IMAGeS team: IMages, leArning, Geometry and Statistics

Difference between revisions of "TIBM: Segmentation"

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* Original algorithms have also been developed in the project [http://icube-miv.unistra.fr/fr/index.php/ERC_FBrain ERC FBrain] for segmenting brain tissues from fetal MRI [https://icube-publis.unistra.fr/2-RHS11 2-RHS11], [https://icube-publis.unistra.fr/2-CPHS11 2-CPHS11], [https://icube-publis.unistra.com/2-PNRK11 2-PNRK11].
 
* Original algorithms have also been developed in the project [http://icube-miv.unistra.fr/fr/index.php/ERC_FBrain ERC FBrain] for segmenting brain tissues from fetal MRI [https://icube-publis.unistra.fr/2-RHS11 2-RHS11], [https://icube-publis.unistra.fr/2-CPHS11 2-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  [https://icube-publis.unistra.fr/2-BCA14 2-BCA14] and temporal [https://icube-publis.unistra.fr/4-LCA14 4-LCA14] MRI sequences.
 
* Finally, Markov approaches have been implemented for the automatic identification of multiple sclerosis lesions in multimodal  [https://icube-publis.unistra.fr/2-BCA14 2-BCA14] and temporal [https://icube-publis.unistra.fr/4-LCA14 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 [https://icube-publis.unistra.fr/4-CNRH16 4-CNRH16], [https://icube-publis.unistra.com/4-CRNH15 4-CRNH15].
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* Besides MRI, other contributions involve CT scan images in order to delineate vertebrae [https://icube-publis.unistra.com/4-CRMC15 4-CRMC15] or to estimate the hepatic tumor necrosis rate [https://icube-publis.unistra.fr/4-CNRH16 4-CNRH16], [https://icube-publis.unistra.com/4-CRNH15 4-CRNH15].
 
* A method combining texture descriptors and a supervised classification scheme has also been proposed for segmenting high-resolution histopathological data [https://icube-publis.unistra.fr/4-ANFF14 4-ANFF14].
 
* A method combining texture descriptors and a supervised classification scheme has also been proposed for segmenting high-resolution histopathological data [https://icube-publis.unistra.fr/4-ANFF14 4-ANFF14].
  

Revision as of 11:11, 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 the cortex and the 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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PhD thesis

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