3D Transrectal Ultrasound (TRUS) Prostate Segmentation Based on Optimal Feature Learning Framework

• Type of Material: Article
• Authors: Xiaofeng Yang, Emory University; Peter J Rossi, Emory University; Ashesh B Jani, Emory University; Hui Mao, Emory University; Walter J Curran, Emory University; Tian Liu, Emory University
• Language: English
• Date: 2016-03-21
• Publisher: Society of Photo-optical Instrumentation Engineers (SPIE)
• Publication Version: Final Published Version
• Copyright Statement: © (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
• Final Published Version (URL): http://dx.doi.org/10.1117/12.2216396
• Title of Journal or Parent Work: Proceedings of SPIE
• ISSN: 0277-786X
• Volume: 9784
• Grant/Funding Information: This research is supported in part by the Department of Defense (DoD) Prostate Cancer Research Program (PCRP) Award W81XWH-13-1-0269; and Winship Cancer Institute.
• Keywords: anatomical feature; machine learning; ultrasound; Prostate segmentation
• Research Categories: Health Sciences, Oncology; Physics, Radiation; Health Sciences, Radiology

Abstract

We propose a 3D prostate segmentation method for transrectal ultrasound (TRUS) images, which is based on patch-based feature learning framework. Patient-specific anatomical features are extracted from aligned training images and adopted as signatures for each voxel. The most robust and informative features are identified by the feature selection process to train the kernel support vector machine (KSVM). The well-trained SVM was used to localize the prostate of the new patient. Our segmentation technique was validated with a clinical study of 10 patients. The accuracy of our approach was assessed using the manual segmentations (gold standard). The mean volume Dice overlap coefficient was 89.7%. In this study, we have developed a new prostate segmentation approach based on the optimal feature learning framework, demonstrated its clinical feasibility, and validated its accuracy with manual segmentations.