A robust Expectation-Maximization algorithm for Multiple Sclerosis lesion segmentation

Daniel García-Lorenzo; Sylvain Prima; Sean Patrick Morrissey; Christian Barillot

A robust Expectation-Maximization algorithm for Multiple Sclerosis lesion segmentation

Daniel García-Lorenzo ^{1, *} Sylvain Prima ¹ Sean Patrick Morrissey ^{1, 2} 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

2 Service de neurologie [Rennes]

Abstract : A fully automatic workflow for Multiple Sclerosis (MS) lesion segmentation is described. Fully automatic means that no user interaction is performed in any of the steps and that all parameters are fixed for all the images processed in beforehand. Our workflow is composed of three steps: an intensity inhomogeneity (IIH) correction, skull-stripping and MS lesions segmentation. A validation comparing our results with two experts is done on MS MRI datasets of 24 MS patients from two different sites.

Document type :

Conference papers

Domain :

Life Sciences [q-bio] / Bioengineering

Complete list of metadatas

Cited literature [17 references] Display Hide Download

https://www.hal.inserm.fr/inserm-00421713
Contributor : Daniel García-Lorenzo <>
Submitted on : Friday, October 2, 2009 - 4:23:19 PM
Last modification on : Monday, March 4, 2019 - 2:07:46 PM
Long-term archiving on : Tuesday, October 16, 2012 - 11:41:27 AM

08-wMiccai-challenge.pdf

Files produced by the author(s)

A robust Expectation-Maximization algorithm for Multiple Sclerosis lesion segmentation

File

Identifiers

Collections

Citation

Export

Metrics

667

419

Contact

A robust Expectation-Maximization algorithm for Multiple Sclerosis lesion segmentation

File

Identifiers

Collections

Citation

Export

Share

Metrics

667

419

Contact