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

The presence of lymphocytic infiltration (LI) has been correlated with nodal metastasis and tumor recurrence in HER2+breast cancer (BC), making it important to study LI. The ability to detect and quantify extent of LI could serve as an image based prognostic tool for HER2+ BC patients. Lymphocyte segmentation in H & E-stained BC histopathology images is, however, complicated due to the similarity in appearance between lymphocyte nuclei and cancer nuclei. Additional challenges include biological variability, histological artifacts, and high prevalence of overlapping objects. Although active contours are widely employed in segmentation, they are limited in their ability to segment overlapping objects. In this paper, we propose a segmentation scheme (EMaGACOR) that integrates Expectation Maximization (EM) based segmentation with a geodesic active contour (GAC). Additionally, a novel heuristic edge-path algorithm exploits the size of lymphocytes to split contours that enclose overlapping objects. For a total of 62 HER2+ breast biopsy images, EMaGACOR was found to have a detection sensitivity of over 90% and a positive predictive value of over 78%. By comparison, EMaGAC (model without overlap resolution) and GAC (Randomly initialized geodesic active contour) model yielded corresponding sensitivities of 57.4% and 26.7%, respectively. Furthermore, EMaGACOR was able to resolve over 92% of overlaps. Our scheme was found to be robust, reproducible, accurate, and could potentially be applied to other biomedical image segmentation applications.