Difference between revisions of "TIBM: Segmentation"

Revision as of 15:58, 7 October 2016

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 2-RHS11, 2-CPHS11, 2-PNRK11.
Finally, Markov approaches have been implemented for the automatic identification of multiple sclerosis lesions in multimodal 2-BCA14 and temporal 4-LCA14 MRI sequences.
Besides MRI, other contributions based on super-voxel decomposition involve CT scan images, especially to delineate vertebrae 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-ANFF14.

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 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 [https://icube-publis.unistra.fr/2-RHS11 2-RHS11].