Interactive surface-guided segmentation of brain MRI data

MRI segmentation is a process of deriving semantic information from volume data. For brain MRI data, segmentation is initially performed at a voxel level and then continued at a brain surface level by generating its approximation. While successful most of the time, automated brain segmentation may leave errors which have to be removed interactively by editing individual 2D slices. We propose an approach for correcting these segmentation errors in 3D modeling space. We actively use the brain surface, which is estimated (potentially wrongly) in the automated FreeSurfer segmentation pipeline. It allows us to work with the whole data set at once, utilizing the context information and correcting several slices simultaneously. Proposed heuristic editing support and automatic visual highlighting of potential error locations allow us to substantially reduce the segmentation time. The paper describes the implementation principles of the proposed software tool and illustrates its application.