Multiple Sclerosis Lesion Segmentation Using an Automatic Multimodal Graph Cuts

Authors:
Daniel García-Lorenzo;Jeremy Lecoeur;Douglas L. Arnold;D. Louis Collins;Christian Barillot
Affiliations:
INRIA, VisAGeS Unit/Project, IRISA, Rennes, France and University of Rennes I, CNRS IRISA, Rennes, France and INSERM, U746 Unit/Project, IRISA, Rennes, France;INRIA, VisAGeS Unit/Project, IRISA, Rennes, France and University of Rennes I, CNRS IRISA, Rennes, France and INSERM, U746 Unit/Project, IRISA, Rennes, France;Montreal Neurological Institute, McGill University, Montreal, Canada;Montreal Neurological Institute, McGill University, Montreal, Canada;INRIA, VisAGeS Unit/Project, IRISA, Rennes, France and University of Rennes I, CNRS IRISA, Rennes, France and INSERM, U746 Unit/Project, IRISA, Rennes, France
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Year:
2009

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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.