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      A nuclear circularity-based classifier for diagnostic distinction of desmoplastic from spindle cell melanoma in digitized histological images

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          Abstract

          Context:

          Distinction of spindle cell melanoma (SM) and desmoplastic melanoma (DM) is clinically important due to differences in metastatic rate and prognosis; however, histological distinction is not always straightforward. During a routine review of cases, we noted differences in nuclear circularity between SM and DM.

          Aim:

          The primary aim in our study was to determine whether these differences in nuclear circularity, when assessed using a basic ImageJ-based threshold extraction, can serve as a diagnostic classifier to distinguish DM from SM.

          Settings and Design:

          Our retrospective analysis of an established patient cohort (SM n = 9, DM n = 9) was employed to determine discriminatory power.

          Subjects and Methods:

          Regions of interest (total n = 108; 6 images per case) were selected from scanned H and E-stained histological sections, and nuclear circularity was extracted and quantified by computational image analysis using open source tools (plugins for ImageJ).

          Statistical Analysis:

          Using analysis of variance, t-tests, and Fisher's exact tests, we compared extracted quantitative shape measures; statistical significance was defined as P < 0.05.

          Results:

          Classifying circularity values into four shape categories (spindled, elongated, oval, round) demonstrated significant differences in the spindled and round categories. Paradoxically, DM contained more spindled nuclei than SM ( P = 0.011) and SM contained more round nuclei than DM ( P = 0.026). Performance assessment using a combined shape-classification of the round and spindled fractions showed 88.9% accuracy and a Youden index of 0.77.

          Conclusions:

          Spindle cell melanoma and DM differ significantly in their nuclear morphology with respect to fractions of round and spindled nuclei. Our study demonstrates that quantifying nuclear circularity can be used as an adjunct diagnostic tool for distinction of DM and SM.

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          Most cited references44

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          NIH Image to ImageJ: 25 years of image analysis.

          For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
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            A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution.

            Histopathology diagnosis is based on visual examination of the morphology of histological sections under a microscope. With the increasing popularity of digital slide scanners, decision support systems based on the analysis of digital pathology images are in high demand. However, computerized decision support systems are fraught with problems that stem from color variations in tissue appearance due to variation in tissue preparation, variation in stain reactivity from different manufacturers/batches, user or protocol variation, and the use of scanners from different manufacturers. In this paper, we present a novel approach to stain normalization in histopathology images. The method is based on nonlinear mapping of a source image to a target image using a representation derived from color deconvolution. Color deconvolution is a method to obtain stain concentration values when the stain matrix, describing how the color is affected by the stain concentration, is given. Rather than relying on standard stain matrices, which may be inappropriate for a given image, we propose the use of a color-based classifier that incorporates a novel stain color descriptor to calculate image-specific stain matrix. In order to demonstrate the efficacy of the proposed stain matrix estimation and stain normalization methods, they are applied to the problem of tumor segmentation in breast histopathology images. The experimental results suggest that the paradigm of color normalization, as a preprocessing step, can significantly help histological image analysis algorithms to demonstrate stable performance which is insensitive to imaging conditions in general and scanner variations in particular.
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              Methods for nuclei detection, segmentation, and classification in digital histopathology: a review-current status and future potential.

              Digital pathology represents one of the major evolutions in modern medicine. Pathological examinations constitute the gold standard in many medical protocols, and also play a critical and legal role in the diagnosis process. In the conventional cancer diagnosis, pathologists analyze biopsies to make diagnostic and prognostic assessments, mainly based on the cell morphology and architecture distribution. Recently, computerized methods have been rapidly evolving in the area of digital pathology, with growing applications related to nuclei detection, segmentation, and classification. In cancer research, these approaches have played, and will continue to play a key (often bottleneck) role in minimizing human intervention, consolidating pertinent second opinions, and providing traceable clinical information. Pathological studies have been conducted for numerous cancer detection and grading applications, including brain, breast, cervix, lung, and prostate cancer grading. Our study presents, discusses, and extracts the major trends from an exhaustive overview of various nuclei detection, segmentation, feature computation, and classification techniques used in histopathology imagery, specifically in hematoxylin-eosin and immunohistochemical staining protocols. This study also enables us to measure the challenges that remain, in order to reach robust analysis of whole slide images, essential high content imaging with diagnostic biomarkers and prognosis support in digital pathology.
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                Author and article information

                Contributors
                Journal
                J Pathol Inform
                J Pathol Inform
                JPI
                Journal of Pathology Informatics
                Medknow Publications & Media Pvt Ltd (India )
                2229-5089
                2153-3539
                2014
                21 October 2014
                : 5
                : 40
                Affiliations
                [1 ]Institute of Pathology, University Ulm, Ulm, Germany
                [2 ]Institut für Lasertechnologien in der Medizin und Meβtechnik, University Ulm, Ulm, Germany
                [3 ]Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
                Author notes
                [* ]Corresponding author
                Article
                JPI-5-40
                10.4103/2153-3539.143335
                4221957
                25379346
                6237d992-557e-438f-8354-0193b06aeb11
                Copyright: © 2014 Schöchlin M.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 16 June 2014
                : 06 September 2014
                Categories
                Research Article

                Pathology
                digital pathology,morphometry,numerical histology
                Pathology
                digital pathology, morphometry, numerical histology

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