Trimmed-likelihood estimation for focal lesions and tissue segmentation in multisequence MRI for multiple sclerosis.

Daniel García-Lorenzo; Sylvain Prima; Douglas Arnold; Louis Collins; Christian Barillot

Trimmed-likelihood estimation for focal lesions and tissue segmentation in multisequence MRI for multiple sclerosis.

Daniel García-Lorenzo ^{1, 2, *} Sylvain Prima ¹ Douglas Arnold ² Louis Collins 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 MNI - McConnell Brain Imaging Centre

Abstract : We present a new automatic method for segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. The method performs tissue classification using a model of intensities of the normal appearing brain tissues. In order to estimate the model, a trimmed likelihood estimator is initialized with a hierarchical random approach in order to be robust to MS lesions and other outliers present in real images. The algorithm is first evaluated with simulated images to assess the importance of the robust estimator in presence of outliers. The method is then validated using clinical data in which MS lesions were delineated manually by several experts. Our method obtains an average Dice similarity coefficient (DSC) of 0.65, which is close to the average DSC obtained by raters (0.66).

keyword : Segmentation Multiple Sclerosis MRI EM Gaussian Mixture Model

Document type :

Journal articles

Domain :

Computer Science [cs] / Medical Imaging

Life Sciences [q-bio] / Bioengineering

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https://www.hal.inserm.fr/inserm-00590724
Contributor : Sylvain Prima <>
Submitted on : Wednesday, May 4, 2011 - 7:01:00 PM
Last modification on : Monday, March 4, 2019 - 2:08:38 PM
Long-term archiving on : Friday, August 5, 2011 - 2:42:40 AM

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Trimmed-likelihood estimation for focal lesions and tissue segmentation in multisequence MRI for multiple sclerosis.

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Trimmed-likelihood estimation for focal lesions and tissue segmentation in multisequence MRI for multiple sclerosis.

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