Angiodysplasia Detection and Localization Using Deep Convolutional Neural Networks
- Resource Type
- Conference
- Authors
- Shvets, Alexey A.; Iglovikov, Vladimir I.; Rakhlin, Alexander; Kalinin, Alexandr A.
- Source
- 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) ICMLA Machine Learning and Applications (ICMLA), 2018 17th IEEE International Conference on. :612-617 Dec, 2018
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Engineering Profession
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Lesions
Image segmentation
Endoscopes
Training
Convolution
Hemorrhaging
Wireless communication
medical imaging
computer vision
image segmentation
deep learning
- Language
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 solution for MICCAI 2017 Endoscopic Vision SubChallenge: Angiodysplasia Detection and Localization its further improvements over the state-of-the-art results using several deep neural network architectures. It addresses the binary segmentation problem, where every pixel in an image is labeled as an angiodysplasia lesions or background. Then, we analyze connected components 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 demonstrates one of the top results in every task subcategory for angiodysplasia detection and localization thereby providing state-of-the-art performance for these problems. The source code for our solution is made publicly available at https://github.com/ternaus/angiodysplasia-segmentation.