Multiple Sclerosis lesion segmentation using an automatic multimodal Graph Cuts: Multiple Sclerosis lesion segmentation using an automatic multimodal Graph Cuts

Daniel García-Lorenzo 1 Jérémy Lecoeur 1 Douglas Arnold 2 D. 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
Abstract : Graph Cuts have been shown as a powerful interactive segmentation technique in several medical domains.We propose to automate the Graph Cuts in order to automatically segment Multiple Sclerosis (MS) lesions in MRI. We replace the manual interaction with a robust EM-based approach in order to discriminate between MS lesions and the Normal Appearing Brain Tissues (NABT). Evaluation is performed in synthetic and real images showing good agreement between the automatic segmentation and the target segmentation. We compare our algorithm with the state of the art techniques and with several manual segmentations. An advantage of our algorithm over previously published ones is the possibility to semi-automatically improve the segmentation due to the Graph Cuts interactive feature.
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Daniel García-Lorenzo, Jérémy Lecoeur, Douglas Arnold, D. Louis Collins, Christian Barillot. Multiple Sclerosis lesion segmentation using an automatic multimodal Graph Cuts: Multiple Sclerosis lesion segmentation using an automatic multimodal Graph Cuts. 12th International Conference on Medical Image Computing and Computer Assisted Intervention, Sep 2009, Londres, United Kingdom. pp.584-591, ⟨10.1007/978-3-642-04271-3⟩. ⟨inria-00423040⟩

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