G
Giovana S. Cover
Researcher at State University of Campinas
Publications - 6
Citations - 46
Giovana S. Cover is an academic researcher from State University of Campinas. The author has contributed to research in topics: Diffusion MRI & Segmentation. The author has an hindex of 2, co-authored 6 publications receiving 29 citations.
Papers
More filters
Journal ArticleDOI
Computational methods for corpus callosum segmentation on MRI: A systematic literature review
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.
Journal ArticleDOI
Data-Driven Corpus Callosum Parcellation Method Through Diffusion Tensor Imaging
Giovana S. Cover,Mariana Mourão de Azevedo Flores Pereira,Mariana P. Bento,Simone Appenzeller,Leticia Rittner +4 more
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.
Book ChapterDOI
Pixel-Based Classification Method for Corpus Callosum Segmentation on Diffusion-MRI
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.
Book ChapterDOI
Corpus Callosum 2D Segmentation on Diffusion Tensor Imaging Using Growing Neural Gas Network
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.
Proceedings ArticleDOI
Corpus callosum parcellation methods: a quantitative comparative study
Mariana Mourão de Azevedo Flores Pereira,Giovana S. Cover,Simone Appenzeller,Leticia Rittner +3 more
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.