Automatic Segmentation of Corpus Callosum in Midsagittal Based on Bayesian Inference Consisting of Sparse Representation Error and Multi-Atlas Voting

• Published in: Frontiers in neuroscience Vol. 12; p. 629
• Main Authors: Park, Gilsoon, Kwak, Kichang, Seo, Sang Won, Lee, Jong-Min
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 11.09.2018; Frontiers Media S.A
• Subjects: Alzheimer's disease; Autism; Automation; Bayesian analysis; Bayesian inference; Corpus callosum; Dictionaries; Epilepsy; Image processing; Medical imaging; multi-atlas voting; Neuroscience; NMR; Nuclear magnetic resonance; Registration; Segmentation; sparse representation; Canada; Montreal Quebec Canada; South Korea; segmentation; corpus callosum; Bayesian inference; multi-atlas voting; sparse representation
• ISSN: 1662-4548; 1662-453X; 1662-453X
• DOI: 10.3389/fnins.2018.00629

In this paper, we introduce a novel automatic method for Corpus Callosum (CC) in midsagittal plane segmentation. The robust segmentation of CC in midsagittal plane is key role for quantitative study of structural features of CC associated with various neurological disorder such as epilepsy, autism, Alzheimer's disease, and so on. Our approach is based on Bayesian inference using sparse representation and multi-atlas voting which both methods are used in various medical imaging, and show outstanding performance. Prior information in the proposed Bayesian inference is obtained from probability map generated from multi-atlas voting. The probability map contains the information of shape and location of CC of target image. Likelihood in the proposed Bayesian inference is obtained from gamma distribution function, generated from reconstruction errors (or sparse representation error), which are calculated in sparse representation of target patch using foreground dictionary and background dictionary each. Unlike the usual sparse representation method, we added gradient magnitude and gradient direction information to the patches of dictionaries and target, which had better segmentation performance than when not added. We compared three main segmentation results as follow: (1) the joint label fusion (JLF) method which is state-of-art method in multi-atlas voting based segmentation for evaluation of our method; (2) prior information estimated from multi-atlas voting only; (3) likelihood estimated from comparison of the reconstruction errors from sparse representation error only; (4) the proposed Bayesian inference. The methods were evaluated using two data sets of T1-weighted images, which one data set consists of 100 normal young subjects and the other data set consist of 25 normal old subjects and 22 old subjects with heavy drinker. In both data sets, the proposed Bayesian inference method has significantly the best segmentation performance than using each method separately.