Patch-Based Label Fusion Using Local Confidence-Measures and Weak Segmentations

A system for the fully automatic segmentation of the liver and spleen is presented. In a multi-atlas based segmentation framework, several existing segmentations are deformed in parallel to image intensity based registrations targeting the unseen patient. A new locally adaptive label fusion method is presented as the core of this paper. In a patch comparison approach, the transformed segmentations are compared to a weak segmentation of the target organ in the unseen patient. The weak segmentation roughly estimates the hidden truth. Traditional fusion approaches just rely on the deformed expert segmentations only. The result of patch comparison is a confidence weight for a neighboring voxel-label in the atlas label images to contribute to the voxel under study. Fusion is finally carried out in a weighted averaging scheme. The new contribution is the incorporation of locally determined confidence features of the unseen patient into the fusion process. For a small experimental set-up consisting of 12 patients, the proposed method performs favorable to standard classifier label fusion methods. In leave-one-out experiments, we obtain a mean Dice ratio of 0.92 for the liver and 0.82 for the spleen.