Multi-Atlas Segmentation with Joint Label Fusion

Wang, Hongzhi; Suh, Jung W.; Das, Sandhitsu R.; Pluta, John; Craige, Caryne P.; Yushkevich, Paul A.

doi:10.1109/tpami.2012.143

articleIEEE Transactions on Pattern Analysis and Machine IntelligenceJun 27, 2012Closed access

Multi-Atlas Segmentation with Joint Label Fusion

HWHongzhi Wang JWJung W. Suh SRSandhitsu R. Das JPJohn Pluta CPCaryne P. Craige

University of Pennsylvania · HeartFlow (United States) · +1 more institution

PubMed

Indexed incrossrefpubmed

Abstract

Multi-atlas segmentation is an effective approach for automatically labeling objects of interest in biomedical images. In this approach, multiple expert-segmented example images, called atlases, are registered to a target image, and deformed atlas segmentations are combined using label fusion. Among the proposed label fusion strategies, weighted voting with spatially varying weight distributions derived from atlas-target intensity similarity have been particularly successful. However, one limitation of these strategies is that the weights are computed independently for each atlas, without taking into account the fact that different atlases may produce similar label errors. To address this limitation, we…

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Topics & keywords

Topics

Keywords

Segmentation
Artificial intelligence
Atlas (anatomy)
Computer science
Pattern recognition (psychology)
Voxel
Image segmentation
Scale-space segmentation

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