Ultrasound prostate segmentation based on multidirectional deeply supervised V‐Net
Abstract
Transrectal ultrasound (TRUS) is a versatile and real‐time imaging modality that is commonly used in image‐guided prostate cancer interventions (e.g., biopsy and brachytherapy). Accurate segmentation of the prostate is key to biopsy needle placement, brachytherapy treatment planning, and motion management. Manual segmentation during these interventions is time‐consuming and subject to inter‐ and intraobserver variation. To address these drawbacks, we aimed to develop a deep learning‐based method which integrates deep supervision into a three‐dimensional (3D) patch‐based V‐Net for prostate segmentation.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 29, 2019
- Source ID
- 10.1002/mp.13577
Entities
People
- Ashesh B. Jani
- Bo Wang
- Hui Mao
- Pretesh Patel
- Sibo Tian
- Tian Liu
- Tonghe Wang
- Walter J. Curran
- Xiaofeng Yang
- Xiuxiu He
- Yang Lei
Organizations
- Emory University
- National Institutes of Health
- United States Department of Defense
- Winship Cancer Institute