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  • Open Access English
    Authors: 
    Bhagwat, Nikhil; Pipitone, Jon; Winterburn, Julie L.; Guo, Ting; Duerden, Emma G.; Voineskos, Aristotle N.; Lepage, Martin; Miller, Steven P.; Pruessner, Jens C.; Chakravarty, M. Mallar;
    Publisher: Frontiers Media S.A.
    Project: NSERC , CIHR , NIH | 1/3 - Social Processes In... (5R01MH102324-02), NIH | Alzheimers Disease Neuroi... (1U01AG024904-01), NIH | Effects of Maintenance Tr... (5R01MH099167-04), NIH | "MR Morphometrics and Cog... (5K01AG030514-02), NIH | UC Davis Alzheimer's Core... (3P30AG010129-28S1)

    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.

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Alzheimers Disease Neuroimaging Initiative (1U01AG024904-01)
Include:
The following results are related to Canada. Are you interested to view more results? Visit OpenAIRE - Explore.
1 Research products, page 1 of 1
  • Open Access English
    Authors: 
    Bhagwat, Nikhil; Pipitone, Jon; Winterburn, Julie L.; Guo, Ting; Duerden, Emma G.; Voineskos, Aristotle N.; Lepage, Martin; Miller, Steven P.; Pruessner, Jens C.; Chakravarty, M. Mallar;
    Publisher: Frontiers Media S.A.
    Project: NSERC , CIHR , NIH | 1/3 - Social Processes In... (5R01MH102324-02), NIH | Alzheimers Disease Neuroi... (1U01AG024904-01), NIH | Effects of Maintenance Tr... (5R01MH099167-04), NIH | "MR Morphometrics and Cog... (5K01AG030514-02), NIH | UC Davis Alzheimer's Core... (3P30AG010129-28S1)

    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.