Scale-adaptive supervoxel-based random forests for liver tumor segmentation in dynamic contrast-enhanced CT scans.
Fiche publication
Date publication
février 2017
Journal
International journal of computer assisted radiology and surgery
Auteurs
Membres identifiés du Cancéropôle Est :
Pr PESSAUX Patrick, Pr HEITZ Fabrice, Dr NOBLET Vincent
Tous les auteurs :
Conze PH, Noblet V, Rousseau F, Heitz F, de Blasi V, Memeo R, Pessaux P
Lien Pubmed
Résumé
Toward an efficient clinical management of hepatocellular carcinoma (HCC), we propose a classification framework dedicated to tumor necrosis rate estimation from dynamic contrast-enhanced CT scans. Based on machine learning, it requires weak interaction efforts to segment healthy, active and necrotic liver tissues.
Mots clés
Algorithms, Carcinoma, Hepatocellular, diagnostic imaging, Contrast Media, Humans, Image Processing, Computer-Assisted, methods, Liver Neoplasms, diagnostic imaging, Tomography, X-Ray Computed, methods
Référence
Int J Comput Assist Radiol Surg. 2017 Feb;12(2):223-233