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Title: Shape-Aware Multi-Atlas Segmentation
Full text: PDF
Authors: Alvén, Jennifer and Kahl, Fredrik and Landgren, Matilda and Larsson, Viktor and Ulén, Johannes
Year: 2016
Document Type:Conference Paper
Conference: International Conference on Pattern Recognition
Status: Published
Refereed: Yes
BibTeX item:BibTeX
Abstract: Despite of having no explicit shape model, multiatlas approaches to image segmentation have proved to be a top performer for several diverse datasets and imaging modalities. In this paper, we show how one can directly incorporate shape regularization into the multi-atlas framework. Unlike traditional methods, our proposed approach does not rely on label fusion on the voxel level. Instead, each registered atlas is viewed as an estimate of the position of a shape model. We evaluate and compare our method on two public benchmarks: (i) the VISCERAL Grand Challenge on multi-organ segmentation of whole-body CT images and (ii) the Hammers brain atlas of MR images for segmenting the hippocampus and the amygdala. For this wide spectrum of both easy and hard segmentation tasks, our experimental quantitative results are on par or better than state-of-the-art. More importantly, we obtain qualitatively better segmentation boundaries, for instance, preserving fine structures.

 

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