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

arXiv: https://arxiv.org/abs/1511.06919 · HTML: https://mlsanity.com/paper/1511.06919/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2015.

## Built on by (most cited first, 2 of 2)

- [An Object Aware Hybrid U-Net for Breast Tumour Annotation](https://mlsanity.com/paper/2202.10691/) (2022)
- [HistoSegCap: Capsules for Weakly-Supervised Semantic Segmentation of Histological Tissue Type in Whole Slide Images](https://mlsanity.com/paper/2402.10851/) (2024)

## Abstract (arXiv)

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.