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 Bioljournal_title
Physics in medicine and biologyauthors
Zhao T,Ruan Ddoi
10.1088/0031-9155/61/11/4223subject
Has Abstractpub_date
2016-06-07 00:00:00pages
4223-34issue
11eissn
0031-9155issn
1361-6560journal_volume
61pub_type
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