Manual-Protocol Inspired Technique for Improving Automated MR Image Segmentation during Label Fusion.

ABSTRACT: Recent advances in multi-atlas based algorithms address many of the previous limitations in model-based and probabilistic segmentation methods. However, at the label fusion stage, a majority of algorithms focus primarily on optimizing weight-maps associated with the atlas library based on a theoretical objective function that approximates the segmentation error. In contrast, we propose a novel method-Autocorrecting Walks over Localized Markov Random Fields (AWoL-MRF)-that aims at mimicking the sequential process of manual segmentation, which is the gold-standard for virtually all the segmentation methods. AWoL-MRF begins with a set of candidate labels generated by a multi-atlas segmentation pipeline as an initial label distribution and refines low confidence regions based on a localized Ma

SUBMITTER: Bhagwat N 

PROVIDER: S-EPMC4949270 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature