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Automatic Evaluation of Brain MR Segmentation Techniques by Level of Agreement

Objective: We are interested in finding a way of comparing and evaluating different segmentation techniques in a robust and automatic way. In this project, we have compared five different brain tissue classifiers applied on 40 different MR images. The evaluation is based on the notion of agreement, i.e. how consistent is a specific technique with all the others. Methods: Our data set consists of 40 pairs of MR volumes acquired on a GE 1.5T scanner. The first volume is a 0.9375x0.9375x1.5mm SPGR coronal scan, the second volume is a 0.9375x0.9375x3mm T2 weighted axial scan. All the classifiers use both volumes for the segmentation of the brain into four tissue classes: Gray Matter, White Matter, CSF and Background.