Manual-Protocol Inspired Technique for Improving Automated MR Image Segmentation during Label Fusion.
|Title||Manual-Protocol Inspired Technique for Improving Automated MR Image Segmentation during Label Fusion.|
|Publication Type||Journal Article|
|Year of Publication||2016|
|Authors||Bhagwat N, Pipitone J, Winterburn JL, Guo T, Duerden EG, Voineskos AN, Lepage M, Miller SP, Pruessner JC, M Chakravarty M|
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.
|Alternate Journal||Front Neurosci|
|PubMed Central ID||PMC4949270|
|Grant List||K01 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