Brain Extraction From Mr Images Using A Combination Of Segmentation Fusion And Marker-Controlled Watershed Transform

A new brain extraction method for MR images is presented that combines segmentation fusion with an active segmentation step. Three common brain extraction methods ( Brain Extraction Tool (BET), 3DSkullStrip and FreeSurfer) were used to provide the input segmentations. The areas where the input segmentations agreed were fused normally, while the areas of disagreement were left to be handled by an active segmentation in a marker-controlled watershed framework. The performance of the proposed algorithm was compared with the input segmentations as well as with the majority voting and staple meta-algorithms. Three evaluation criteria related to the overlap error, average distance and volume differences were used on two datasets. The results showed that the proposed method outperformed the input segmentations as well as the meta-algorithms on all the evaluation criteria for both datasets. It is concluded that the proposed method appears highly suitable for large studies involving brain extraction, where full automation and robust segmentation results are needed for all datasets.