TIBM: Segmentation

This research topic gathers all contributions related to the automatic extraction of anatomical or functional structures from biomedical images.

A multi-atlas segmentation framework based on patch fusion has been proposed for parcellating both the cortex and the deep brain structures 2-RHS11.
In the context of the Vivabrain ANR, a method based on component-trees has been set upt to segment the cerebral vascular tree 2-DTNT13.
Specific and original algorithms have also been developed in the project FBrain ERC for segmenting brain tissues from the fetal MRI [https : //icube-publis.unistra.fr/2-RHS11 RHS11-2], [2-https://icube-publis.unistra.fr/2-CPHS11 CPHS11], [https: //icube-publis.unistra .com / 2-PNRK11 2-PNRK11].
Finally, Markov approaches have been implemented for the automatic identification of multiple sclerosis lesions in multimodal 2 and temporal 4-LCA14 MRI sequences.
Besides MRI, other contributions based on super-voxel decomposition involve CT scan images, especially to delineate vertebrae [https: //icube-publis.unistra .com / 4-CRMC15 4-CRMC15] or estimate the hepatic tumor necrosis rate 4-CNRH16, 4-CRNH15.
A method combining texture descriptors and a supervised classification scheme has also been proposed for segmenting high-resolution histopathological data [4-https://icube-publis.unistra.fr/4-ANFF14 4-ANFF14].

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