Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation
Abstract
Reliable automated segmentation of the prostate is indispensable for image‐guided prostate interventions. However, the segmentation task is challenging due to inhomogeneous intensity distributions, variation in prostate anatomy, among other problems. Manual segmentation can be time‐consuming and is subject to inter‐ and intraobserver variation. We developed an automated deep learning‐based method to address this technical challenge.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 19, 2019
- Source ID
- 10.1002/mp.13416
Entities
People
- Ashesh B. Jani
- Bo Wang
- Hui Mao
- Pretesh Patel
- Sibo Tian
- Tian Liu
- Tonghe Wang
- Walter J. Curran
- Xiaofeng Yang
- Yang Lei
- Yingzi Liu
Organizations
- Emory University
- National Institutes of Health
- Ningxia University
- United States Department of Defense