Patch-Based Label Fusion for Automatic Multi-Atlas-Based Prostate Segmentation in MR Images

• Type of Material: Article
• Authors: Xiaofeng Yang, Emory University; Ashesh B Jani, Emory University; Peter J Rossi, Emory University; Hui Mao, Emory University; Walter J Curran, Emory University; Tian Liu, Emory University
• Language: English
• Date: 2016-03-18
• 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/10.1117/12.2216424
• Title of Journal or Parent Work: Proceedings of SPIE
• ISSN: 0277-786X
• Volume: 9786
• 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: multi-atlas; prostate segmentation; prostate cancer; label fusion; MRI
• Research Categories: Health Sciences, Radiology; Physics, Radiation

Abstract

In this paper, we propose a 3D multi-atlas-based prostate segmentation method for MR images, which utilizes patch-based label fusion strategy. The atlases with the most similar appearance are selected to serve as the best subjects in the label fusion. A local patch-based atlas fusion is performed using voxel weighting based on anatomical signature. This segmentation technique was validated with a clinical study of 13 patients and its accuracy was assessed using the physicians’ manual segmentations (gold standard). Dice volumetric overlapping was used to quantify the difference between the automatic and manual segmentation. In summary, we have developed a new prostate MR segmentation approach based on nonlocal patch-based label fusion, demonstrated its clinical feasibility, and validated its accuracy with manual segmentations.