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whereRU: Aresty Poster 108- EMaGACOR - Automated Lymphocyte Segmentation on BC histopathology images by Aresty Posters 2009

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Taken by
Aresty Posters 2009 Aresty Posters 2009
Explore score
0.06 Gigapixels
Date added
May 19, 2009
Date taken
May 18, 2009

TITLE- Expectation Maximization driven Geodesic Active Contour with Overlap Resolution (EMaGACOR): Lymphocyte Segmentation on Breast Cancer Histopathology

Contact: hussainf@eden.rutgers.edu Will open in a new tab or window, anantm@rci.rutgers.edu

Laboratory for Computational Imaging and Bioinformatics (LCIB)- lcib.rutgers.edu

The presence of lymphocytic infiltration (LI) has been correlated with nodal metastasis and tumor recurrence in HER2+
breast cancer (BC). The ability to automatically detect and quantify extent of LI on histopathology imagery could potentially result in the development of an image based prognostic tool for HER2+ BC patients. Lymphocyte segmentation in H & E-stained BC histopathology images is complicated by the similarity in appearance between lymphocyte nuclei and other structures (eg. cancer nuclei) in the image. Additional challenges include biological variability, histological artifacts, and high prevalence of overlapping objects. Although active contours are widely employed in image segmentation, they are limited in their ability to segment overlapping objects and are sensitive to initialization. In this paper, we present a new segmentation scheme, Expectation Maximization driven Geodesic Active Contour with Overlap Resolution (EMaGACOR) which we apply to automatically detecting and segmenting lymphocytes on HER2+ BC histopathology images. EMaGACOR utilizes the Expectation Maximization (EM) algorithm for automatically initializing a geodesic active contour (GAC) and includes a novel scheme based on heuristic splitting of contours between high concavities for resolving overlapping structures. EMaGACOR was evaluated on a total of 100 HER2+ breast biopsy histology images and was found to have a detection sensitivity of over 86% and a positive predictive value of over 64%. By comparison, the EMaGAC model (without overlap resolution) and GAC model yielded corresponding detection sensitivities of 42% and 19%, respectively. Furthermore, EMaGACOR was able to correctly resolve over 90% of overlaps between intersecting structures. Hausdorff distance (HD) and mean absolute distance (MAD) for EMaGACOR were found to be 2.1 and 0.9 pixels respectively and significantly better compared to the corresponding performance of the EMaGAC and GAC models. EMaGACOR was found to be efficient, robust, reproducible, accurate, and could be potentially applied to other biomedical image segmentation problems.

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