Multimodal evaluation for medical image segmentation

This paper is a joint effort between five institutions that introduces several novel similarity measures and combines them to carry out a multimodal segmentation evaluation. The new similarity measures proposed are based on the location and the intensity values of the misclassified voxels as well as on the connectivity and the boundaries of the segmented data. We show experimentally that the combination of these measures improves the quality of the evaluation, increasing the significance between different methods both visually and numerically and providing better understanding about their difference. The study shown here has been carried out using four different segmentation methods applied to a MRI simulated dataset of the brain.