NDLI: Improving multi-atlas segmentation accuracy by leveraging local neighborhood information during label-fusion

Content Provider	IEEE Xplore Digital Library
Author	Bhagwat, N. Pipitone, J. Voineskos, A.N. Pruessner, J. Chakravarty, M.M.
Copyright Year	2015
Description	Author affiliation: Kimel Translational Imaging Genetics Lab., CAMH, Toronto, ON, Canada (Pipitone, J.; Voineskos, A.N.) \|\| Inst. of Biomed. Eng., Univ. of Toronto, Toronto, ON, Canada (Bhagwat, N.; Chakravarty, M.M.) \|\| Cerebral Imaging Centre, Douglas Mental Health Univ. Inst., Verdun, QC, Canada (Pruessner, J.)
Abstract	Multi-atlas segmentation techniques typically comprise generation of multiple candidate labels that are then combined at a final label fusion stage. Label fusion strategies usually leverage information contained in these training labels but ignore local neuroanatomical information. Here, we address this limitation by explicitly incorporating local information at the label fusion stage. The proposed method - Autocorrecting Walks over Localized Markov Random Fields (AWoL-MRF) - is initialized using a set of candidate labels from the atlas library to partition a specific structure into high and low confidence regions. The labels of the low confidence regions are updated based on a localized Markov random field model and a novel sequential inference process (walks), which mimics manual segmentation protocols. The approach combines a priori information from the atlas library with the local spatial constraints improving the accuracy and robustness of the existing segmentation methods.
Starting Page	617
Ending Page	620
File Size	818009
Page Count	4
File Format	PDF
ISBN	9781479923748
DOI	10.1109/ISBI.2015.7163949
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Hippocampus Accuracy Libraries Manuals Computational modeling Markov random fields MRF MR Imaging Segmentation Multi-Atlas Label-Fusion
Content Type	Text
Resource Type	Article

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