Multiple Sclerosis lesion segmentation using an automated multimodal Graph Cut

Jeremy Beaumont 1 Olivier Commowick 1, * Christian Barillot 1
* Corresponding author
1 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U746, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : In this paper, we present an algorithm for Multiple Sclerosis (MS) lesion segmentation. Our method is fully automated and includes three main steps: 1. the computation of a rough total lesion load in order to optimize the parameter set of the following step; 2. the detection of lesions by graph cut initialized with a robust Expectation-Maximization (EM) algorithm; 3. the application of rules to remove false positives and to adjust the contour of the detected lesions. Our algorithm will be tested on the FLI 2016 MSSEG challenge data.
Keywords : Graph Cut Multiple sclerosis Expectation-Maximization Tissue classification
Document type :
Conference papers
Complete list of metadatas

Cited literature [11 references]  Display  Hide  Download
https://www.hal.inserm.fr/inserm-01417378
Contributor : Nathalie Duchange <>
Submitted on : Thursday, December 15, 2016 - 3:41:46 PM
Last modification on : Monday, March 4, 2019 - 2:07:47 PM
Long-term archiving on : Thursday, March 16, 2017 - 6:02:15 PM

File

Beaumont et al, MSSEG_Challeng...
Files produced by the author(s)

Identifiers

  • HAL Id : inserm-01417378, version 1

Collections

INSTITUT-TELECOM | UNIV-RENNES1 | INRIA | IRISA | UR1-UFR-ISTIC | UNIV-RENNES | IRISA-D5

Citation

Jeremy Beaumont, Olivier Commowick, Christian Barillot. Multiple Sclerosis lesion segmentation using an automated multimodal Graph Cut. Proceedings of the 1st MICCAI Challenge on Multiple Sclerosis Lesions Segmentation Challenge Using a Data Management and Processing Infrastructure – MICCAI-MSSEG, Oct 2016, Athens, Greece. pp.1-8. ⟨inserm-01417378⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

367

Files downloads

372

Contact

CCSD