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Proceedings Paper

Patch-based label fusion for automatic multi-atlas-based prostate segmentation in MR images
Author(s): Xiaofeng Yang; Ashesh B. Jani; Peter J. Rossi; Hui Mao; Walter J. Curran; Tian Liu
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Paper 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.

Paper Details

Date Published: 18 March 2016
PDF: 7 pages
Proc. SPIE 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, 978621 (18 March 2016); doi: 10.1117/12.2216424
Show Author Affiliations
Xiaofeng Yang, Winship Cancer Institute, Emory Univ. (United States)
Ashesh B. Jani, Winship Cancer Institute, Emory Univ. (United States)
Peter J. Rossi, Winship Cancer Institute, Emory Univ. (United States)
Hui Mao, Winship Cancer Institute, Emory Univ. (United States)
Walter J. Curran, Winship Cancer Institute, Emory Univ. (United States)
Tian Liu, Winship Cancer Institute, Emory Univ. (United States)


Published in SPIE Proceedings Vol. 9786:
Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling
Robert J. Webster; Ziv R. Yaniv, Editor(s)

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