Work

3D transrectal ultrasound (TRUS) prostate segmentation based on optimal feature learning framework

Year: 2016

Type: conference-paper

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.

Source: Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE

DOI: 10.1117/12.2216396

Authors: Xiaofeng Yang, Peter J. Rossi, Ashesh B. Jani, Hui Mao, Walter J. Curran, Tian Liu

Institution: Emory University

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funder: National Cancer Institute