Manual-Protocol Inspired Technique for Improving Automated MR Image Segmentation during Label Fusion
Manual-Protocol Inspired Technique for Improving Automated MR Image Segmentation during Label Fusion
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.
first-episode-psychosis, MR Imaging, hippocampus, premature birth and neonates, multi-atlas label fusion, segmentation, Alzheimer's disease, Neuroscience
first-episode-psychosis, MR Imaging, hippocampus, premature birth and neonates, multi-atlas label fusion, segmentation, Alzheimer's disease, Neuroscience
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Aisen P. S. Petersen R. C. Donohue M. C. Gamst A. Raman R. Thomas R. G. . (2010). Clinical core of the Alzheimers Disease neuroimaging initiative: progress and plans. Alzheimers Dement. 6, 239–246. 10.1016/j.jalz.2010.03.006 26194312 [OpenAIRE] [PubMed] [DOI]
Akhondi-Asl A. Warfield S. K. (2012). Estimation of the prior distribution of ground truth in the STAPLE algorithm: an empirical bayesian approach. Med. Image Comput. Comput. Assist. Interv. 15, 593–600. 10.1007/978-3-642-33415-3_73 23285600 [OpenAIRE] [PubMed] [DOI]
Aljabar P. Heckemann R. A. Hammers A. Hajnal J. V. Rueckert D. (2009). Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy. Neuroimage 46, 726–738. 10.1016/j.neuroimage.2009.02.018 19245840 [OpenAIRE] [PubMed] [DOI]
Barnes J. Boyes R. G. Lewis E. B. Schott J. M. Frost C. Scahill R. I. . (2007). Automatic calculation of hippocampal atrophy rates using a hippocampal template and the boundary shift integral. Neurobiol. Aging. 28, 1657–1663. 10.1016/j.neurobiolaging.2006.07.008 16934913 [OpenAIRE] [PubMed] [DOI]
Bland J. M. Altman D. G. (1986). Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 327, 307–310. 10.1016/S0140-6736(86)90837-8 2868172 [PubMed] [DOI]
Boccardi M. Bocchetta M. Ganzola R. Robitaille N. Redolfi A. Duchesne S. . (2015). Operationalizing protocol differences for EADC-ADNI manual hippocampal segmentation. Alzheimers Dement. 11, 184–194. 10.1016/j.jalz.2013.03.001 23706515 [OpenAIRE] [PubMed] [DOI]
Chakravarty M. M. Sadikot A. F. Germann J. Bertrand G. Collins D. L. (2008). Towards a validation of atlas warping techniques. Med. Image Anal. 12, 713–726. 10.1016/j.media.2008.04.003 18640867 [OpenAIRE] [PubMed] [DOI]
Chakravarty M. M. Sadikot A. F. Germann J. Hellier P. Bertrand G. Collins D. L. (2009). Comparison of piece-wise linear, linear, and non linear atlas-to-patient warping techniques: analysis of the labeling of subcortical nuclei for functional neurosurgical applications. Hum. Brain Mapp. 30, 3574–3595. 10.1002/hbm.20780 19387981 [OpenAIRE] [PubMed] [DOI]
Chakravarty M. M. Steadman P. van Eede M. C. Calcott R. D. Gu V. Shaw P. . (2013). Performing label fusion-based segmentation using multiple automatically generated templates. Hum. Brain Mapp. 34, 2635–2654. 10.1002/hbm.22092 22611030 [OpenAIRE] [PubMed] [DOI]
Collins D. L. Holmes C. J. Peters T. M. Evans A. C. (1995). Automatic 3-D model-based neuroanatomical segmentation. Hum. Brain Mapp. 3, 190–208. 10.1002/hbm.460030304 [OpenAIRE] [DOI]
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