Combining atlas based segmentation and intensity classification with nearest neighbor transform and accuracy weighted vote

Abstract

In this paper, different methods to improve atlas based segmentation are presented. The first technique is a new mapping of the labels of an atlas consistent with a given intensity classification segmentation. This new mapping combines the two segmentations using the nearest neighbor transform and is especially effective for complex and folded regions like the cortex where the registration is difficult. Then, in a multi atlas context, an original weighting is introduced to combine the segmentation of several atlases using a voting procedure. This weighting is derived from statistical classification theory and is computed offline using the atlases as a training dataset. Concretely, the accuracy map of each atlas is computed and the vote is weighted by the accuracy of the atlases. Numerical experiments have been performed on publicly available in vivo datasets and show that, when used together, the two techniques provide an important improvement of the segmentation accuracy.