| Full text | |
| Author(s): |
Total Authors: 4
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| Affiliation: | [1] Univ Estadual Campinas, Sch Elect & Comp Engn, Campinas, SP - Brazil
[2] Univ Estadual Campinas, Fac Med Sci, Rheumatol Dept, Campinas, SP - Brazil
Total Affiliations: 2
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| Document type: | Journal article |
| Source: | COMPUTERS & GRAPHICS-UK; v. 99, p. 259-271, OCT 2021. |
| Web of Science Citations: | 0 |
| Abstract | |
The Corpus Callosum (CC) is the largest white matter structure in the human brain. Due to its highly organized fibers, analysis with diffusion tensor imaging (DTI) has been extensively used and has provided new relevant information about the CC. Most CC studies on DTI are concerned with diffusion measures alongside the structure, which requires its segmentation determination of its limits and parcellation division of the structure in different parts, according to the cortical regions with which they are interconnected and their respective functions. Recent access to larger datasets has required the use of fully automated methods for segmentation and parcellation for CC studies on DTI, often available as algorithms, but not as implemented tool. This leads to studies that lack reproducibility and are incomparable due to the differences in the early stages of segmentation and parcellation. To allow researchers to perform CC analysis on DTI with confidence, especially when working with large datasets, we implemented inCCsight (available at https: //github.com/MICLab-Unicamp/inCCsight ), a portable platform for DTI automated segmentation and parcellation for small or large datasets, with an automated step of quality assessment of resulting segmentations and several interactive plots and measurements to allow data visualization and exploration, inciting discoveries even based on previously observed data. (c) 2021 Elsevier Ltd. All rights reserved. (AU) | |
| FAPESP's process: | 13/07559-3 - BRAINN - The Brazilian Institute of Neuroscience and Neurotechnology |
| Grantee: | Fernando Cendes |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |