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Shape-aware multi-atlas segmentation

Jennifer Alvén (Institutionen för signaler och system, Bildanalys och datorseende) ; Fredrik Kahl (Institutionen för signaler och system, Bildanalys och datorseende) ; M. Landgren ; V. Larsson ; J. Ulén
Proceedings - 23rd International Conference on Pattern Recognition, ICPR 2016, Cancun, Mexico, 4-8 December 2016 (1051-4651). p. 1101-1106. (2017)
[Konferensbidrag, refereegranskat]

Despite of having no explicit shape model, multi-atlas 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.



Denna post skapades 2017-06-01.
CPL Pubid: 249552

 

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Institutioner (Chalmers)

Institutionen för signaler och system, Bildanalys och datorseende (2013-2017)

Ämnesområden

Datorseende och robotik (autonoma system)

Chalmers infrastruktur