Adaptive Bayesian label fusion using kernel-based similarity metrics in hippocampus segmentation

David Cárdenas-Peña; Andres Tobar-Rodríguez; Germán Castellanos-Domínguez; Alzheimer’s Disease Neuroimaging Initiative

doi:10.1117/1.JMI.6.1.014003

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4 February 2019 Adaptive Bayesian label fusion using kernel-based similarity metrics in hippocampus segmentation

David Cárdenas-Peña, Andres Tobar-Rodríguez, Germán Castellanos-Domínguez, Alzheimer’s Disease Neuroimaging Initiative

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J. of Medical Imaging, 6(1), 014003 (2019). https://doi.org/10.1117/1.JMI.6.1.014003

Abstract

The effectiveness of brain magnetic resonance imaging (MRI) as a useful evaluation tool strongly depends on the performed segmentation of associated tissues or anatomical structures. We introduce an enhanced brain segmentation approach of Bayesian label fusion that includes the construction of adaptive target-specific probabilistic priors using atlases ranked by kernel-based similarity metrics to deal with the anatomical variability of collected MRI data. In particular, the developed segmentation approach appraises patch-based voxel representation to enhance the voxel embedding in spaces with increased tissue discrimination, as well as the construction of a neighborhood-dependent model that addresses the label assignment of each region with a different patch complexity. To measure the similarity between the target and training atlases, we propose a tensor-based kernel metric that also includes the training labeling set. We evaluate the proposed approach, adaptive Bayesian label fusion using kernel-based similarity metrics, in the specific case of hippocampus segmentation of five benchmark MRI collections, including ADNI dataset, resulting in an increased performance (assessed through the Dice index) as compared to other recent works.

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David Cárdenas-Peña, Andres Tobar-Rodríguez, Germán Castellanos-Domínguez, and Alzheimer’s Disease Neuroimaging Initiative "Adaptive Bayesian label fusion using kernel-based similarity metrics in hippocampus segmentation," Journal of Medical Imaging 6(1), 014003 (4 February 2019). https://doi.org/10.1117/1.JMI.6.1.014003

Received: 25 July 2018; Accepted: 27 December 2018; Published: 4 February 2019

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