Introduction: The quality of data obtained from image analysis can be directly affected by several preanalytical (e.g., staining, image acquisition), analytical (e.g., algorithm, region of interest [ROI]), and postanalytical (e.g., computer processing) variables. Whole-slide scanners generate digital images that may vary depending on the type of scanner and device settings. Our goal was to evaluate the impact of altering brightness, contrast, compression, and blurring on image analysis data quality. Methods: Slides from 55 patients with invasive breast carcinoma were digitized to include a spectrum of human epidermal growth factor receptor 2 (HER2) scores analyzed with Visiopharm (30 cases with score 0, 10 with 1+, 5 with 2+, and 10 with 3+). For all images, an ROI was selected and four parameters (brightness, contrast, JPEG2000 compression, out-of-focus blurring) then serially adjusted. HER2 scores were obtained for each altered image. Results: HER2 scores decreased with increased illumination, higher compression ratios, and increased blurring. HER2 scores increased with greater contrast. Cases with HER2 score 0 were least affected by image adjustments. Conclusion: This experiment shows that variations in image brightness, contrast, compression, and blurring can have major influences on image analysis results. Such changes can result in under- or over-scoring with image algorithms. Standardization of image analysis is recommended to minimize the undesirable impact such variations may have on data output.

Context: Image segmentation pipelines often are sensitive to algorithm input parameters. Algorithm parameters optimized for a set of images do not necessarily produce good-quality-segmentation results for other images. Even within an image, some regions may not be well segmented due to a number of factors, including multiple pieces of tissue with distinct characteristics, differences in staining of the tissue, normal versus tumor regions, and tumor heterogeneity. Evaluation of quality of segmentation results is an important step in image analysis. It is very labor intensive to do quality assessment manually with large image datasets because a whole-slide tissue image may have hundreds of thousands of nuclei. Semi-automatic mechanisms are needed to assist researchers and application developers to detect image regions with bad segmentations efficiently. Aims: Our goal is to develop and evaluate a machine-learning-based semi-automated workflow to assess quality of nucleus segmentation results in a large set of whole-slide tissue images. Methods: We propose a quality control methodology, in which machine-learning algorithms are trained with image intensity and texture features to produce a classification model. This model is applied to image patches in a whole-slide tissue image to predict the quality of nucleus segmentation in each patch. The training step of our methodology involves the selection and labeling of regions by a pathologist in a set of images to create the training dataset. The image regions are partitioned into patches. A set of intensity and texture features is computed for each patch. A classifier is trained with the features and the labels assigned by the pathologist. At the end of this process, a classification model is generated. The classification step applies the classification model to unlabeled test images. Each test image is partitioned into patches. The classification model is applied to each patch to predict the patch's label. Results: The proposed methodology has been evaluated by assessing the segmentation quality of a segmentation method applied to images from two cancer types in The Cancer Genome Atlas; WHO Grade II lower grade glioma (LGG) and lung adenocarcinoma (LUAD). The results show that our method performs well in predicting patches with good-quality segmentations and achieves F1 scores 84.7% for LGG and 75.43% for LUAD. Conclusions: As image scanning technologies advance, large volumes of whole-slide tissue images will be available for research and clinical use. Efficient approaches for the assessment of quality and robustness of output from computerized image analysis workflows will become increasingly critical to extracting useful quantitative information from tissue images. Our work demonstrates the feasibility of machine-learning-based semi-automated techniques to assist researchers and algorithm developers in this process.

Imaging is vital for the assessment of physiologic and phenotypic details. In the past, biomedical imaging was heavily reliant on analog, low-throughput methods, which would produce two-dimensional images. However, newer, digital, and high-throughput three-dimensional (3D) imaging methods, which rely on computer vision and computer graphics, are transforming the way biomedical professionals practice. 3D imaging has been useful in diagnostic, prognostic, and therapeutic decision-making for the medical and biomedical professions. Herein, we summarize current imaging methods that enable optimal 3D histopathologic reconstruction: Scanning, 3D scanning, and whole slide imaging. Briefly mentioned are emerging platforms, which combine robotics, sectioning, and imaging in their pursuit to digitize and automate the entire microscopy workflow. Finally, both current and emerging 3D imaging methods are discussed in relation to current and future applications within the context of pathology.

Background: The first satellite center to offer interventional radiology procedures at Memorial Sloan Kettering Cancer Center opened in October 2014. Two of the procedures offered, fine needle aspirations and core biopsies, required a rapid on-site cytologic evaluation of smears and biopsy touch imprints for cellular content and adequacy. The volume and frequency of such evaluations did not justify hiring on-site cytotechnologists, and therefore, a dynamic robotic telecytology (TC) solution was created. In this article, we provide data on our experience with this active implementation. Sakura VisionTek was selected as our robotic TC solution. Methods: A retrospective analysis of all TC evaluations from this satellite site was performed. Information was collected on demographics, lesion location, imaging modality; a comparison of TC-assisted adequacy with final adequacy was also conducted. Results: An analysis of 439 cases was performed over a period of 23 months with perfect correlation in 92.7% (407/439) of the cases. An adequacy upgrade (inadequate specimen becomes adequate) in 6.6% (29/439) of the cases. An adequacy downgrade (adequate specimen becomes inadequate), is near zero at 0.7% (3/439) of the cases. Conclusions: Dynamic robotic TC is effective for immediate evaluations performed without on-site cytotechnology staff. The overall intent of this article is to present data and concordance rates as outcome metrics. Thus far, such outcome metrics have exceeded our expectations. Our TC implementation shows high, perfect concordance. Adequacy upgrades are minor but more relevant and impressive is a near zero adequacy downgrade. Our full implementation has been so successful that plans are in place for configurations at future satellite sites.

Context: Previous studies showed that the agreement among pathologists in recognition of mitoses in breast slides is fairly modest. Aims: Determining the significantly different quantitative features among easily identifiable mitoses, challenging mitoses, and miscounted nonmitoses within breast slides and identifying which color spaces capture the difference among groups better than others. Materials and Methods: The dataset contained 453 mitoses and 265 miscounted objects in breast slides. The mitoses were grouped into three categories based on the confidence degree of three pathologists who annotated them. The mitoses annotated as “probably a mitosis” by the majority of pathologists were considered as the challenging category. The miscounted objects were recognized as a mitosis or probably a mitosis by only one of the pathologists. The mitoses were segmented using k-means clustering, followed by morphological operations. Morphological, intensity-based, and textural features were extracted from the segmented area and also the image patch of 63 × 63 pixels in different channels of eight color spaces. Holistic features describing the mitoses' surrounding cells of each image were also extracted. Statistical Analysis Used: The Kruskal–Wallis H-test followed by the Tukey-Kramer test was used to identify significantly different features. Results: The results indicated that challenging mitoses were smaller and rounder compared to other mitoses. Among different features, the Gabor textural features differed more than others between challenging mitoses and the easily identifiable ones. Sizes of the non-mitoses were similar to easily identifiable mitoses, but nonmitoses were rounder. The intensity-based features from chromatin channels were the most discriminative features between the easily identifiable mitoses and the miscounted objects. Conclusions: Quantitative features can be used to describe the characteristics of challenging mitoses and miscounted nonmitotic objects.

Background: The use of minimally invasive procedures to obtain material for diagnostic purposes has become more prevalent in recent years. As such, there is increased demand for immediate cytologic adequacy assessment of minimally invasive procedures. The array of different locations in which rapid on-site evaluation (ROSE) is expected requires an ever-increasing number of cytology personnel to provide support for adequacy assessment. In our study, we describe the implementation process of a telecytology (TC) system in a high case volume setting and evaluate the performance of this activity. Methods: We performed retrospectively an analysis of all consecutive remote TC ROSE evaluations obtained for 15 months. The specimens were evaluated using a TC system. The ROSE adequacy assessment obtained at the time of the procedure was compared to the final cytopathologist-rendered adequacy assessment when all the material was available for review, including the alcohol-fixed preparations. Results: A total of 8106 distinct cases were analyzed. TC-assisted preliminary adequacy assessment was highly concordant with the final cytopathologist-rendered adequacy assessment. Perfect concordance or accuracy was at 93.1% (7547/8106). The adequacy upgrade rate (inadequate specimen became adequate) was 6.8% (551/8106), and the initial adequacy downgrade (adequate specimen became inadequate) was <0.1% (8/8106). Conclusions: The TC outcome demonstrates high concordance between the initial adequacy assessment and final cytopathologist-rendered adequacy assessment. Adequacy upgrades were minor but, more importantly, our results demonstrate a minimal adequacy downgrade. The process implemented effectively eliminated the need for an attending pathologist to be physically present onsite during a biopsy procedure.

Background: The first satellite center to offer interventional radiology procedures at Memorial Sloan Kettering Cancer Center opened in October 2014. Two of the procedures offered, fine needle aspirations and core biopsies, required rapid on-site cytologic evaluation of smears and biopsy touch imprints for cellular content and adequacy. The volume and frequency of such evaluations did not justify hiring on-site cytotechnologists, and therefore, a dynamic robotic telecytology (TC) solution was created. In this technical article, we present a detailed description of our implementation of robotic TC. Methods: Pathology devised the remote robotic TC solution after acknowledging that it would not be cost effective to staff cytotechnologists on-site at the satellite location. Sakura VisionTek was selected as our robotic TC solution. In addition to configuration of the dynamic robotic TC solution, pathology realized integrating the technology solution into operations would require a multidisciplinary effort and reevaluation of existing staffing and workflows. Results: Extensively described are the architectural framework and multidisciplinary process re-design, created to navigate the constraints of our technical, cultural, and organizational environment. Also reviewed are the benefits and challenges associated with available desktop sharing solutions, particularly accounting for information security concerns. Conclusions: Dynamic robotic TC is effective for immediate evaluations performed without on-site cytotechnology staff. Our goal is providing an extensive perspective of the implementation process, particularly technical, cultural, and operational constraints. Through this perspective, our template can serve as an extensible blueprint for other centers interested in implementing robotic TC without on-site cytotechnologists.

Context: Within digital pathology, digitalization of the grossing procedure has been relatively underexplored in comparison to digitalization of pathology slides. Aims: Our investigation focuses on the interaction design of an augmented reality gross pathology workstation and refining the interface so that information and visualizations are easily recorded and displayed in a thoughtful view. Settings and Design: The work in this project occurred in two phases: the first phase focused on implementation of an augmented reality grossing workstation prototype while the second phase focused on the implementation of an incremental prototype in parallel with a deeper design study. Subjects and Methods: Our research institute focused on an experimental and “designerly” approach to create a digital gross pathology prototype as opposed to focusing on developing a system for immediate clinical deployment. Statistical Analysis Used: Evaluation has not been limited to user tests and interviews, but rather key insights were uncovered through design methods such as “rapid ethnography” and “conversation with materials”. Results: We developed an augmented reality enhanced digital grossing station prototype to assist pathology technicians in capturing data during examination. The prototype uses a magnetically tracked scalpel to annotate planned cuts and dimensions onto photographs taken of the work surface. This article focuses on the use of qualitative design methods to evaluate and refine the prototype. Our aims were to build on the strengths of the prototype's technology, improve the ergonomics of the digital/physical workstation by considering numerous alternative design directions, and to consider the effects of digitalization on personnel and the pathology diagnostics information flow from a wider perspective. A proposed interface design allows the pathology technician to place images in relation to its orientation, annotate directly on the image, and create linked information. Conclusions: The augmented reality magnetically tracked scalpel reduces tool switching though limitations in today's augmented reality technology fall short of creating an ideal immersive workflow by requiring the use of a monitor. While this technology catches up, we recommend focusing efforts on enabling the easy creation of layered, complex reports, linking, and viewing information across systems. Reflecting upon our results, we argue for digitalization to focus not only on how to record increasing amounts of data but also how these data can be accessed in a more thoughtful way that draws upon the expertise and creativity of pathology professionals using the systems.