Angiodysplasia Detection and Localization Using Deep Convolutional Neural Networks

TL;DR

This paper presents a deep learning approach for detecting and localizing angiodysplasia lesions in gastrointestinal images, achieving state-of-the-art results in a challenging medical imaging task.

Contribution

The authors developed novel deep neural network architectures and a classification method for improved angiodysplasia detection and localization, surpassing previous methods.

Findings

Outperforms existing methods in detection accuracy

Achieves state-of-the-art localization results

Provides publicly available source code for reproducibility

Abstract

Accurate detection and localization for angiodysplasia lesions is an important problem in early stage diagnostics of gastrointestinal bleeding and anemia. Gold-standard for angiodysplasia detection and localization is performed using wireless capsule endoscopy. This pill-like device is able to produce thousand of high enough resolution images during one passage through gastrointestinal tract. In this paper we present our winning solution for MICCAI 2017 Endoscopic Vision SubChallenge: Angiodysplasia Detection and Localization its further improvements over the state-of-the-art results using several novel deep neural network architectures. It address the binary segmentation problem, where every pixel in an image is labeled as an angiodysplasia lesions or background. Then, we analyze connected component of each predicted mask. Based on the analysis we developed a classifier that predict angiodysplasia lesions (binary variable) and a detector for their localization (center of a component). In this setting, our approach outperforms other methods in every task subcategory for angiodysplasia detection and localization thereby providing state-of-the-art results for these problems. The source code for our solution is made publicly available at https://github.com/ternaus/angiodysplasia-segmentatio