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

• 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
• Journal Title: Proceedings of SPIE
• Volume: Volume 9784
• Publisher: Society of Photo-optical Instrumentation Engineers (SPIE) | 2016-03-21
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1117/12.2216396
• Subjects: Health Sciences, Radiology; Health Sciences, Oncology; Physics, Radiation
• Keywords: Prostate segmentation; ultrasound; anatomical feature; machine learning

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.