Learning image based surrogate relevance criterion for atlas selection in segmentation.

Abstract:

:Picking geometrically relevant atlases from the whole training set is crucial to multi-atlas based image segmentation, especially with extensive data of heterogeneous quality in the Big Data era. Unfortunately, there is very limited understanding of how currently used image similarity criteria reveal geometric relevance, let alone the optimization of them. This paper aims to develop a good image based surrogate relevance criterion to best reflect the underlying inaccessible geometric relevance in a learning context. We cast this surrogate learning problem into an optimization framework, by encouraging the image based surrogate to behave consistently with geometric relevance during training. In particular, we desire a criterion to be small for image pairs with similar geometry and large for those with significantly different segmentation geometry. Validation experiments on corpus callosum segmentation demonstrate the improved quality of the learned surrogate compared to benchmark surrogate candidates.

journal_name

Phys Med Biol

authors

Zhao T,Ruan D

doi

10.1088/0031-9155/61/11/4223

subject

Has Abstract

pub_date

2016-06-07 00:00:00

pages

4223-34

issue

11

eissn

0031-9155

issn

1361-6560

journal_volume

61

pub_type

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