Image Segmentation using Local Probabilistic Atlases Coupled with Topological Information
Gaëtan Galisot, Thierry Brouard, Jean-Yves Ramel, Élodie Chaillou · 2017
Atlas-based segmentation is a widely used method for Magnetic Resonance Imaging (MRI) segmentation. Itis also a very efficient method for the automatic segmentation of brain structures. In this paper, we proposea more adaptive and interactive atlas-based method. The proposed model allows to combine several localprobabilistic atlases with a topological graph. Local atlases can provide more precise information about thestructure’s shape and the spatial relationships between each of these atlases are learned and stored inside agraph representation. In this way, local registrations need less computational time and image segmentationcan be guided by the user in an incremental way. Pixel classification is achieved with the help of a hiddenMarkov random field that is able to integrate the a priori information with the intensities coming from differentmodalities. The proposed method was tested on the OASIS dataset, used in the MICCAI’12 challenge formulti-atlas labeling.