From label fusion to correspondence fusion: A new approach to unbiased groupwise registration

Label fusion strategies are used in multi-atlas image segmentation approaches to compute a consensus segmentation of an image, given a set of candidate segmentations produced by registering the image to a set of atlases [19, 11, 8]. Effective label fusion strategies, such as local similarity-weighted voting [1, 13] substantially reduce segmentation errors compared to single-atlas segmentation. This paper extends the label fusion idea to the problem of finding correspondences across a set of images. Instead of computing a consensus segmentation, weighted voting is used to estimate a consensus coordinate map between a target image and a reference space. Two variants of the problem are considered: (1) where correspondences between a set of atlases are known and are propagated to the target image; (2) where correspondences are estimated across a set of images without prior knowledge. Evaluation in synthetic data shows that correspondences recovered by fusion methods are more accurate tha...

• Type: Journal
• Year: 2012
• Where: CVPR
• Authors: Paul A. Yushkevich, Hongzhi Wang, John Pluta, Brian B. Avants
• Keywords: Computer Vision, Consistent Mappings, Cvpr 2012, Image Segmentation, Target Image