Giovana S. Cover

TL;DR: A systematic literature review of computational methods focusing on CC segmentation and parcellation acquired on magnetic resonance imaging found that model-based techniques are the most recurrently used for the segmentation task, but machine learning approaches achieved better outcomes when analyzing mean values for segmentations and classification metrics results.

TL;DR: This paper proposed an automatic data-driven CC parcellation method, based on diffusion data extracted from diffusion tensor imaging that uses the Watershed transform, and was the only study that proposed a non-geometric approach for the CC par cellation, based only on the diffusion data of each subject analyzed.

TL;DR: A pixel-based classifier on Diffusion-MRI (directly in Diffusions-Weighted Imaging) using a Support Vector Machine is proposed for CC segmentation, and a subsampling technique, based on K-means clustering, is used to treat the intrinsically unbalanced pixel classification problem.

TL;DR: An automatic CC segmentation approach on Diffusion Tensor imaging (DTI) using Growing Neural Gas (GNG) network, an unsupervised machine learning algorithm, on the fractional anisotropy map is proposed.

TL;DR: This work presents a quantitative analysis of different state of art CC parcellation methods aiming to compare their results on a common dataset and shows a significant difference among the same CC parcels, but using different CC par cellation methods, and its impact on the diffusion properties.