Digitized slides produced by whole slide image scanners can be easily shared over a network or by transferring image files to optical or other data storage devices. Navigation of digitized slides is interactive and intended to simulate viewing glass slides with a microscope (virtual microscopy). Image viewing software permits users to edit, annotate, analyze, and easily share whole slide images (WSI). As a result, WSI have begun to replace the traditional light microscope, offering a myriad of opportunities for education. This article focuses on current applications of WSI in education and proficiency testing. WSI has been successfully explored for graduate education (medical, dental, and veterinary schools), training of pathology residents, as an educational tool in allied pathology schools (e.g., cytotechnology), for virtual tracking and tutoring, tele-education (tele-conferencing), e-learning, virtual workshops, at tumor boards, with interactive publications, and on examinations. WSI supports flexible and cost-effective distant learning and augments problem-oriented teaching, competency evaluation, and proficiency testing. WSI viewed on touchscreen displays and with tablet technology are especially beneficial for education. Further investigation is necessary to develop superior WSI applications that better support education and to design viewing stations with ergonomic tools that improve the WSI-human interface and navigation of virtual slides. Studies to determine the impact of training pathologists without exposure to actual glass slides are also needed.

Several modes of telepathology exist including static (store-and-forward), dynamic (live video streaming or robotic microscopy), and hybrid technology involving whole slide imaging (WSI). Telepathology has been employed at the University of Pittsburgh Medical Center (UPMC) for over a decade at local, national, and international sites. All modes of telepathology have been successfully utilized to exploit our institutions subspecialty expertise and to compete for pathology services. This article discusses the experience garnered at UPMC with each of these teleconsultation methods. Static and WSI telepathology systems have been utilized for many years in transplant pathology using a private network and client-server architecture. Only minor clinically significant differences of opinion were documented. In hematopathology, the CellaVision^® system is used to transmit, via email, static images of blood cells in peripheral blood smears for remote interpretation. While live video streaming has remained the mode of choice for providing immediate adequacy assessment of cytology specimens by telecytology, other methods such as robotic microscopy have been validated and shown to be effective. Robotic telepathology has been extensively used to remotely interpret intra-operative neuropathology consultations (frozen sections). Adoption of newer technology and increased pathologist experience has improved accuracy and deferral rates in teleneuropathology. A digital pathology consultation portal (https://pathconsult.upmc.com/) was recently created at our institution to facilitate digital pathology second opinion consults, especially for WSI. The success of this web-based tool is the ability to handle vendor agnostic, large image files of digitized slides, and ongoing user-friendly customization for clients and teleconsultants. It is evident that the practice of telepathology at our institution has evolved in concert with advances in technology and user experience. Early and continued adoption of telepathology has promoted additional digital pathology resources that are now being leveraged for other clinical, educational, and research purposes.

In-house software development for use in a clinical laboratory is a controversial issue. Many of the objections raised are based on outdated software development practices, an exaggeration of the risks involved, and an underestimation of the benefits that can be realized. Buy versus build analyses typically do not consider total costs of ownership, and unfortunately decisions are often made by people who are not directly affected by the workflow obstacles or benefits that result from those decisions. We have been developing custom software for clinical use for over a decade, and this article presents our perspective on this practice. A complete analysis of the decision to develop or purchase must ultimately examine how the end result will mesh with the departmental workflow, and custom-developed solutions typically can have the greater positive impact on efficiency and productivity, substantially altering the decision balance sheet. Involving the end-users in preparation of the functional specifications is crucial to the success of the process. A large development team is not needed, and even a single programmer can develop significant solutions. Many of the risks associated with custom development can be mitigated by a well-structured development process, use of open-source tools, and embracing an agile development philosophy. In-house solutions have the significant advantage of being adaptable to changing departmental needs, contributing to efficient and higher quality patient care.

Context: Digital pathology has the potential to dramatically alter the way pathologists work, yet little is known about pathologists' viewing behavior while interpreting digital whole slide images. While tracking pathologist eye movements when viewing digital slides may be the most direct method of capturing pathologists' viewing strategies, this technique is cumbersome and technically challenging to use in remote settings. Tracking pathologist mouse cursor movements may serve as a practical method of studying digital slide interpretation, and mouse cursor data may illuminate pathologists' viewing strategies and time expenditures in their interpretive workflow. Aims: To evaluate the utility of mouse cursor movement data, in addition to eye-tracking data, in studying pathologists' attention and viewing behavior. Settings and Design: Pathologists (N = 7) viewed 10 digital whole slide images of breast tissue that were selected using a random stratified sampling technique to include a range of breast pathology diagnoses (benign/atypia, carcinoma in situ, and invasive breast cancer). A panel of three expert breast pathologists established a consensus diagnosis for each case using a modified Delphi approach. Materials and Methods: Participants' foveal vision was tracked using SensoMotoric Instruments RED 60 Hz eye-tracking system. Mouse cursor movement was tracked using a custom MATLAB script. Statistical Analysis Used: Data on eye-gaze and mouse cursor position were gathered at fixed intervals and analyzed using distance comparisons and regression analyses by slide diagnosis and pathologist expertise. Pathologists' accuracy (defined as percent agreement with the expert consensus diagnoses) and efficiency (accuracy and speed) were also analyzed. Results: Mean viewing time per slide was 75.2 seconds (SD = 38.42). Accuracy (percent agreement with expert consensus) by diagnosis type was: 83% (benign/atypia); 48% (carcinoma in situ); and 93% (invasive). Spatial coupling was close between eye-gaze and mouse cursor positions (highest frequency ∆x was 4.00px (SD = 16.10), and ∆y was 37.50px (SD = 28.08)). Mouse cursor position moderately predicted eye gaze patterns (Rx = 0.33 and Ry = 0.21). Conclusions: Data detailing mouse cursor movements may be a useful addition to future studies of pathologists' accuracy and efficiency when using digital pathology.

Context: In 2013 the high throughput technology known as Tissue Micro Array (TMA) will be fifteen years old. Its elements (design, construction and analysis) are intuitive and the core histopathology technique is unsophisticated, which may be a reason why has eluded a rigorous scientific scrutiny. The source of errors, particularly in specimen identification and how to control for it is unreported. Formal validation of the accuracy of segmenting (also known as de-arraying) hundreds of samples, pairing with the sample data is lacking. Aims: We wanted to address these issues in order to bring the technique to recognized standards of quality in TMA use for research, diagnostics and industrial purposes. Results: We systematically addressed the sources of error and used barcode-driven data input throughout the whole process including matching the design with a TMA virtual image and segmenting that image back to individual cases, together with the associated data. In addition we demonstrate on mathematical grounds that a TMA design, when superimposed onto the corresponding whole slide image, validates on each and every sample the correspondence between the image and patient's data. Conclusions: High throughput use of the TMA technology is a safe and efficient method for research, diagnosis and industrial use if all sources of errors are identified and addressed.

Background: Last year, our pathology informatics fellowship added informatics-based interactive case studies to its existing educational platform of operational and research rotations, clinical conferences, a common core curriculum with an accompanying didactic course, and national meetings. Methods: The structure of the informatics case studies was based on the traditional business school case study format. Three different formats were used, varying in length from short, 15-minute scenarios to more formal multiple hour-long case studies. Case studies were presented over the course of three retreats (Fall 2011, Winter 2012, and Spring 2012) and involved both local and visiting faculty and fellows. Results: Both faculty and fellows found the case studies and the retreats educational, valuable, and enjoyable. From this positive feedback, we plan to incorporate the retreats in future academic years as an educational component of our fellowship program. Conclusions: Interactive case studies appear to be valuable in teaching several aspects of pathology informatics that are difficult to teach in more traditional venues (rotations and didactic class sessions). Case studies have become an important component of our fellowship's educational platform.

The Human Genome Project (HGP) provided the initial draft of mankind's DNA sequence in 2001. The HGP was produced by 23 collaborating laboratories using Sanger sequencing of mapped regions as well as shotgun sequencing techniques in a process that occupied 13 years at a cost of ~$3 billion. Today, Next Generation Sequencing (NGS) techniques represent the next phase in the evolution of DNA sequencing technology at dramatically reduced cost compared to traditional Sanger sequencing. A single laboratory today can sequence the entire human genome in a few days for a few thousand dollars in reagents and staff time. Routine whole exome or even whole genome sequencing of clinical patients is well within the realm of affordability for many academic institutions across the country. This paper reviews current sequencing technology methods and upcoming advancements in sequencing technology as well as challenges associated with data generation, data manipulation and data storage. Implementation of routine NGS data in cancer genomics is discussed along with potential pitfalls in the interpretation of the NGS data. The overarching importance of bioinformatics in the clinical implementation of NGS is emphasized. ^[7] We also review the issue of physician education which also is an important consideration for the successful implementation of NGS in the clinical workplace. NGS technologies represent a golden opportunity for the next generation of pathologists to be at the leading edge of the personalized medicine approaches coming our way. Often under-emphasized issues of data access and control as well as potential ethical implications of whole genome NGS sequencing are also discussed. Despite some challenges, it's hard not to be optimistic about the future of personalized genome sequencing and its potential impact on patient care and the advancement of knowledge of human biology and disease in the near future.

Background: In the recent years, the advances in digital methods in pathology have resulted in the use of telecytology in the immediate assessment of fine needle aspiration (FNA) specimens. However, there is a need for organ-based and body site-specific studies on the use of telecytology for the immediate assessment of FNA to evaluate its pitfalls and limitations. We present our experience with the use of telecytology for on-site evaluation of ultrasound-guided FNA (USG-FNA) of axillary lymph nodes in a remote breast care center. Materials and Methods: Real-time images of Diff-Quik-stained cytology smears were obtained with an Olympus digital camera attached to an Olympus CX41 microscope and transmitted via ethernet by a cytotechnologist to a pathologist who rendered preliminary diagnosis while communicating with the on-site cytotechnologist over the Vocera system. The accuracy of the preliminary diagnosis was compared with the final diagnosis, retrospectively. Results: A total of 39 female patients (mean age: 50.5 years) seen at the breast care center underwent USG-FNA of 44 axillary nodes. Preliminary diagnoses of benign, suspicious/malignant, and unsatisfactory were 41, 52, and 7%, respectively. Only one of the 23 cases that were initially interpreted as benign was reclassified as suspicious on final cytologic diagnosis. Seventeen of 18 suspicious/malignant cases on initial cytology corresponded with a malignant diagnosis on final cytology. One suspicious case was reclassified as benign on final cytologic diagnosis. All unsatisfactory cases remained inadequate for final cytologic interpretation. The presence of additional material in the cell block and interpretative error were the main reasons for discrepancy, accounting for the two discrepant cases. Conclusions: This retrospective study demonstrates that the on-site telecytology evaluation of USG-FNA of axillary lymph nodes in patients at a remote breast care center was highly accurate compared with the final cytologic evaluation. It allows pathologists to use their time more efficiently and makes on-site evaluation at a remote site possible.

Background: For decades anatomic pathology (AP) workflow have been a highly manual process based on the use of an optical microscope and glass slides. Recent innovations in scanning and digitizing of entire glass slides are accelerating a move toward widespread adoption and implementation of a workflow based on digital slides and their supporting information management software. To support the design of digital pathology systems and ensure their adoption into pathology practice, the needs of the main users within the AP workflow, the pathologists, should be identified. Contextual inquiry is a qualitative, user-centered, social method designed to identify and understand users' needs and is utilized for collecting, interpreting, and aggregating in-detail aspects of work. Objective: Contextual inquiry was utilized to document current AP workflow, identify processes that may benefit from the introduction of digital pathology systems, and establish design requirements for digital pathology systems that will meet pathologists' needs. Materials and Methods: Pathologists were observed and interviewed at a large academic medical center according to contextual inquiry guidelines established by Holtzblatt et al. 1998. Notes representing user-provided data were documented during observation sessions. An affinity diagram, a hierarchal organization of the notes based on common themes in the data, was created. Five graphical models were developed to help visualize the data including sequence, flow, artifact, physical, and cultural models. Results: A total of six pathologists were observed by a team of two researchers. A total of 254 affinity notes were documented and organized using a system based on topical hierarchy, including 75 third-level, 24 second-level, and five main-level categories, including technology, communication, synthesis/preparation, organization, and workflow. Current AP workflow was labor intensive and lacked scalability. A large number of processes that may possibly improve following the introduction of digital pathology systems were identified. These work processes included case management, case examination and review, and final case reporting. Furthermore, a digital slide system should integrate with the anatomic pathologic laboratory information system. Conclusions: To our knowledge, this is the first study that utilized the contextual inquiry method to document AP workflow. Findings were used to establish key requirements for the design of digital pathology systems.

Background: One of the drawbacks of studying pathology in the second year of medical school in a classroom setting is the relatively limited exposure to patient encounters/clinical rotations, making it difficult to understand and fully appreciate the significance of the course material, specifically the molecular and tissue aspects of disease. In this study, we determined if case vignettes incorporating pathologist-clinician encounters with whole slide imaging (WSI) and narrated/annotated videos of whole slide (WS) scans in addition to clinical data improved student understanding of pathologic disease processes. Materials and Methods: Case vignettes were created for several genitourinary disease processes that utilized clinical data including narratives of pathologist-clinician encounters, WSI, and annotated video tutorials of WS scans (designed to simulate "double-heading"). The students were encouraged to view the virtual slide first, with the video tutorials being provided to offer additional assistance. The case vignettes were created to be interactive with a detailed explanation of each correct and incorrect question choice. The cases were made available to all second year medical students via a website and could be viewed only after completing a 10 question pre-test. A post-test could be completed after viewing all cases followed by a brief satisfaction survey. Results: Ninety-six students completed the pre-test with an average score of 7.7/10. Fifty-seven students completed the post-test with an average score of 9.4/10. Thirty-six students completed the satisfaction survey. 94% agreed or strongly agreed that this was a useful exercise and 91% felt that it helped them better understand the topics. Conclusion: The development of interactive case vignettes incorporating simulated pathologist-clinician encounters with WSI and video tutorials of WS scans helps to improve student enthusiasm to learn and grasp pathologic aspects of disease processes that lead to clinical therapeutic decision making.

The robustness of image features is a very important consideration in quantitative image analysis. The objective of this paper is to investigate the robustness of a range of image texture features using hematoxylin stained breast tissue microarray slides which are assessed while simulating different imaging challenges including out of focus, changes in magnification and variations in illumination, noise, compression, distortion, and rotation. We employed five texture analysis methods and tested them while introducing all of the challenges listed above. The texture features that were evaluated include co-occurrence matrix, center-symmetric auto-correlation, texture feature coding method, local binary pattern, and texton. Due to the independence of each transformation and texture descriptor, a network structured combination was proposed and deployed on the Rutgers private cloud. The experiments utilized 20 randomly selected tissue microarray cores. All the combinations of the image transformations and deformations are calculated, and the whole feature extraction procedure was completed in 70 minutes using a cloud equipped with 20 nodes. Center-symmetric auto-correlation outperforms all the other four texture descriptors but also requires the longest computational time. It is roughly 10 times slower than local binary pattern and texton. From a speed perspective, both the local binary pattern and texton features provided excellent performance for classification and content-based image retrieval.

Background: The need for informatics and genomics training in pathology is critical, yet limited resources for such training are available. In this study we sought to critically test the hypothesis that the incorporation of a wiki (a collaborative writing and publication tool with roots in "Web 2.0") in a combined informatics and genomics course could both (1) serve as an interactive, collaborative educational resource and reference and (2) actively engage trainees by requiring the creation and sharing of educational materials. Materials and Methods: A 2-week full-time course at our institution covering genomics, research, and pathology informatics (GRIP) was taught by 36 faculty to 18 second- and third-year pathology residents. The course content included didactic lectures and hands-on demonstrations of technology (e.g., whole-slide scanning, telepathology, and statistics software). Attendees were given pre- and posttests. Residents were trained to use wiki technology (MediaWiki) and requested to construct a wiki about the GRIP course by writing comprehensive online review articles on assigned lectures. To gauge effectiveness, pretest and posttest scores for our course were compared with scores from the previous 7 years from the predecessor course (limited to informatics) given at our institution that did not utilize wikis. Results: Residents constructed 59 peer-reviewed collaborative wiki articles. This group showed a 25% improvement (standard deviation 12%) in test scores, which was greater than the 16% delta recorded in the prior 7 years of our predecessor course (P = 0.006). Conclusions: Our use of wiki technology provided a wiki containing high-quality content that will form the basis of future pathology informatics and genomics courses and proved to be an effective teaching tool, as evidenced by the significant rise in our resident posttest scores. Data from this project provide support for the notion that active participation in content creation is an effective mechanism for mastery of content. Future residents taking this course will continue to build on this wiki, keeping content current, and thereby benefit from this collaborative teaching tool.

Background: In 2007, our healthcare system established a clinical fellowship program in Pathology Informatics. In 2010 a core didactic course was implemented to supplement the fellowship research and operational rotations. In 2011, the course was enhanced by a formal, structured core curriculum and reading list. We present and discuss our rationale and development process for the Core Curriculum and the role it plays in our Pathology Informatics Fellowship Training Program. Materials and Methods: The Core Curriculum for Pathology Informatics was developed, and is maintained, through the combined efforts of our Pathology Informatics Fellows and Faculty. The curriculum was created with a three-tiered structure, consisting of divisions, topics, and subtopics. Primary (required) and suggested readings were selected for each subtopic in the curriculum and incorporated into a curated reading list, which is reviewed and maintained on a regular basis. Results: Our Core Curriculum is composed of four major divisions, 22 topics, and 92 subtopics that cover the wide breadth of Pathology Informatics. The four major divisions include: (1) Information Fundamentals, (2) Information Systems, (3) Workflow and Process, and (4) Governance and Management. A detailed, comprehensive reading list for the curriculum is presented in the Appendix to the manuscript and contains 570 total readings (current as of March 2012). Discussion: The adoption of a formal, core curriculum in a Pathology Informatics fellowship has significant impacts on both fellowship training and the general field of Pathology Informatics itself. For a fellowship, a core curriculum defines a basic, common scope of knowledge that the fellowship expects all of its graduates will know, while at the same time enhancing and broadening the traditional fellowship experience of research and operational rotations. For the field of Pathology Informatics itself, a core curriculum defines to the outside world, including departments, companies, and health systems considering hiring a pathology informatician, the core knowledge set expected of a person trained in the field and, more fundamentally, it helps to define the scope of the field within Pathology and healthcare in general.

Background: Pathology Informatics is a new field; a field that is still defining itself even as it begins the formalization, accreditation, and board certification process. At the same time, Pathology itself is changing in a variety of ways that impact informatics, including subspecialization and an increased use of data analysis. In this paper, we examine how these changes impact both the structure of Pathology Informatics fellowship programs and the fellows' goals within those programs. Materials and Methods: As part of our regular program review process, the fellows evaluated the value and effectiveness of our existing fellowship tracks (Research Informatics, Clinical Two-year Focused Informatics, Clinical One-year Focused Informatics, and Clinical 1 + 1 Subspecialty Pathology and Informatics). They compared their education, informatics background, and anticipated career paths and analyzed them for correlations between those parameters and the fellowship track chosen. All current and past fellows of the program were actively involved with the project. Results: Fellows' anticipated career paths correlated very well with the specific tracks in the program. A small set of fellows (Clinical - one or two year - Focused Informatics tracks) anticipated clinical careers primarily focused in informatics (Director of Informatics). The majority of the fellows, however, anticipated a career practicing in a Pathology subspecialty, using their informatics training to enhance that practice (Clinical 1 + 1 Subspecialty Pathology and Informatics Track). Significantly, all fellows on this track reported they would not have considered a Clinical Two-year Focused Informatics track if it was the only track offered. The Research and the Clinical One-year Focused Informatics tracks each displayed unique value for different situations. Conclusions: It seems a "one size fits all" fellowship structure does not fit the needs of the majority of potential Pathology Informatics candidates. Increasingly, these fellowships must be able to accommodate the needs of candidates anticipating a wide range of Pathology Informatics career paths, be able to accommodate Pathology's increasingly subspecialized structure, and do this in a way that respects the multiple fellowships needed to become a subspecialty pathologist and informatician. This is further complicated as Pathology Informatics begins to look outward and takes its place in the growing, and still ill-defined, field of Clinical Informatics, a field that is not confined to just one medical specialty, to one way of practicing medicine, or to one way of providing patient care.

Context and Aims: Rapid, accurate peripheral blood differentials are essential to maintain standards of patient care. CellaVision DM96 (CellaVision AB, Lund, Sweden) (CV) is an automated digital morphology and informatics system used to locate, pre-classify, store and transmit images of platelets, red and white blood cells to a trained technologist who confirms or edits CV cell classification. We assessed our experience with CV by evaluating sensitivity, specificity, positive predictive value and negative predictive value for CV in three different patient populations. Materials and Methods: We analyzed classification accuracy of CV for white blood cells, erythroblasts, platelets and artefacts over six months for three different university hospitals using CV. Results: CV classified 211,218 events for the adult cancer center; 51,699 events for the adult general hospital; and 8,009 events for the children's hospital with accuracy of CV being 93%, 87.3% and 95.4% respectively. Sensitivity and positive predictive value were <80% for immature granulocytes (band neutrophil, promyelocyte, myelocyte and metamyelocytes) (differences usually within one stage of maturation). Cell types comprising a lower frequency of the total events, including blasts, showed lower accuracy at some sites. Conclusions: The reduced immature granulocyte classification accuracy may be due in part to the subjectivity in classification of these cells, length of experience with the system and individual expertise of the technologist. Cells with low sensitivity and positive predictive value comprised a minority of the cells and should not significantly affect the technologist re-classification time. CV serves as a clinically useful instrument in performance of peripheral blood differentials.

Context: Skype is a peer to peer software application that has been historically used for voice and video calls, instant messaging, and file transfer over the Internet. Few studies are available using Skype specifically for telepathology. Aims: Our aim is to show that dynamic nonrobotic teleconsultation is possible and even effective via means of a standard microscope camera capable of live acquisition, Skype, an established broad band internet connection, and experienced pathologists. Settings and Design: Both the consulting "sending" pathologist and consultant "receiving" pathologist are reasonably experienced general surgical pathologists at junior attending level with several years of experience in sign out. Forty-five cases were chosen encompassing a broad range of surgical pathology specimens. The cases were prospectively evaluated with the consultant diagnosis used as a preliminary pathologic impression with the final diagnosis being confirmation. Materials and Methods: Versions of Skype 5.0 and above were used along with established broadband internet connections, usually between academic medical institutions. Results: Forty of forty-five cases (89%) were essentially concordant. In four of forty-five cases (9%), the consulting impression gave a differential, but favored an entity which did not match the final diagnosis. Only one case (2%) did the consulting impression not match the final diagnosis; a discordant opinion. Conclusions: The image quality via Skype screen sharing option is excellent. Essentially no lag time was seen. We have shown in our small pilot study that Skype is an effective cost-efficient means for teleconsultation, particularly in the setting of entity-related differential diagnoses in surgical pathology and when both the consulting and consultant pathologists are reasonably experienced.

Background: Whole slide digital imaging (WSDI) offers an alternative to glass slides for diagnostic interpretation. While prior work has concentrated on the use of whole slide digital imaging for routine diagnostic cases, this study focuses on diagnostic interpretation of digital images for a highly challenging area, upper gastro-intestinal (GI) dysplasia. The aim of this study is to study the accuracy and efficiency of WSDI in the diagnosis of upper GI tract dysplasia. Materials and Methods: Forty-two hematoxylin and eosin (H and E)-stained slides representing negative, indefinite, low grade and high grade dysplasia were selected and scanned at 20x (Aperio XT). Four attending GI pathologists reviewed the WSDI, then glass slides, with at least 3-4 weeks between each media; glass slides were re-reviewed 16-18 months later. Results: Intraobserver variability for three clinically relevant categories (negative, indefinite/low grade, high grade) was wider for WSDI to glass (kappa range 0.36-0.78) than glass to glass (kappa range 0.58-0.75). In comparison to glass slide review, WSDI review required more time and was associated with an unexpected trend toward downgrading dysplasia. Conclusions: Our results suggest: (1) upper GI dysplasia can be diagnosed using WSDI with similar intraobserver reproducibility as for glass slides; however, this is not true for all pathologists; (2) pathologists may have a tendency to downgrade dysplasia in digital images; and (3) pathologists who use WSDI for interpretation of GI dysplasia cases may benefit from regular, on-going, re-review of paired digital and glass images to ensure the most accurate utilization of digital technology, at least in the early stages of implementation.

Background: Graphical reports that contain charts, images, and tables have potential to convey information more effectively than text-based reports; however, studies have not measured how much clinicians value such features. We sought to identify factors that might influence the utilization of reports with graphical elements postulating that this is a surrogate for relative clinical utility of these graphical elements. Materials and Methods: We implemented a pilot project at ARUP laboratories to develop online enhanced laboratory test reports that contained graphical elements. We monitored on-demand clinician access to reports generated for 48 reportable tests over 22 months. We evaluated utilization of reports with graphical elements by clinicians at all institutions that use ARUP as a reference laboratory using descriptive statistics, regression, and meta-analysis tools to evaluate groups of similar test reports. Results: Median download rate by test was 8.6% with high heterogeneity in download rates between tests. Test reports with additional graphical elements were not necessarily downloaded more often than reports without these elements. Recently implemented tests and tests reporting abnormal results were associated with higher download rates (P < 0.01). Higher volume tests were associated with lower download rates (P = 0.03). Conclusions: In select cases graphical information may be clinically useful, particularly for less frequently ordered tests and in on reports of abnormal results. The utilization data presented could be used as a reference point for other laboratories planning on implementing graphical reporting. However, between-test heterogeneity was high and in many cases graphical elements may add little clinical utility, particularly if these merely reinforce information already contained in text based reports.

Background: Image bioinformatics infrastructure typically relies on a combination of server-side high-performance computing and client desktop applications tailored for graphic rendering. On the server side, matrix manipulation environments are often used as the back-end where deployment of specialized analytical workflows takes place. However, neither the server-side nor the client-side desktop solution, by themselves or combined, is conducive to the emergence of open, collaborative, computational ecosystems for image analysis that are both self-sustained and user driven. Materials and Methods: ImageJS was developed as a browser-based webApp, untethered from a server-side backend, by making use of recent advances in the modern web browser such as a very efficient compiler, high-end graphical rendering capabilities, and I/O tailored for code migration. Results : Multiple versioned code hosting services were used to develop distinct ImageJS modules to illustrate its amenability to collaborative deployment without compromise of reproducibility or provenance. The illustrative examples include modules for image segmentation, feature extraction, and filtering. The deployment of image analysis by code migration is in sharp contrast with the more conventional, heavier, and less safe reliance on data transfer. Accordingly, code and data are loaded into the browser by exactly the same script tag loading mechanism, which offers a number of interesting applications that would be hard to attain with more conventional platforms, such as NIH's popular ImageJ application. Conclusions : The modern web browser was found to be advantageous for image bioinformatics in both the research and clinical environments. This conclusion reflects advantages in deployment scalability and analysis reproducibility, as well as the critical ability to deliver advanced computational statistical procedures machines where access to sensitive data is controlled, that is, without local "download and installation."

Background: Conventional tissue microarrays (TMAs) consist of cores of tissue inserted into a recipient paraffin block such that a tissue section on a single glass slide can contain numerous patient samples in a spatially structured pattern. Scanning TMAs into digital slides for subsequent analysis by computer-aided diagnostic (CAD) algorithms all offers the possibility of evaluating candidate algorithms against a near-complete repertoire of variable disease morphologies. This parallel interrogation approach simplifies the evaluation, validation, and comparison of such candidate algorithms. A recently developed digital tool, digital core (dCORE), and image microarray maker (iMAM) enables the capture of uniformly sized and resolution-matched images, with these representing key morphologic features and fields of view, aggregated into a single monolithic digital image file in an array format, which we define as an image microarray (IMA). We further define the TMA-IMA construct as IMA-based images derived from whole slide images of TMAs themselves. Methods: Here we describe the first combined use of the previously described dCORE and iMAM tools, toward the goal of generating a higher-order image construct, with multiple TMA cores from multiple distinct conventional TMAs assembled as a single digital image montage. This image construct served as the basis of the carrying out of a massively parallel image analysis exercise, based on the use of the previously described spatially invariant vector quantization (SIVQ) algorithm. Results: Multicase, multifield TMA-IMAs of follicular lymphoma and follicular hyperplasia were separately rendered, using the aforementioned tools. Each of these two IMAs contained a distinct spectrum of morphologic heterogeneity with respect to both tingible body macrophage (TBM) appearance and apoptotic body morphology. SIVQ-based pattern matching, with ring vectors selected to screen for either tingible body macrophages or apoptotic bodies, was subsequently carried out on the differing TMA-IMAs, with attainment of excellent discriminant classification between the two diagnostic classes. Conclusion: The TMA-IMA construct enables and accelerates high-throughput multicase, multifield based image feature discovery and classification, thus simplifying the development, validation, and comparison of CAD algorithms in settings where the heterogeneity of diagnostic feature morphologic is a significant factor.

Objective: The opportunity to integrate clinical decision support systems into clinical practice is limited due to the lack of structured, machine readable data in the current format of the electronic health record. Natural language processing has been designed to convert free text into machine readable data. The aim of the current study was to ascertain the feasibility of using natural language processing to extract clinical information from >76,000 breast pathology reports. Approach and Procedure: Breast pathology reports from three institutions were analyzed using natural language processing software (Clearforest, Waltham, MA) to extract information on a variety of pathologic diagnoses of interest. Data tables were created from the extracted information according to date of surgery, side of surgery, and medical record number. The variety of ways in which each diagnosis could be represented was recorded, as a means of demonstrating the complexity of machine interpretation of free text. Results: There was widespread variation in how pathologists reported common pathologic diagnoses. We report, for example, 124 ways of saying invasive ductal carcinoma and 95 ways of saying invasive lobular carcinoma. There were >4000 ways of saying invasive ductal carcinoma was not present. Natural language processor sensitivity and specificity were 99.1% and 96.5% when compared to expert human coders. Conclusion: We have demonstrated how a large body of free text medical information such as seen in breast pathology reports, can be converted to a machine readable format using natural language processing, and described the inherent complexities of the task.

Pathology informatics encompasses digital imaging and related applications. Several specialized microscopy techniques have emerged which permit the acquisition of digital images ("optical biopsies") at high resolution. Coupled with fiber-optic and micro-optic components, some of these imaging techniques (e.g., optical coherence tomography) are now integrated with a wide range of imaging devices such as endoscopes, laparoscopes, catheters, and needles that enable imaging inside the body. These advanced imaging modalities have exciting diagnostic potential and introduce new opportunities in pathology. Therefore, it is important that pathology informaticists understand these advanced imaging techniques and the impact they have on pathology. This paper reviews several recently developed microscopic techniques, including diffraction-limited methods (e.g., confocal microscopy, 2-photon microscopy, 4Pi microscopy, and spatially modulated illumination microscopy) and subdiffraction techniques (e.g., photoactivated localization microscopy, stochastic optical reconstruction microscopy, and stimulated emission depletion microscopy). This article serves as a primer for pathology informaticists, highlighting the fundamentals and applications of advanced optical imaging techniques.

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.