Computational methods for corpus callosum segmentation on MRI: A systematic literature review. (February 2018) - Cover, G.S.; Herrera, W.G.; Bento, M.P.; Appenzeller, S.; Rittner, L.
Highlights: Because no reviews or surveys covering CC segmentation and parcellation so far, this work presents a systematic literature review about that theme. From 802 publications reviewed, 36 studies were selected through the systematic literature review process. This systematic review led to arrange main studies, focusing on CC segmentation, in four main groups: model-based, region-based, thresholding, and machine learning techniques. Besides, 32 metrics for method validation were summarized and reported. The analyzed computational methods used to perform CC segmentation on magnetic resonance imaging have not yet overcome all presented challenges owing to metrics variability and lack of traceable materials. Abstract: Background and objective: The corpus callosum (CC) is the largest white matter structure in the brain and has a significant role in central nervous system diseases. Its volume correlates with the severity and/or extent of neurodegenerative disease. Even though the CC's role has been extensively studied over the last decades, and different algorithms and methods have been published regarding CC segmentation and parcellation, no reviews or surveys covering such developments have been reported so far. To bridge this gap, this paper presents a systematic literature review of computational methods focusing on CC segmentation and parcellation acquired on magnetic resonance imaging. Methods: IEEExplore, PubMed, EBSCO Host, and Scopus database were searched with the following search terms: ((Segmentation OR Parcellation) AND (Corpus Callosum) AND (DTI OR MRI OR Diffusion Tensor Imag* OR Diffusion Tractography OR Magnetic Resonance Imag*)), resulting in 802 publications. Two reviewers independently evaluated all articles and 36 studies were selected through the systematic literature review process. Results: This work reviewed four main segmentation methods groups: model-based, region-based, thresholding, and machine learning; 32 different validity metrics were reported. Even though model-based techniques are the most recurrently used for the segmentation task (13 articles), machine learning approaches achieved better outcomes of 95% when analyzing mean values for segmentation and classification metrics results. Moreover, CC segmentation is better established in T 1 -weighted images, having more methods implemented and also being tested in larger datasets, compared with diffusion tensor images. Conclusions: The analyzed computational methods used to perform CC segmentation on magnetic resonance imaging have not yet overcome all presented challenges owing to metrics variability and lack of traceable materials.