Background: The use of virtual microscopy (VM) in clinical cytology has been limited due to the inability to focus through three dimensional (3D) cell clusters with a single focal plane (2D images). Limited information exists regarding the optimal scanning parameters for 3D scanning. Aims: The purpose of this study was to determine the optimal number of the focal plane levels and the optimal scanning interval to digitize gynecological (GYN) specimens prepared on SurePath^™ glass slides while maintaining a manageable file size. Subjects and Methods: The iScanCoreo Au scanner (Ventana, AZ, USA) was used to digitize 192 SurePath^™ glass slides at three focal plane levels at 1 μ interval. The digitized virtual images (VI) were annotated using BioImagene's Image Viewer. Five participants interpreted the VI and recorded the focal plane level at which they felt confident and later interpreted the corresponding glass slide specimens using light microscopy (LM). The participants completed a survey about their experiences. Inter-rater agreement and concordance between the VI and the glass slide specimens were evaluated. Results: This study determined an overall high intra-rater diagnostic concordance between glass and VI (89-97%), however, the inter-rater agreement for all cases was higher for LM (94%) compared with VM (82%). Survey results indicate participants found low grade dysplasia and koilocytes easy to diagnose using three focal plane levels, the image enhancement tool was useful and focusing through the cells helped with interpretation; however, the participants found VI with hyperchromatic crowded groups challenging to interpret. Participants reported they prefer using LM over VM. This study supports using three focal plane levels and 1 μ interval to expand the use of VM in GYN cytology. Conclusion: Future improvements in technology and appropriate training should make this format a more preferable and practical option in clinical cytology.

Context: For a precise and objective quantification of liver fibrosis, quantitative evaluations through image analysis have been utilized. However, manual operations are required in most cases for extracting fiber areas because of color variation included in digital pathology images. Aims: The purpose of this research is to propose a color correction method for whole slide images (WSIs) of Elastica van Gieson (EVG) stained liver biopsy tissue specimens and to realize automated operation of image analysis for fibrosis quantification. Materials and Methods: Our experimental dataset consisted of 38 WSIs of liver biopsy specimens collected from 38 chronic viral hepatitis patients from multiple medical facilities, stained with EVG and scanned at ×20 using a Nano Zoomer 2.0 HT (Hamamatsu Photonics K.K., Hamamatsu, Japan). Color correction was performed by modifying the color distribution of a target WSI so as to fit to the reference, where the color distribution was modeled by a set of two triangle pyramids. Using color corrected WSIs; fibrosis quantification was performed based on tissue classification analysis. Statistical Analysis Used: Spearman's rank correlation coefficients were calculated between liver stiffness measured by transient elastography and median area ratio of collagen fibers calculated based on tissue classification results. Results: Statistical analysis results showed a significant correlation r = 0.61-0.68 even when tissue classifiers were trained by using a subset of WSIs, while the correlation coefficients were reduced to r = 0.40-0.50 without color correction. Conclusions: Fibrosis quantification accompanied with the proposed color correction method could provide an objective evaluation tool for liver fibrosis, which complements semi-quantitative histologic evaluation systems.

Background: Oral submucous fibrosis (OSF) is a pre-cancerous condition with features of chronic, inflammatory and progressive sub-epithelial fibrotic disorder of the buccal mucosa. In this study, malignant potentiality of OSF has been assessed by quantification of immunohistochemical expression of epithelial prime regulator-p63 molecule in correlation to its malignant (oral squamous cell carcinoma [OSCC] and normal counterpart [normal oral mucosa [NOM]). Attributes of spatial extent and distribution of p63 ⁺ expression in the epithelium have been investigated. Further, a correlated assessment of histopathological attributes inferred from H&E staining and their mathematical counterparts (molecular pathology of p63) have been proposed. The suggested analytical framework envisaged standardization of the immunohistochemistry evaluation procedure for the molecular marker, using computer-aided image analysis, toward enhancing its prognostic value. Subjects and Methods: In histopathologically confirmed OSF, OSCC and NOM tissue sections, p63 ⁺ nuclei were localized and segmented by identifying regional maxima in plateau-like intensity spatial profiles of nuclei. The clustered nuclei were localized and segmented by identifying concave points in the morphometry and by marker-controlled watersheds. Voronoi tessellations were constructed around nuclei centroids and mean values of spatial-relation metrics such as tessellation area, tessellation perimeter, roundness factor and disorder of the area were extracted. Morphology and extent of expression are characterized by area, diameter, perimeter, compactness, eccentricity and density, fraction of p63 ⁺ expression and expression distance of p63 ⁺ nuclei. Results: Correlative framework between histopathological features characterizing malignant potentiality and their quantitative p63 counterparts was developed. Statistical analyses of mathematical trends were evaluated between different biologically relevant combinations: (i) NOM to oral submucous fibrosis without dysplasia (OSFWT) (ii) NOM to oral submucous fibrosis with dysplasia (OSFWD) (iii) OSFWT-OSFWD (iv) OSFWD-OSCC. Significant histopathogical correlates and their corroborative mathematical features, inferred from p63 staining, were also investigated into. Conclusion: Quantitative assessment and correlative analysis identified mathematical features related to hyperplasia, cellular stratification, differentiation and maturation, shape and size, nuclear crowding and nucleocytoplasmic ratio. It is envisaged that this approach for analyzing the p63 expression and its distribution pattern may help to establish it as a quantitative bio-marker to predict the malignant potentiality and progression. The proposed work would be a value addition to the gold standard by incorporating an observer-independent framework for the associated molecular pathology.

Background: Medical students are expected to master the ability to interpret histopathologic images, a difficult and time-consuming process. A major problem is the issue of transferring information learned from one example of a particular pathology to a new example. Recent advances in cognitive science have identified new approaches to address this problem. Methods: We adapted a new approach for enhancing pattern recognition of basic pathologic processes in skin histopathology images that utilizes perceptual learning techniques, allowing learners to see relevant structure in novel cases along with adaptive learning algorithms that space and sequence different categories (e.g. diagnoses) that appear during a learning session based on each learner's accuracy and response time (RT). We developed a perceptual and adaptive learning module (PALM) that utilized 261 unique images of cell injury, inflammation, neoplasia, or normal histology at low and high magnification. Accuracy and RT were tracked and integrated into a "Score" that reflected students rapid recognition of the pathologies and pre- and post-tests were given to assess the effectiveness. Results: Accuracy, RT and Scores significantly improved from the pre- to post-test with Scores showing much greater improvement than accuracy alone. Delayed post-tests with previously unseen cases, given after 6-7 weeks, showed a decline in accuracy relative to the post-test for 1 ^st -year students, but not significantly so for 2 ^nd -year students. However, the delayed post-test scores maintained a significant and large improvement relative to those of the pre-test for both 1 ^st and 2 ^nd year students suggesting good retention of pattern recognition. Student evaluations were very favorable. Conclusion: A web-based learning module based on the principles of cognitive science showed an evidence for improved recognition of histopathology patterns by medical students.