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

TitleManual-Protocol Inspired Technique for Improving Automated MR Image Segmentation during Label Fusion.
Publication TypeJournal Article
Year of Publication2016
AuthorsBhagwat N, Pipitone J, Winterburn JL, Guo T, Duerden EG, Voineskos AN, Lepage M, Miller SP, Pruessner JC, M Chakravarty M
JournalFront Neurosci
Volume10
Pagination325
Date Published2016
ISSN1662-4548
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 Markov random field (L-MRF) model using a novel sequential inference process (walks). We show that AWoL-MRF produces state-of-the-art results with superior accuracy and robustness with a small atlas library compared to existing methods. We validate the proposed approach by performing hippocampal segmentations on three independent datasets: (1) Alzheimer's Disease Neuroimaging Database (ADNI); (2) First Episode Psychosis patient cohort; and (3) A cohort of preterm neonates scanned early in life and at term-equivalent age. We assess the improvement in the performance qualitatively as well as quantitatively by comparing AWoL-MRF with majority vote, STAPLE, and Joint Label Fusion methods. AWoL-MRF reaches a maximum accuracy of 0.881 (dataset 1), 0.897 (dataset 2), and 0.807 (dataset 3) based on Dice similarity coefficient metric, offering significant performance improvements with a smaller atlas library (< 10) over compared methods. We also evaluate the diagnostic utility of AWoL-MRF by analyzing the volume differences per disease category in the ADNI1: Complete Screening dataset. We have made the source code for AWoL-MRF public at: https://github.com/CobraLab/AWoL-MRF.

DOI10.3389/fnins.2016.00325
Alternate JournalFront Neurosci
PubMed ID27486386
PubMed Central IDPMC4949270
Grant ListK01 AG030514 / AG / NIA NIH HHS / United States
R01 MH102324 / MH / NIMH NIH HHS / United States
R01 MH099167 / MH / NIMH NIH HHS / United States
U01 AG024904 / AG / NIA NIH HHS / United States
P30 AG010129 / AG / NIA NIH HHS / United States