# Semantic Segmentation of Colon Glands with Deep Convolutional Neural Networks and Total Variation Segmentation Source: [Semantic Segmentation of Colon Glands with Deep Convolutional Neural Networks and Total Variation Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:47:17.894Z Authors: Philipp Kainz, Michael Pfeiffer, Martin Urschler Published: 2015-11-21T20:13:24.000Z ## Abstract Segmentation of histopathology sections is an ubiquitous requirement in digital pathology and due to the large variability of biological tissue, machine learning techniques have shown superior performance over standard image processing methods. As part of the GlaS@MICCAI2015 colon gland segmentation challenge, we present a learning-based algorithm to segment glands in tissue of benign and malignant colorectal cancer. Images are preprocessed according to the Hematoxylin-Eosin staining protocol and two deep convolutional neural networks (CNN) are trained as pixel classifiers. The CNN predictions are then regularized using a figure-ground segmentation based on weighted total variation to produce the final segmentation result. On two test sets, our approach achieves a tissue classification accuracy of 98% and 94%, making use of the inherent capability of our system to distinguish between benign and malignant tissue. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()