Scale-adaptive supervoxel-based random forests for liver tumor segmentation in dynamic contrast-enhanced CT scans.

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

https://www.ncbi.nlm.nih.gov/pubmed/27771843

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