Segmentation and Landmark Localization Based on Multiple Atlases

In this work, we present multi-atlas based techniques for both segmentation and landmark detection. We focus on modality and anatomy independent techniques to be applied to a wide range of input images, in contrast to methods customized to a specific anatomy or image modality. For segmentation, we use label propagation from several atlases to a target image via a Markov random field (MRF) based registration method, followed by label fusion by majority voting weighted by local cross-correlations. For landmark localization, we use a consensus based fusion of location estimates from several atlases identified by a template-matching approach. Results in IEEE ISBI 2014 VISCERAL challenge as well as VISCERAL Anatomy1 challenge are presented herein.