Abstract
The Corpus Callosum (CC) is an important brain structure and its volume and variations in shape are correlated with diseases like Alzheimer, schizophrenia, dyslexia, epilepsy and multiple sclerosis. CC segmentation is a necessary step in both clinical and research studies. CC is commonly studied using structural Magnetic Resonance Imaging (MRI); evaluation and segmentation on Diffusion-MRI is important because there is relevant fiber and tissue information presented on these images, although it is challenging and rarely considered. In this work, a pixel-based classifier on Diffusion-MRI (directly in Diffusion-Weighted Imaging) using a Support Vector Machine is proposed for CC segmentation. A subsampling technique, based on K-means clustering, is used to treat the intrinsically unbalanced pixel classification problem. STAPLE algorithm is used to estimate both a silver-standard and a quantitative analysis through sensitivity, specificity and the Dice coefficient metrics. Our method reached a median value of \(88\%\) in Dice coefficient, had no initialization or parameters to be set and it was compared with two state-of-the-art approaches, showing higher CC detection rate.
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Acknowledgements
The authors would like to thank to Coordination for Improvement of Higher Education Personnel (CAPES - process PVE 88881.062158/2014-01), and the São Paulo Research Foundation (FAPESP - process CEPID2013/07559), for supporting this project. Special thanks go to National Counsel of Technological and Scientific Development (CNPq - process 190557/2014-1), for funding.
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Herrera, W.G., Cover, G.S., Rittner, L. (2018). Pixel-Based Classification Method for Corpus Callosum Segmentation on Diffusion-MRI. In: Tavares, J., Natal Jorge, R. (eds) VipIMAGE 2017. ECCOMAS 2017. Lecture Notes in Computational Vision and Biomechanics, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-68195-5_24
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DOI: https://doi.org/10.1007/978-3-319-68195-5_24
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