Quantitative assessment of serial brain sections provides an objective measure of neurological events at cellular and molecular levels but is difficult to implement in experimental neuroscience laboratories because of variation from person-to-person and the time required for analysis. Whole slide imaging (WSI) technology, recently introduced for pathological diagnoses, offers an electronic environment and a variety of computational tools for performing high-throughput histological analysis and managing the associated information. In our study, we applied various algorithms to quantify histologic changes associated with brain injury and compared the results to manual assessment. WSI showed a high degree of concordance with manual quantitation by Pearson correlation and strong agreement using Bland-Altman plots in: (i) cortical necrosis in cresyl-violet-stained brain sections of mice after focal cerebral ischemia; (ii) intracerebral hemorrhage in ischemic mouse brains for automated annotation of the small regions, rather than whole hemisphere of the tissue sections; (iii) Iba1-immunoreactive cell density in the adjacent and remote brain regions of mice subject to controlled cortical impact (CCI); and (iv) neuronal degeneration by silver staining after CCI. These results show that WSI, when appropriately applied and carefully validated, is a highly efficient and unbiased tool to locate and identify neuropathological features, delineate affected regions and histologically quantify these events.

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Background:Visual heuristics of pathology diagnosis is a largely unexplored area where reported studies only provided a qualitative insight into the subject. Uncovering and quantifying pathology visual and nonvisual diagnostic patterns have great potential to improve clinical outcomes and avoid diagnostic pitfalls.Methods:Here, we present PathEdEx, an informatics computational framework that incorporates whole-slide digital pathology imaging with multiscale gaze-tracking technology to create web-based interactive pathology educational atlases and to datamine visual and nonvisual diagnostic heuristics.Results:We demonstrate the capabilities of PathEdEx for mining visual and nonvisual diagnostic heuristics using the first PathEdEx volume of a hematopathology atlas. We conducted a quantitative study on the time dynamics of zooming and panning operations utilized by experts and novices to come to the correct diagnosis. We then performed association rule mining to determine sets of diagnostic factors that consistently result in a correct diagnosis, and studied differences in diagnostic strategies across different levels of pathology expertise using Markov chain (MC) modeling and MC Monte Carlo simulations. To perform these studies, we translated raw gaze points to high-explanatory semantic labels that represent pathology diagnostic clues. Therefore, the outcome of these studies is readily transformed into narrative descriptors for direct use in pathology education and practice.Conclusion:PathEdEx framework can be used to capture best practices of pathology visual and nonvisual diagnostic heuristics that can be passed over to the next generation of pathologists and have potential to streamline implementation of precision diagnostics in precision medicine settings.

Visual interpretation of histopathological slides is a complex process that may lead to diagnostic pitfalls. We have started a study to quantify and analyze pathologist's eye gaze patterns while viewing whole slide images (WSI) of specimens on computer monitor in order to explore and uncover diagnostic heuristics. As part of our ongoing research, we are developing a digital pathology integrative platform (DPIP) to provide a training and research resource that includes an online case-based training site, a collection of digitized whole slide images, in-house developed microscopy image analysis software for full-and semi-automated annotation of H&E and IHC images, and eye gaze tracking hardware to collect viewing behavior data. Our online, interactive hematopathology training site has WSI viewing capability, and a realistic case-based diagnosis workflow for training of pathologists, and it also enables collection of data for research in diagnostics. Our training site, named PathEdEx (pathedex.com), consists of 43 deidentified real patient cases from University of Missouri Ellis Fischel Cancer Center, and provides an interactive diagnostic workflow that allows users to navigate and review the available patient data including all H&E and IHC whole slides. Figure 1 shows the components of the platform. Each case contains patient history, all laboratory tests that are available for the patient, and high resolution images of H&E and several IHC stains scanned by a whole slide scanner that produces GB size images. The user is allowed to navigate through the case data to experience a realistic diagnostic workflow, and the navigation behavior as well as final diagnosis of each user is recorded and scored.We collected eye gaze tracking data of pathologists and residents in order to quantify and analyze viewing patterns to relate the differences in their viewing behavior to their levels of experience. Nine users with different levels of experience (medical students, first, second, fourth year residents, and pathologists) have navigated through four cases in order to make a diagnosis. The eye gaze tracking data of the participants were collected by using a Tobii EyeX tracker device while they were interacting with the whole slide images using the integrated WSI viewer by panning and zooming in a virtual microscope manner. Figure 2 shows a sample of eye gaze data superposed on the low magnification whole slide images. Eye gaze and navigational data were collected at a rate of about 30 samples/s and analyzed at different zoom levels in the corresponding whole slides to extract the fixation points and a variety of features to explore the best features related to expertise levels. We have found that three features are significant in terms of representing viewing behavior of participants on a case by case basis.Analyzing gaze tracking data is becoming more available in radiology and pathology [1,2,3,4]. Many studies analyze the captured gaze data with respect to the regions of interest (ROI) marked in the medical images; gaze tim...

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