Conference proceeding
Weakly Supervised Learning of Recurrent Residual ConvNets for Pancreas Segmentation in CT Scans
2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp 1409-1415
Nov 2019
Abstract
Deep neural networks trained by medical images with dense annotations have revealed favourable performance on accurate organ segmentation. The current supervised methods demand voxel-level annotations which are not easily accessible due to the consuming of time and requirements of specialized knowledge and skills. In this paper, we propose a weakly supervised method based on a recurrent residual convolutional neural network trained only with image-level labels to generate voxel-level segmentation. The recurrent residual convolutional units take advantage of contextual information of successive slices and a spatial pooling layer is introduced after the last convolutional layer to aggregate local features and learn accurate localization. The final segmentation mask is computed by applying a conditional random field for spatial prediction. Our method shows competitive performance to fully supervised methods on the public NIH-CT-82 dataset for pancreas segmentation.
Metrics
9 Record Views
3 citations in Web of Science
6 citations in Scopus
Details
- Title
- Weakly Supervised Learning of Recurrent Residual ConvNets for Pancreas Segmentation in CT Scans
- Creators
- Huiru Zeng - Institute of Electronic and Information Engineering in Guangdong, University of Electronic Science and Technology of China,Dongguan,ChinaXiaohua Hu - Drexel University, Information ScienceLeiting Chen - Institute of Electronic and Information Engineering in Guangdong, University of Electronic Science and Technology of China,Dongguan,ChinaChuan Zhou - Institute of Electronic and Information Engineering in Guangdong, University of Electronic Science and Technology of China,Dongguan,ChinaYang Wen - Institute of Electronic and Information Engineering in Guangdong, University of Electronic Science and Technology of China,Dongguan,China
- Publication Details
- 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp 1409-1415
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Scopus ID
- 2-s2.0-85084331804
- Other Identifier
- 991019170145004721