Difference between revisions of "TIBM: Segmentation"

Latest revision as of 14:47, 10 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 cortex and deep brain structures 2-RHS11.
In the context of the ANR Vivabrain, a method based on component-trees has been set up to segment the cerebral vascular tree 2-DTNT13.
Original algorithms have also been developed in the project ERC FBrain for segmenting brain tissues from 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 involve CT scan images in order to delineate vertebrae 4-CRMC15 or to 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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 * T. Kensicher, [http://www.theses.fr/s145927 Protocole d'extraction de caractéristiques dans une base de données de scanners angiographiques pour la prédiction de complications post-opérative après des opérations de type EVAR], en cours