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Segmentation of histopathology sections is an ubiquitous requirement indigital pathology and due to the large variability of biological tissue,machine learning techniques have shown superior performance over standard imageprocessing methods. As part of the GlaS@MICCAI2015 colon gland segmentationchallenge, we present a learning-based algorithm to segment glands in tissue ofbenign and malignant colorectal cancer. Images are preprocessed according tothe Hematoxylin-Eosin staining protocol and two deep convolutional neuralnetworks (CNN) are trained as pixel classifiers. The CNN predictions are thenregularized using a figure-ground segmentation based on weighted totalvariation to produce the final segmentation result. On two test sets, ourapproach achieves a tissue classification accuracy of 98% and 94%, making useof the inherent capability of our system to distinguish between benign andmalignant tissue.

Semantic Segmentation of Colon Glands with Deep Convolutional Neural Networks and Total Variation Segmentation