality of our existing pathology informatics fellowship. The program charter and operations of the program were judged to be concordant with the great majority of specific white paper proposals. However, there were some areas of discrepancy and the reasons for the discrepancies are discussed in the manuscript. Conclusions: After the comparison, we conclude that the existing pathology informatics fellowship could easily meet all substantive proposals put forth in the 2009 clinical informatics program requirements white paper. There was also agreement on a number of philosophical issues, such as the advantages of multiple fellows, the need for core knowledge and skill sets, and the need to maintain clinical skills during informatics training. However, there were other issues, such as a requirement for a 2-year fellowship and for informatics fellowships to be done after primary board certification, that pathology should consider carefully as it moves toward a subspecialty status and board certification.

Background: Sharing digital pathology images for enterprise- wide use into a picture archiving and communication system (PACS) is not yet widely adopted. We share our solution and 3-year experience of transmitting such images to an enterprise image server (EIS). Methods: Gross pathology images acquired by prosectors were integrated with clinical cases into the laboratory information system's image management module, and stored in JPEG2000 format on a networked image server. Automated daily searches for cases with gross images were used to compile an ASCII text file that was forwarded to a separate institutional Enterprise Digital Imaging and Communications in Medicine (DICOM) Wrapper (EDW) server. Concurrently, an HL7-based image order for these cases was generated, containing the locations of images and patient data, and forwarded to the EDW, which combined data in these locations to generate images with patient data, as required by DICOM standards. The image and data were then "wrapped" according to DICOM standards, transferred to the PACS servers, and made accessible on an institution-wide basis. Results: In total, 26,966 gross images from 9,733 cases were transmitted over the 3-year period from the laboratory information system to the EIS. The average process time for cases with successful automatic uploads (n=9,688) to the EIS was 98 seconds. Only 45 cases (0.5%) failed requiring manual intervention. Uploaded images were immediately available to institution- wide PACS users. Since inception, user feedback has been positive. Conclusions: Enterprise- wide PACS- based sharing of pathology images is feasible, provides useful services to clinical staff, and utilizes existing information system and telecommunications infrastructure. PACS-shared pathology images, however, require a "DICOM wrapper" for multisystem compatibility.

Objective: The image quality in whole slide imaging (WSI) is one of the most important issues for the practical use of WSI scanners. In this paper, we proposed an image quality evaluation method for scanned slide images in which no reference image is required. Methods: While most of the conventional methods for no-reference evaluation only deal with one image degradation at a time, the proposed method is capable of assessing both blur and noise by using an evaluation index which is calculated using the sharpness and noise information of the images in a given training data set by linear regression analysis. The linear regression coefficients can be determined in two ways depending on the purpose of the evaluation. For objective quality evaluation, the coefficients are determined using a reference image with mean square error as the objective value in the analysis. On the other hand, for subjective quality evaluation, the subjective scores given by human observers are used as the objective values in the analysis. The predictive linear regression models for the objective and subjective image quality evaluations, which were constructed using training images, were then used on test data wherein the calculated objective values are construed as the evaluation indices. Results: The results of our experiments confirmed the effectiveness of the proposed image quality evaluation method in both objective and subjective image quality measurements. Finally, we demonstrated the application of the proposed evaluation method to the WSI image quality assessment and automatic rescanning in the WSI scanner.

Introduction: Delta checks use two specimen test results taken in succession in order to detect test result changes greater than expected physiological variation. One of the most common and serious errors detected by delta checks is specimen mix-up errors. The positive and negative predictive values of delta checks for detecting specimen mix-up errors, however, are largely unknown. Materials and Methods: We addressed this question by first constructing a stochastic dynamic model using repeat test values for five analytes from approximately 8000 inpatients in Calgary, Alberta, Canada. The analytes examined were sodium, potassium, chloride, bicarbonate, and creatinine. The model simulated specimen mix-up errors by randomly switching a set number of pairs of second test results. Sensitivities and specificities were then calculated for each analyte for six combinations of delta check equations and cut-off values from the published literature. Results: Delta check specificities obtained from this model ranged from 50% to 99%; however the sensitivities were generally below 20% with the exception of creatinine for which the best performing delta check had a sensitivity of 82.8%. Within a plausible incidence range of specimen mix-ups the positive predictive values of even the best performing delta check equation and analyte became negligible. Conclusion: This finding casts doubt on the ongoing clinical utility of delta checks in the setting of low rates of specimen mix-ups.

Background: Microdissection testicular sperm extraction (micro-TESE) has replaced conventional testis biopsies as a method of choice for obtaining sperm for in vitro fertilization for men with nonobstructive azoospermia. A technical challenge of micro-TESE is that the low magnification inspection of the tubules with a surgical microscope is insufficient to definitively identify sperm-containing tubules, necessitating tissue removal and cytologic assessment. Full field optical coherence tomography (FFOCT) uses white light interference microscopy to generate quick high-resolution tomographic images of fresh (unprocessed and unstained) tissue. Furthermore, by using a nonlaser safe light source (150 W halogen lamp) for tissue illumination, it ensures that the sperm extracted for in vitro fertilization are not photo-damaged or mutagenized. Materials and Methods: A focal Sertoli-cell only rodent model was created with busulfan injection in adult rats. Ex vivo testicular tissues from both normal and busulfan-treated rats were imaged with a commercial modified FFOCT system, Light-CT ^TM , and the images were correlated with gold standard hematoxylin and eosin staining. Results: Light-CT ^TM identified spermatogenesis within the seminiferous tubules in freshly excised testicular tissue, without the use of exogenous contrast or fixation. Normal adult rats exhibited tubules with uniform size and shape (diameter 328 ±11 μm). The busulfan-treated animals showed marked heterogeneity in tubular size and shape (diameter 178 ± 35 μm) and only 10% contained sperm within the lumen. Conclusion : FFOCT has the potential to facilitate real-time visualization of spermatogenesis in humans, and aid in micro-TESE for men with infertility.

Background: Laboratory ordering functions within computerized provider order entry (CPOE) systems typically support the display of electronic alert messages to improve test utilization or implement new ordering policies. However, alert strategies have been shown to vary considerably in their success and the characteristics contributing to an alert's success are poorly understood. Improved methodologies are needed to evaluate alerts and their mechanisms of action. Materials and Methods: Clinicians order inpatient and emergency department laboratory tests using our institutional CPOE system. We analyzed user interaction data captured by our CPOE system to evaluate how clinicians responded to an alert. We evaluated an alert designed to implement an institutional policy restricting the indications for ordering creatine kinase-MB (CKMB). Results: Within 2 months of alert implementation, CKMB-associated searches declined by 79% with a corresponding decline in CKMB orders. Furthermore, while prior to alert implementation, clinicians searching for CKMB ultimately ordered this test 99% of the time, following implementation, only 60% of CKMB searches ultimately led to CKMB test orders. This difference presumably represents clinicians who reconsidered the need for CKMB in response to the alert, demonstrating the alert's just-in-time advisory capability. In addition, as clinicians repeatedly viewed the alert, there was a "dose-dependant" decrease in the fraction of searches without orders. This presumably reflects the alerting strategy's long-term educational component, as clinicians aware of the new policy will not search for CKMB when not indicated. Conclusions: Our analytic approach provides insight into the mechanism of a CPOE alert and demonstrates that alerts may act through a combination of just-in-time advice and longer term education. Use of this approach when implementing alerts may prove useful to improve the success of a given alerting strategy.

Background: There is a need for telemedicine, particularly in countries with large geographical areas and widely scattered low-density communities as is the case of the Canadian system, particularly if equality of care is to be achieved or the difference gap is to be narrowed between urban centers and more peripheral communities. Aims: 1. To validate teledermatopathology as a diagnostic tool in under-serviced areas; 2. To test its utilization in inflammatory and melanocytic lesions; 3. To compare the impact of 20× (0.5 μm/pixel) and 40× (0.25 μm/pixel) scans on the diagnostic accuracy. Materials and Methods: A total of 103 dermatopathology cases divided into three arms were evaluated by two pathologists and results compared. The first arm consisted of 79 consecutive routine cases (n=79). The second arm consisted of 12 inflammatory skin biopsies (n=12) and the third arm consisted of 12 melanocytic lesions (n=12). Diagnosis concordance was used to evaluate the first arm. Whereas concordance of preset objective findings were used to evaluate the second and third arms. Results: The diagnostic concordance rate for the first arm was 96%. The concordance rates of the objective findings for the second and third arms were 100%. The image quality was deemed superior to light microscopy for 40× scans. Conclusion: The current scanners produce high-resolution images that are adequate for evaluation of a variety of cases of different complexities.

Background: Immunohistochemistry (IHC) is an important tool to identify and quantify expression of certain proteins (antigens) to gain insights into the molecular processes in a diseased tissue. However, it is a challenge for pathologists to remember the discriminative characteristics of the growing number of such antigens across multiple diseases. The complexity of their expression patterns, fueled by continuous discoveries in molecular pathology, gives rise to a combinatorial explosion that places an unprecedented burden on a practicing pathologist and therefore increases cost and variability of IHC studies. Materials and Methods: To tackle these issues, we have developed antibody test optimized selection method, a novel informatics tool to help pathologists in improving the IHC antibody selection process. The method uses extensions of Shannon's information entropies and Bayesian probabilities to dynamically build an efficient diagnostic tree. Results: A comparative analysis of our method with the expert and World Health Organization classification guidelines showed that the proposed method brings threefold reduction in number of antibody tests required to reach a diagnostic conclusion. Conclusion: The developed method can significantly streamline the antibody test selection process, decrease associated costs and reduce inter- and intrapathologist variability in IHC decision-making.

Commodity graphics hardware has become a cost-effective parallel platform to solve many general computational problems. In medical imaging and more so in digital pathology, segmentation of multiple structures on high-resolution images, is often a complex and computationally expensive task. Shape-based level set segmentation has recently emerged as a natural solution to segmenting overlapping and occluded objects. However the flexibility of the level set method has traditionally resulted in long computation times and therefore might have limited clinical utility. The processing times even for moderately sized images could run into several hours of computation time. Hence there is a clear need to accelerate these segmentations schemes. In this paper, we present a parallel implementation of a computationally heavy segmentation scheme on a graphical processing unit (GPU). The segmentation scheme incorporates level sets with shape priors to segment multiple overlapping nuclei from very large digital pathology images. We report a speedup of 19× compared to multithreaded C and MATLAB-based implementations of the same scheme, albeit with slight reduction in accuracy. Our GPU-based segmentation scheme was rigorously and quantitatively evaluated for the problem of nuclei segmentation and overlap resolution on digitized histopathology images corresponding to breast and prostate biopsy tissue specimens.

Aims: The aim was to demonstrate a method for automated image analysis of immunohistochemically stained tissue samples for extracting features that correlate with patient disease. We address the problem of quantifying tumor tissue and segmenting and counting cell nuclei. Materials and Methods: Our method utilizes a flexible segmentation method based on sparse coding trained from representative image samples. Nuclei counting is based on a nucleus model that takes size, shape, and nucleus probability into account. Nuclei clustering and overlays are resolved using a gray-weighted distance transform. We obtain a probability measure for pixels belonging to a nucleus from our segmentation procedure. Experiments are carried out on two sets of immunohistochemically stained images - one set based on the estrogen receptor (ER) and the other on antigen KI-67. For the nuclei separation we have selected 207 ER image samples from 58 tissue micro array-cores corresponding to 58 patients and 136 KI-67 image samples also from 58 cores. The images are hand-annotated by marking the center position of each nucleus. For the ER data we have a total of 1006 nuclei and for the KI-67 we have 796 nuclei. Segmentation performance was evaluated in terms of missing nuclei, falsely detected nuclei, and multiple detections. The proposed method is compared to state-of-the-art Bayesian classification. Statistical analysis used: The performance of the proposed method and a state-of-the-art algorithm including variations thereof is compared using the Wilcoxon rank sum test. Results: For both the ER experiment and the KI-67 experiment the proposed method exhibits lower error rates than the state-of-the-art method. Total error rates were 4.8 % and 7.7 % in the two experiments, corresponding to an average of 0.23 and 0.45 errors per image, respectively. The Wilcoxon rank sum tests show statistically significant improvements over the state-of-the-art method. Conclusions: We have demonstrated a method and obtained good performance compared to state-of-the-art nuclei separation. The segmentation procedure is simple, highly flexible, and we demonstrate how it, in addition to the nuclei separation, can perform precise segmentation of cancerous tissue. The complexity of the segmentation procedure is linear in the image size and the nuclei separation is linear in the number of nuclei. Additionally the method can be parallelized to obtain high-speed computations.

Histological image analysis methods often employ machine-learning classifiers in order to adapt to the huge variability of histological images. To train these classifiers, the user must select samples of the relevant image objects. In the field of active learning, there has been much research on sampling strategies that exploit the uncertainty of the current classification in order to guide the user to maximally informative samples. Although these approaches have the potential to reduce the training effort and increase the classification accuracy, they are very rarely employed in practice. In this paper, we investigate the practical value of uncertainty sampling in the context of histological image analysis. To obtain practically meaningful results, we have devised an evaluation algorithm that simulates the way a human interacts with a user interface. The results show that uncertainty sampling outperforms common random or error sampling strategies by achieving more accurate classification results with a lower number of training images.

A biomechanical model-based deformable image registration incorporating specimen-specific changes in material properties is optimized and evaluated for correlating histology of clinical prostatectomy specimens with in vivo MRI. In this methodology, a three-step registration based on biomechanics calculates the transformations between histology and fixed, fixed and fresh, and fresh and in vivo states. A heterogeneous linear elastic material model is constructed based on magnetic resonance elastography (MRE) results. The ex vivo tissue MRE data provide specimen-specific information for the fresh and fixed tissue to account for the changes due to fixation. The accuracy of the algorithm was quantified by calculating the target registration error (TRE) by identifying naturally occurring anatomical points within the prostate in each image. TRE were improved with the deformable registration algorithm compared to rigid registration alone. The qualitative assessment also showed a good alignment between histology and MRI after the proposed deformable registration.

Histological investigation of a lesion induced by radiofrequency ablation (RFA) treatment provides ground-truth about the true lesion size, thus verifying the success or failure of the RFA treatment. This work presents a framework for registration of two-dimensional large-scale histological sections and three-dimensional CT data typically used to guide the RFA intervention. The focus is on the developed interactive methods for reconstruction of the histological volume data by fusion of histological and high-resolution CT (MicroCT) data and registration into CT data based on natural feature points. The framework is evaluated using RFA interventions in a porcine liver and applying medically relevant metrics. The results of registration are within clinically required precision targets; thus the developed methods are suitable for validation of the RFA treatment.

Several applications such as multiprojector displays and microscopy require the mosaicing of images (tiles) acquired by a camera as it traverses an unknown trajectory in 3D space. A homography relates the image coordinates of a point in each tile to those of a reference tile provided the 3D scene is planar. Our approach in such applications is to first perform pairwise alignment of the tiles that have imaged common regions in order to recover a homography relating the tile pair. We then find the global set of homographies relating each individual tile to a reference tile such that the homographies relating all tile pairs are kept as consistent as possible. Using these global homographies, one can generate a mosaic of the entire scene. We derive a general analytical solution for the global homographies by representing the pair-wise homographies on a connectivity graph. Our solution can accommodate imprecise prior information regarding the global homographies whenever such information is available. We also derive equations for the special case of translation estimation of an X-Y microscopy stage used in histology imaging and present examples of stitched microscopy slices of specimens obtained after radical prostatectomy or prostate biopsy. In addition, we demonstrate the superiority of our approach over tree-structured approaches for global error minimization.

Histological tissue preparation stages (e.g., cutting, sectioning, etc.) often introduce tissue distortions that prevent a smooth 3D reconstruction from being built. In this paper, we propose a method to correct histology distortions by running a piecewise registration scheme. It takes the information of several consecutive slices in a neighborhood into account. In order to achieve an accurate anatomic presentation, we run the method iteratively with the assistance from a pre-segmented brain atlas. The registration parameters are optimized to accommodate different brain sub-regions, e.g., cerebellum, hippocampus, etc. The results are evaluated by both visual and quantitative approaches. The proposed method has been proved to be robust enough for reconstructing an accurate and smooth mouse brain volume.

Background: Registration of high-resolution tissue images is a critical step in the 3D analysis of protein expression. Because the distance between images (~4-5μm thickness of a tissue section) is nearly the size of the objects of interest (~10-20μm cancer cell nucleus), a given object is often not present in both of two adjacent images. Without consistent correspondence of objects between images, registration becomes a difficult task. This work assesses the feasibility of current registration techniques for such images. Methods: We generated high resolution synthetic 3-D image data sets emulating the constraints in real data. We applied multiple registration methods to the synthetic image data sets and assessed the registration performance of three techniques (i.e., mutual information (MI), kernel density estimate (KDE) method [1], and principal component analysis (PCA)) at various slice thicknesses (with increments of 1μm) in order to quantify the limitations of each method. Results: Our analysis shows that PCA, when combined with the KDE method based on nuclei centers, aligns images corresponding to 5μm thick sections with acceptable accuracy. We also note that registration error increases rapidly with increasing distance between images, and that the choice of feature points which are conserved between slices improves performance. Conclusions: We used simulation to help select appropriate features and methods for image registration by estimating best-case-scenario errors for given data constraints in histological images. The results of this study suggest that much of the difficulty of stained tissue registration can be reduced to the problem of accurately identifying feature points, such as the center of nuclei.

With modern automated microscopes and digital cameras, pathologists no longer have to examine samples looking through microscope binoculars. Instead, the slide is digitized to an image, which can then be examined on a screen. This creates the possibility for computers to analyze the image. In this work, a fully automated approach to region of interest (ROI) segmentation in prostate biopsy images is proposed. This will allow the pathologists to focus on the most important areas of the image. The method proposed is based on level-set and mean filtering techniques for lumen centered expansion and cell density localization respectively. The novelty of the technique lies in the ability to detect complete ROIs, where a ROI is composed by the conjunction of three different structures, that is, lumen, cytoplasm, and cells, as well as regions with a high density of cells. The method is capable of dealing with full biopsies digitized at different magnifications. In this paper, results are shown with a set of 100 H and E slides, digitized at 5×, and ranging from 12 MB to 500 MB. The tests carried out show an average specificity above 99% across the board and average sensitivities of 95% and 80%, respectively, for the lumen centered expansion and cell density localization. The algorithms were also tested with images at 10× magnification (up to 1228 MB) obtaining similar results.

Histopathological images are an important resource for clinical diagnosis and biomedical research. From an image understanding point of view, the automatic annotation of these images is a challenging problem. This paper presents a new method for automatic histopathological image annotation based on three complementary strategies, first, a part-based image representation, called the bag of features, which takes advantage of the natural redundancy of histopathological images for capturing the fundamental patterns of biological structures, second, a latent topic model, based on non-negative matrix factorization, which captures the high-level visual patterns hidden in the image, and, third, a probabilistic annotation model that links visual appearance of morphological and architectural features associated to 10 histopathological image annotations. The method was evaluated using 1,604 annotated images of skin tissues, which included normal and pathological architectural and morphological features, obtaining a recall of 74% and a precision of 50%, which improved a baseline annotation method based on support vector machines in a 64% and 24%, respectively.

A computer-assisted system for histological prostate cancer diagnosis can assist pathologists in two stages: (i) to locate cancer regions in a large digitized tissue biopsy, and (ii) to assign Gleason grades to the regions detected in stage 1. Most previous studies on this topic have primarily addressed the second stage by classifying the preselected tissue regions. In this paper, we address the first stage by presenting a cancer detection approach for the whole slide tissue image. We propose a novel method to extract a cytological feature, namely the presence of cancer nuclei (nuclei with prominent nucleoli) in the tissue, and apply this feature to detect the cancer regions. Additionally, conventional image texture features which have been widely used in the literature are also considered. The performance comparison among the proposed cytological textural feature combination method, the texture-based method and the cytological feature-based method demonstrates the robustness of the extracted cytological feature. At a false positive rate of 6%, the proposed method is able to achieve a sensitivity of 78% on a dataset including six training images (each of which has approximately 4,000x7,000 pixels) and 1 1 whole-slide test images (each of which has approximately 5,000x23,000 pixels). All images are at 20X magnification.

Diabetes can be associated with a reduction in functional β cell mass, which must be restored if the disease is to be cured or progress is to be arrested. To study the cell count, it is also necessary to determine the number of nuclei within the insulin stained area. It can take a single experimentalist several months to complete a single study of this kind, results of which may still be quite subjective. In this paper, we propose a framework based on a novel measure of local symmetry for detection of cells. The local isotropic phase symmetry measure (LIPSyM) is designed to give high values at or near the cell centers. We demonstrate the effectiveness of our algorithm for detection of two types of specific cells in histology images, cells in mouse pancreatic sections and lymphocytes in human breast tissue. Experimental results for these two problems show that our algorithm performs better than human experts for the former problem, and outperforms the best reported results for the latter.

In this paper, we attempt to quantify the prognostic information embedded in multi-parametric histologic biopsy images to predict disease aggressiveness in estrogen receptor-positive (ER+) breast cancers (BCa). The novel methodological contribution is in the use of a multi-field-of-view (multi-FOV) framework for integrating image-based information from differently stained histopathology slides. The multi-FOV approach involves a fixed image resolution while simultaneously integrating image descriptors from many FOVs corresponding to different sizes. For each study, the corresponding risk score (high scores reflecting aggressive disease and vice versa), predicted by a molecular assay (Oncotype DX), is available and serves as the surrogate ground truth for long-term patient outcome. Using the risk scores, a trained classifier is used to identify disease aggressiveness for each FOV size. The predictions for each FOV are then combined to yield the final prediction of disease aggressiveness (good, intermediate, or poor outcome). Independent multi-FOV classifiers are constructed for (1) 50 image features describing the spatial arrangement of cancer nuclei (via Voronoi diagram, Delaunay triangulation, and minimum spanning tree graphs) in H and E stained histopathology and (2) one image feature describing the vascular density in CD34 IHC stained histopathology. In a cohort of 29 patients, the multi-FOV classifiers obtained by combining information from the H and E and CD34 IHC stained channels were able to distinguish low- and high-risk patients with an accuracy of 0.91 ± 0.02 and a positive predictive value of 0.94 ± 0.10, suggesting that a purely image-based assay could potentially replace more expensive molecular assays for making disease prognostic predictions.

Introduction: Critical values are reported to clinicians when laboratory values are life threatening and require immediate attention. To date no definitive critical value limit recommendations have been produced regarding therapeutic drug monitoring. Some laboratories choose to publish critical value lists online. These publicly available values may be accessed and potentially utilized by laboratory staff, patient care providers, and patients. Materials and Methods: A web-based search of laboratories associated with the Accreditation Council for Graduate Medical Education pathology residency programs was initiated to determine which therapeutic drugs had critical values and to examine the degree of variation in published critical values for these institutions. Results: Of the 107 institutions with university-based pathology training programs, 36 had published critical values online for review. Thirteen therapeutic drugs were investigated and the number of institutions reporting critical value limits for the drug, as well as the median, range, standard deviation, and the coefficient of variation of critical value concentration limits for each drug were determined. A number of the online critical value limits were deemed to be erroneous, most likely due to incorrectly listed units of measurement. Conclusions: There was a large degree of heterogeneity with regard to the chosen critical value limits for therapeutic drugs. This wide variance in critical values appears to be greater than that observed in interassay proficiency testing. Institutions should reexamine the rationale for their current critical value parameters and ensure that critical value limits and associated units are accurately published online.

Increased type I error resulting from multiple statistical comparisons remains a common problem in the scientific literature. This may result in the reporting and promulgation of spurious findings. One approach to this problem is to correct groups of P-values for "family-wide significance" using a Bonferroni correction or the less conservative Bonferroni-Holm correction or to correct for the "false discovery rate" with a Benjamini-Hochberg correction. Although several solutions are available for performing this correction through commercially available software there are no widely available easy to use open source programs to perform these calculations. In this paper we present an open source program written in Python 3.2 that performs calculations for standard Bonferroni, Bonferroni-Holm and Benjamini-Hochberg corrections.

Telecytology is the interpretation of cytology material at a distance using digital images. For more than a decade, pioneering efforts to introduce telecytology into clinical practice have been reported. A Medline search for "telecytology" and "cytology" reveals a voluminous literature, though much of what has been published to date is based on technologies that are rapidly becoming obsolete. The technological limitations of previous techniques, including the transmission of static digital images and dynamic streaming images, have limited telecytology to minor niches. The primary problem with these technologies is that the remote viewer can only see a small fraction of the material on the original slides, introducing the possibility of diagnostic error based not only on image quality but also on image selection. Remote robotic microscopy offers one possible solution to this problem, but to date has found limited acceptance, principally attributable to slow operating times. Whole slide imaging seems to be a much more promising solution, though cytology-specific literature regarding its use is still scant. The advent of whole slide imaging opens up new possibilities for telecytology by enabling high-quality images of entire cytology specimens to be available to anyone, anywhere via the Internet. Although challenges remain, especially with regard to capturing the full microscopy experience including multiple planes of focus and sharp high-powered images, rapidly advancing technology promises to overcome these limitations. Increasing application of whole slide imaging technology in surgical pathology will undoubtedly also increase its application to cytology due to the increasing affordability and practicality of the equipment as it serves a larger number of useful roles within a pathology department. The current and expanding applications of telecytology for clinical practice, education, quality assurance, and testing will be reviewed.

For many years pathologists have used Hematoxylin and Eosin (H&E), single marker immunohistochemistry (IHC) and in situ hybridization with manual analysis by microscopy or at best simple digital imaging. There is a growing trend to update pathology to a digital workflow to improve objectivity and productivity, as has been done in radiology. There is also a need for tissue-based multivariate biomarker assays to improve the accuracy of diagnostic, prognostic, and predictive testing. Multivariate tests are not compatible with the traditional single marker, manual analysis pathology methods but instead require a digital platform with brightfield and fluorescence imaging, quantitative image analysis, and informatics. Here we describe the use of the Hamamatsu NanoZoomer Digital Pathology slide scanner with HCImage software for combined brightfield and multiplexed fluorescence biomarker analysis and highlight its applications in biomarker research and pathology testing. This combined approach will be an important aid to pathologists in making critical diagnoses.

Background: In histopathology, the quantitative assessment of various morphologic features is based on methods originally conceived on specific areas observed through the microscope used. Failure to reproduce the same reference field of view using a different microscope will change the score assessed. Visualization of a digital slide on a screen through a dedicated viewer allows selection of the magnification. However, the field of view is rectangular, unlike the circular field of optical microscopy. In addition, the size of the selected area is not evident, and must be calculated. Materials and Methods: A digital slide morphometric system was conceived to reproduce the various methods published for assessing tumor budding in colorectal cancer. Eighteen international experts in colorectal cancer were invited to participate in a web-based study by assessing tumor budding with five different methods in 100 digital slides. Results: The specific areas to be tested by each method were marked by colored circles. The areas were grouped in a target-like pattern and then saved as an .xml file. When a digital slide was opened, the .xml file was imported in order to perform the measurements. Since the morphometric tool is composed of layers that can be freely moved on top of the digital slide, the technique was named digital slide dynamic morphometry. Twelve investigators completed the task, the majority of them performing the multiple evaluations of each of the cases in less than 12 minutes. Conclusions: Digital slide dynamic morphometry has various potential applications and might be a useful tool for the assessment of histologic parameters originally conceived for optical microscopy that need to be quantified.

Introduction: The increasing availability of whole slide imaging (WSI) data sets (digital slides) from glass slides offers new opportunities for the development of computer-aided diagnostic (CAD) algorithms. With the all-digital pathology workflow that these data sets will enable in the near future, literally millions of digital slides will be generated and stored. Consequently, the field in general and pathologists, specifically, will need tools to help extract actionable information from this new and vast collective repository. Methods: To address this limitation, we designed and implemented a tool (dCORE) to enable the systematic capture of image tiles with constrained size and resolution that contain desired histopathologic features. Results: In this communication, we describe a user-friendly tool that will enable pathologists to mine digital slides archives to create image microarrays (IMAs). IMAs are to digital slides as tissue microarrays (TMAs) are to cell blocks. Thus, a single digital slide could be transformed into an array of hundreds to thousands of high quality digital images, with each containing key diagnostic morphologies and appropriate controls. Current manual digital image cut-and-paste methods that allow for the creation of a grid of images (such as an IMA) of matching resolutions are tedious. Conclusion: The ability to create IMAs representing hundreds to thousands of vetted morphologic features has numerous applications in education, proficiency testing, consensus case review, and research. Lastly, in a manner analogous to the way conventional TMA technology has significantly accelerated in situ studies of tissue specimens use of IMAs has similar potential to significantly accelerate CAD algorithm development.

Background: Cytology poses different obstacles in whole slide imaging compared to surgical pathology slides. A single focal plane suffices for most of the latter, but cytology slides are thicker, potentially requiring multiple focal planes for adequate diagnostic information. Multiple focal planes adversely impact scanning time per slide, evaluation times, and file sizes. In this pilot study, we evaluated and compared the multilayer stack method to the extended focus algorithm as an alternative which collapses multiple focal planes into a single image, retaining only focused areas from each plane. Materials and Methods: 10 SurePath^; cervical cytology slides were scanned at three thickness settings: 18, 24, and 30 μm. Three scanners were used: (1) Hamamatsu Nanozoomer 2.0-HT, (2) 3DHISTECH Mirax scan, and (3) Bioimagene iScan Coreo Au. The Nanozoomer and iScan utilized multilayer stacking, while the Mirax files were composited by extended focus. Scan times and file sizes were recorded, and image quality compared. Results: The Nanozoomer stacks averaged 1.58 gb and around 25 min for each slide, while the iScan stacks ranged from 6.23 to 9.3 gb and took 34-50 min to scan. The Mirax images averaged 210 mb and took 13-20 min to scan. Multilayer stack image quality from both Nanozoomer and iScan was fairly comparable. The iScan revealed significant mechanical issues that did not correspond to user settings. The Mirax images showed worrisome loss of crisp focus detail, worsening with increasing focal planes and impacting assessment of nuclear contours and chromatin detail. Conclusions: The optimal number of focal planes remains unknown for cytology. Multilayer stacks require excessive scanning time, network bandwidth, and file storage. Extended focus was evaluated as an alternative, but significant image quality issues were revealed. Further large-scale studies are needed to assess their clinical impact.

Introduction: A lot of attention has been generated in recent years by digital pathology and telepathology. Multiple reasons for and barriers to effective adoption are discussed in the current literature. Digital slides are the most promising medium at this time. The goal of our study was to evaluate whether the change in the methodology, particularly utilizing the so-called high-definition hematoxylin and eosin (H and E) slides, enhanced the quality of the final digital slide, and whether pathologists who tested the results perceived this as a difference in quality. Methods: The study was a blinded comparison of digital slides prepared using two methods: standard H&E batch staining and automated individual "high definition" HD HE staining. Four pathologists have compared 80 cases stained with each method. Results: The results discussed in this study show potential promise that the utilization of protocol(s) adapted for tissue and for imaging might be preferable for digital pathology in at least some of the pathology subspecialties. In particular, the protocol evaluated here was capable of turning out digital slides that had more contrast and detail, and therefore were perceived to provide enhanced diagnostically significant information for the pathologist.

Accurate focusing is a critical challenge of whole slide imaging, primarily due to inherent tissue topography variability. Traditional line scanning and tile-based scanning systems are limited in their ability to acquire a high degree of focus points while still maintaining high throughput. This review examines limitations with first-generation whole slide scanning systems and explores a novel approach that employs continuous autofocus, referred to as independent dual sensor scanning. This "second-generation" concept decouples image acquisition from focusing, allowing for rapid scanning while maintaining continuous accurate focus. The technical concepts, merits, and limitations of this method are explained and compared to that of a traditional whole slide scanning system.

Background: Quality assurance (QA) programs in cytopathology laboratories in the USA currently primarily involve the review of Pap tests per clinical laboratory improvement amendments of 1988 federal regulations. A pre-signout quality assurance tool (PQAT) at our institution allows the laboratory information system (LIS) to also automatically and randomly select an adjustable percentage of non-gynecological cytopathology cases for review before release of the final report. The aim of this study was to review our experience and the effectiveness of this novel PQAT tool in cytology. Materials and Methods: Software modifications in the existing LIS application (CoPathPlus, Cerner) allow for the random QA of 8% of cases prior to signout. Selected cases are assigned to a second QA cytopathologist for review and all agreement and disagreements tracked. Detected errors are rectified before the case is signed out. Data from cases selected for PQAT over an 18-month period were collected and analyzed. Results: The total number of non-gynecological cases selected for QA review was 1339 (7.45%) out of 17,967 cases signed out during this time period. Most (1304) cases (97.4%) had an agreement in diagnosis. In 2.6% of cases, there were disagreements, including 34 minor and only 1 major disagreement. Average turnaround time of cases selected for review was not significantly altered. Conclusion: The PQAT provides a prospective QA mechanism in non-gynecological cytopathology to prevent diagnostic errors from occurring. This LIS-driven tool allows for peer review and corrective action to be taken prior to reporting without delaying turnaround time, thereby improving patient safety.

Introduction: With recent advances, it is now possible to view whole slide images (WSI) on mobile, high-resolution, viewing devices (MVD). This creates a new paradigm in which MVDs may be used for consultation and/or diagnosis. Validation of the results with devices is important for practitioners and regulators. We evaluated the use of MVDs in frozen section (FS) interpretation. Methods: A series of 72 consecutive FS cases were selected for potential inclusion in the study. A 67 case subset of these were successfully scanned at 20x magnification. Scan times were recorded. A sample of WSI FS cases, with gross and clinical information, was presented to six pathologists on an iPad MVD using the Interpath application. Times to diagnosis were recorded. Results were compared with the original reported and final diagnosis. Participants also completed a survey assessing image quality, interface, and diagnostic comfort level. Results: Scan times averaged two minutes and 46 seconds per slide, (standard deviation [SD] 2 minutes 46 seconds). Evaluation times averaged 4 minutes and 59 seconds per case, range to 13 minutes and 50 seconds, SD 3 minutes 48 seconds. Concordance between initial FS diagnosis and rendered through the MVD was 89%. Minor discrepancies made up 8% and major disagreements 3%. The kappa statistic for this series is 0.85. Participants rated the experience at 5 on a 10-point scale, range 3 to 7. Two-thirds found the image quality to be adequate, half were satisfied with image resolution, and 33% would be willing to make a diagnosis on the iPad, plus one only for special cases. Five of six respondents (83%) found the navigation with the study software difficult. Conclusion: Image fidelity and resolution makes the iPad potentially suitable for WSI evaluation of FS. Acceptable accuracy is attainable for FS interpretation. But, although possible to obtain acceptable results, use of the iPad with Interpath to view WSI is not easy and meets user resistance. The obstacle of slide navigation at high magnification could introduce frustrations, delays, or errors.

Background: The Tissue Microarray Data Exchange Specification (TMA DES) is an XML specification for encoding TMA experiment data. While TMA DES data is encoded in XML, the files that describe its syntax, structure, and semantics are not. The DTD format is used to describe the syntax and structure of TMA DES, and the ISO 11179 format is used to define the semantics of TMA DES. However, XML Schema can be used in place of DTDs, and another XML encoded format, RDF, can be used in place of ISO 11179. Encoding all TMA DES data and metadata in XML would simplify the development and usage of programs which validate and parse TMA DES data. XML Schema has advantages over DTDs such as support for data types, and a more powerful means of specifying constraints on data values. An advantage of RDF encoded in XML over ISO 11179 is that XML defines rules for encoding data, whereas ISO 11179 does not. Materials and Methods: We created an XML Schema version of the TMA DES DTD. We wrote a program that converted ISO 11179 definitions to RDF encoded in XML, and used it to convert the TMA DES ISO 11179 definitions to RDF. Results: We validated a sample TMA DES XML file that was supplied with the publication that originally specified TMA DES using our XML Schema. We successfully validated the RDF produced by our ISO 11179 converter with the W3C RDF validation service. Conclusions: All TMA DES data could be encoded using XML, which simplifies its processing. XML Schema allows datatypes and valid value ranges to be specified for CDEs, which enables a wider range of error checking to be performed using XML Schemas than could be performed using DTDs.

Background: Whole slide imaging (WSI) makes it possible to capture images of an entire histological slide. WSI has established roles in surgical pathology, including support of off-site frozen section interpretation, primary diagnosis, educational activities, and laboratory quality assurance (QA) activities. Analyses of the cost of WSI have traditionally been based solely on direct costs and diagnostic accuracy; however, these types of analyses largely ignore workflow and cost issues that arise as a result of redundancy, the need for additional staffing, and customized software development when WSI is integrated into routine diagnostic surgical pathology. The pre-scan, scan, and post-scan costs; quality control and QA costs; and IT process costs can be significant, and consequently, pathology groups can find it difficult to perform a realistic cost-benefit analysis of adding WSI to their practice. Materials and Methods: In this paper, we report a "value added" approach developed to guide our decisions regarding integration of WSI into surgical pathology practice. The approach focuses on specific operational measures (cost, time, and enhanced patient care) and practice settings (clinical, education, and research) to identify routine activities in which the addition of WSI can provide improvements. Results: When applied to our academic pathology group practice, the value added approach resulted in expanded and improved operations, as demonstrated by outcome based measures. Conclusion: A value added can be used to perform a realistic cost-benefit analysis of integrating WSI into routine surgical pathology practice.

Context: Whole slide imaging (WSI) for digital pathology involves the rapid automated acquisition of multiple high-power fields from a microscope slide containing a tissue specimen. Capturing each field in the correct focal plane is essential to create high-quality digital images. Others have described a novel focusing method which reduces the number of focal planes required to generate accurate focus. However, this method was not applied dynamically in an automated WSI system under continuous motion. Aims: This report measures the accuracy of this method when applied in a rapid continuous scan mode using a dual sensor WSI system with interleaved acquisition of images. Methods: We acquired over 400 tiles in a "stop and go" scan mode, surveying the entire z depth in each tile and used this as ground truth. We compared this ground truth focal height to the focal height determined using a rapid 3-point focus algorithm applied dynamically in a continuous scanning mode. Results: Our data showed the average focal height error of 0.30 (±0.27) μm compared to ground truth, which is well within the system's depth of field. On a tile by tile assessment, approximately 95% of the tiles were within the system's depth of field. Further, this method was six times faster than acquiring tiles compared to the same method in a non-continuous scan mode. Conclusions: The data indicates that the method employed can yield a significant improvement in scan speed while maintaining highly accurate autofocusing.

Introduction: Spatially invariant vector quantization (SIVQ) is a texture and color-based image matching algorithm that queries the image space through the use of ring vectors. In prior studies, the selection of one or more optimal vectors for a particular feature of interest required a manual process, with the user initially stochastically selecting candidate vectors and subsequently testing them upon other regions of the image to verify the vector's sensitivity and specificity properties (typically by reviewing a resultant heat map). In carrying out the prior efforts, the SIVQ algorithm was noted to exhibit highly scalable computational properties, where each region of analysis can take place independently of others, making a compelling case for the exploration of its deployment on high-throughput computing platforms, with the hypothesis that such an exercise will result in performance gains that scale linearly with increasing processor count. Methods: An automated process was developed for the selection of optimal ring vectors to serve as the predicate matching operator in defining histopathological features of interest. Briefly, candidate vectors were generated from every possible coordinate origin within a user-defined vector selection area (VSA) and subsequently compared against user-identified positive and negative "ground truth" regions on the same image. Each vector from the VSA was assessed for its goodness-of-fit to both the positive and negative areas via the use of the receiver operating characteristic (ROC) transfer function, with each assessment resulting in an associated area-under-the-curve (AUC) figure of merit. Results: Use of the above-mentioned automated vector selection process was demonstrated in two cases of use: First, to identify malignant colonic epithelium, and second, to identify soft tissue sarcoma. For both examples, a very satisfactory optimized vector was identified, as defined by the AUC metric. Finally, as an additional effort directed towards attaining high-throughput capability for the SIVQ algorithm, we demonstrated the successful incorporation of it with the MATrix LABoratory (MATLAB ^TM ) application interface. Conclusion: The SIVQ algorithm is suitable for automated vector selection settings and high throughput computation.

Whole slide imaging (WSI), or "virtual" microscopy, involves the scanning (digitization) of glass slides to produce "digital slides". WSI has been advocated for diagnostic, educational and research purposes. When used for remote frozen section diagnosis, WSI requires a thorough implementation period coupled with trained support personnel. Adoption of WSI for rendering pathologic diagnoses on a routine basis has been shown to be successful in only a few "niche" applications. Wider adoption will most likely require full integration with the laboratory information system, continuous automated scanning, high-bandwidth connectivity, massive storage capacity, and more intuitive user interfaces. Nevertheless, WSI has been reported to enhance specific pathology practices, such as scanning slides received in consultation or of legal cases, of slides to be used for patient care conferences, for quality assurance purposes, to retain records of slides to be sent out or destroyed by ancillary testing, and for performing digital image analysis. In addition to technical issues, regulatory and validation requirements related to WSI have yet to be adequately addressed. Although limited validation studies have been published using WSI there are currently no standard guidelines for validating WSI for diagnostic use in the clinical laboratory. This review addresses the current status of WSI in pathology related to regulation and validation, the provision of remote and routine pathologic diagnoses, educational uses, implementation issues, and the cost-benefit analysis of adopting WSI in routine clinical practice.

Clinicians have traditionally ordered laboratory tests using paper-based orders and requisitions. However, paper orders are becoming increasingly incompatible with the complexities, challenges, and resource constraints of our modern healthcare systems and are being replaced by electronic order entry systems. Electronic systems that allow direct provider input of diagnostic testing or medication orders into a computer system are known as Computerized Provider Order Entry (CPOE) systems. Adoption of laboratory CPOE systems may offer institutions many benefits, including reduced test turnaround time, improved test utilization, and better adherence to practice guidelines. In this review, we outline the functionality of various CPOE implementations, review the reported benefits, and discuss strategies for using CPOE to improve the test ordering process. Further, we discuss barriers to the implementation of CPOE systems that have prevented their more widespread adoption.

Patient safety initiatives throughout the anatomic laboratory and in biorepository laboratories have mandated increasing emphasis on the need for accurately identifying and tracking biospecimen assets throughout their production lifecycle and for archiving/retrieval purposes. However, increasing production volume along with complex workflow characteristics, reliance on manual production processes, and required asset movement to disparate destinations throughout asset lifecycles continue to challenge laboratory efforts. Radio Frequency Identification (RFID) technology, use of radio waves to communicate data between electronic tags attached to objects and a reader, shows significant potential to facilitate and overcome these hurdles. Advantages over traditional barcode labeling include readability without direct line-of-sight alignment to the reader, ability to read multiple tags simultaneously, higher data storage capacity, faster data transmission rate, and capacity to perform multiple read-writes of data to the tag. Most importantly, use of radio waves decreases the need to manually scan each asset, and at each step, identification or tracking event is needed. Temperature monitoring by on-board sensors and three-dimensional position tracking are additional potential benefits of using RFID technology. To date, barriers to implementation of RFID systems in the anatomic laboratory include increased associated costs of tags and readers, system software, data security concerns, lack of specific data standards for stored information, and potential for technological obsolescence during decades of specimen storage. Novel RFID production techniques and increased production capacity are projected to lower costs of some tags to a few cents each. Potentially, information security concerns can be addressed by techniques such as shielding, data encryption, and tag pseudonyms. Commitment by stakeholder groups to develop RFID tag data standards for anatomic pathology and biorepository laboratories could avoid or mitigate the "islands of data" dilemma presented by barcode usage where there are innumerable standards and a consequent paucity of hardware or software "plug and play" interoperability. Work remains to be done to establish the durability and appropriate shielding of individual tag types for use in harsh laboratory environmental conditions, and for long-term archival storage. Finally, given the requirements for long-term storage of biospecimen assets, consideration should be given to ways of mitigating data isolation due to eventual technological obsolescence of a particular RFID technology or software.

Background: Identification of individual prostatic glandular structures is an important prerequisite to quantitative histological analysis of prostate cancer with the aid of a computer. We have developed a computer method to segment individual glandular units and to extract quantitative image features, for computer identification of prostatic adenocarcinoma. Methods: Two sets of digital histology images were used: database I (n = 57) for developing and testing the computer technique, and database II (n = 116) for independent validation. The segmentation technique was based on a k-means clustering and a region-growing method. Computer segmentation results were evaluated subjectively and also compared quantitatively against manual gland outlines, using the Jaccard similarity measure. Quantitative features that were extracted from the computer segmentation results include average gland size, spatial gland density, and average gland circularity. Linear discriminant analysis (LDA) was used to combine quantitative image features. Classification performance was evaluated with receiver operating characteristic (ROC) analysis and the area under the ROC curve (AUC). Results: Jaccard similarity coefficients between computer segmentation and manual outlines of individual glands were between 0.63 and 0.72 for non-cancer and between 0.48 and 0.54 for malignant glands, respectively, similar to an interobserver agreement of 0.79 for non-cancer and 0.75 for malignant glands, respectively. The AUC value for the features of average gland size and gland density combined via LDA was 0.91 for database I and 0.96 for database II. Conclusions: Using a computer, we are able to delineate individual prostatic glands automatically and identify prostatic adenocarcinoma accurately, based on the quantitative image features extracted from computer-segmented glandular structures.

Background: The systematic analysis of imaged pathology specimens often results in a vast amount of morphological information at both the cellular and sub-cellular scales. While microscopy scanners and computerized analysis are capable of capturing and analyzing data rapidly, microscopy image data remain underutilized in research and clinical settings. One major obstacle which tends to reduce wider adoption of these new technologies throughout the clinical and scientific communities is the challenge of managing, querying, and integrating the vast amounts of data resulting from the analysis of large digital pathology datasets. This paper presents a data model, which addresses these challenges, and demonstrates its implementation in a relational database system. Context: This paper describes a data model, referred to as Pathology Analytic Imaging Standards (PAIS), and a database implementation, which are designed to support the data management and query requirements of detailed characterization of micro-anatomic morphology through many interrelated analysis pipelines on whole-slide images and tissue microarrays (TMAs). Aims: (1) Development of a data model capable of efficiently representing and storing virtual slide related image, annotation, markup, and feature information. (2) Development of a database, based on the data model, capable of supporting queries for data retrieval based on analysis and image metadata, queries for comparison of results from different analyses, and spatial queries on segmented regions, features, and classified objects. Settings and Design: The work described in this paper is motivated by the challenges associated with characterization of micro-scale features for comparative and correlative analyses involving whole-slides tissue images and TMAs. Technologies for digitizing tissues have advanced significantly in the past decade. Slide scanners are capable of producing high-magnification, high-resolution images from whole slides and TMAs within several minutes. Hence, it is becoming increasingly feasible for basic, clinical, and translational research studies to produce thousands of whole-slide images. Systematic analysis of these large datasets requires efficient data management support for representing and indexing results from hundreds of interrelated analyses generating very large volumes of quantifications such as shape and texture and of classifications of the quantified features. Materials and Methods: We have designed a data model and a database to address the data management requirements of detailed characterization of micro-anatomic morphology through many interrelated analysis pipelines. The data model represents virtual slide related image, annotation, markup and feature information. The database supports a wide range of metadata and spatial queries on images, annotations, markups, and features. Results: We currently have three databases running on a Dell PowerEdge T410 server with CentOS 5.5 Linux operating system. The database server is IBM DB2 Enterprise Edition 9.7.2. The set of databases consists of 1) a TMA database containing image analysis results from 4740 cases of breast cancer, with 641 MB storage size; 2) an algorithm validation database, which stores markups and annotations from two segmentation algorithms and two parameter sets on 18 selected slides, with 66 GB storage size; and 3) an in silico brain tumor study database comprising results from 307 TCGA slides, with 365 GB storage size. The latter two databases also contain human-generated annotations and markups for regions and nuclei. Conclusions: Modeling and managing pathology image analysis results in a database provide immediate benefits on the value and usability of data in a research study. The database provides powerful query capabilities, which are otherwise difficult or cumbersome to support by other approaches such as programming languages. Standardized, semantic annotated data representation and interfaces also make it possible to more efficiently share image data and analysis results.

All hospitals deal with patient deaths. Multiple departments and personnel must be coordinated to ensure that decedents are safely managed. Prior to 2004, at the University of Pittsburgh Medical Center (UPMC), when a patient passed away, the process of alerting involved personnel, transporting the decedent, and tracking the completion of clinical documents was cumbersome and inefficient. In order to address these concerns, UPMC Remains Tracker, a web-based application, was developed to improve the efficiency and simplify the logistics related to the management of patient deaths. The UPMC Information Services division developed UPMC Remains Tracker, an application that tracks decedents' locations, documentation status, and autopsy status within UPMC hospitals. We assessed qualitative improvement in decedent remains tracking, decedent paperwork management, and staff satisfaction and compliance. UPMC Remains Tracker improved the process of tracking decedents' locations, identifying involved personnel, monitoring autopsy requests, and determining the availability for funeral home transportation. Resident satisfaction with UPMC Remains Tracker was generally positive and scored as "Improved efficiency" and makes identifying and tracking decedents "Much easier". Additionally, the nursing staff reacted favorably to the application. A retrospective review of the use of the application in the management of 100 decedents demonstrated a 93% compliance rate. Among the cases requiring an autopsy, there was a 90% compliance rate. The process of tracking decedents, their paperwork, involved staff, and decedent autopsy status is often inefficient. This assessment suggests that incorporating new technologies such as UPMC Remains Tracker into the management of hospital deaths provides accurate tracking of remains, streamlines the administrative tasks associated with deaths, and increases nursing and resident satisfaction and compliance.

Background: Here, we report the first use of a commercial prototype of full-field optical coherence tomography called Light-CT ^TM . Based on the principle of white light interferometry, Light-CT ^TM generates quick high-resolution three-dimensional tomographic images from unprocessed tissues. Its advantage over the current intra-surgical diagnostic standard, i.e. frozen section analysis, lies in the absence of freezing artifacts, which allows real-time diagnostic impressions, and/or for the tissues to be triaged for subsequent conventional histopathology. Materials and Methods: In this study, we recapitulate known normal histology in nine formalin fixed ex vivo rat organs (skin, heart, lung, liver, stomach, kidney, prostate, urinary bladder, and testis). Large surface and virtually sectioned stacks of images at varying depths were acquired by a pair of 10x/0.3 numerical aperture water immersion objectives, processed and visualized in real time. Results: Normal histology of the following organs was recapitulated by identifying various tissue microstructures. Skin: epidermis, dermal-epidermal junction and hair follicles with surrounding sebaceous glands in the dermis. Stomach: mucosa with surface pits, submucosa, muscularis propria and serosa. Liver: hepatocytes separated by sinusoidal spaces, central veins and portal triad. Kidney: convoluted tubules, medullary rays (straight tubules) and collecting ducts. Prostate: acini and fibro-muscular stroma. Lung: bronchi, bronchioles, alveolar ducts, alveoli and pleura. Urinary bladder: urothelium, lamina propria, muscularis propria, and serosa. Testis: seminiferous tubules with intra-tubular sperms. Conclusion: Light-CT ^TM is a powerful imaging tool to perform fast histology on fresh and fixed tissues, without introducing artifacts. Its compact size, ease of handling, fast image acquisition and safe incident light levels makes it well-suited for various intra-operative and intra-procedural triaging and decision making applications.

Whole slide digital imaging technology has matured considerably over the past decade. Applications in pathology education are widespread and are rapidly transforming the manner in which medical students learn pathology and histology, and they have a novel and significant impact on postgraduate continuing medical education. Whole slide digital images for use in pathology graduate education have been slower in adoption and remain much less widespread. Emphasis on professional competency by the Accreditation Council on Graduate Medical Education (ACGME) and credentialing organizations, however, appear poised to significantly increase. The convergence of these two forces is propitious for pathology training. This article examines the opportunities for the use of whole slide images (WSI) in pathology residency training along with the developing potential uses in each of the areas of competency, as categorized by the ACGME. Barriers to WSI adoption in the pathology community are identified along with potentially significant promoters for adoption in training and practice. Current literature and recent presentations are reviewed. Digital pathology coupled with emphasis on competency is a shift of tremendous magnitude that can dramatically improve our abilities to help trainees acquire, demonstrate, and maintain the skills to practice pathology in the generation ahead.

As digital slides need a lot of storage space, lack of a singular method to acquire and store these large, two-dimensional images has been a major stumbling block in the universal acceptance of this technology. The DICOMS Standard Committee Working Group 26 has put in a tremendous effort to standardize storage methods so that they are more in line with currently available PACS in most hospitals for storage of radiology images. A recent press release (Supplement 145) of these standards was hailed by one and all involved in the field of digital pathology as it will make it easier for hospitals to integrate digital pathology into their already established systems without adding too much overhead costs. Besides, it will enable different vendors developing the scanners to upgrade their products to storage systems that are common across all systems.

Background: Patient identification (ID) errors in point-of-care testing (POCT) can cause test results to be transferred to the wrong patient's chart or prevent results from being transmitted and reported. Despite the implementation of patient barcoding and ongoing operator training at our institution, patient ID errors still occur with glucose POCT. The aim of this study was to develop a solution to reduce identification errors with POCT. Materials and Methods: Glucose POCT was performed by approximately 2,400 clinical operators throughout our health system. Patients are identified by scanning in wristband barcodes or by manual data entry using portable glucose meters. Meters are docked to upload data to a database server which then transmits data to any medical record matching the financial number of the test result. With a new model, meters connect to an interface manager where the patient ID (a nine-digit account number) is checked against patient registration data from admission, discharge, and transfer (ADT) feeds and only matched results are transferred to the patient's electronic medical record. With the new process, the patient ID is checked prior to testing, and testing is prevented until ID errors are resolved. Results: When averaged over a period of a month, ID errors were reduced to 3 errors/month (0.015%) in comparison with 61.5 errors/month (0.319%) before implementing the new meters. Conclusion: Patient ID errors may occur with glucose POCT despite patient barcoding. The verification of patient identification should ideally take place at the bedside before testing occurs so that the errors can be addressed in real time. The introduction of an ADT feed directly to glucose meters reduced patient ID errors in POCT.

Background: Telemedicine has emerged as an efficient means of distributing professional medical expertise over a broad geographic area with few limitations to the various services that can be provided around the globe. Telepathology is particularly well suited to distributing subspecialty expertise in certain environments in an economical fashion, while preserving centers of excellence. Materials and Methods: After a decade of intrainstitutional teleneuropathology for intraoperative consultation, we expanded our practice to cross state lines and communicate between geographically and financially separate medical centers. Results: The result was an effective means of distributing neuropathological expertise while at the same time preserving a professional center of excellence. While technical and legal (i.e., physician licensing) barriers were surmounted, expected and unexpected issues related to communication required commitment on the part of multiple individuals with diverse expertise and responsibilities. Conclusion: Lessons learned from this successful venture can be used to facilitate future efforts in this ever-growing practical vehicle for distributing pathology subspecialty expertise.