期刊
COMPUTERS & GRAPHICS-UK
卷 99, 期 -, 页码 259-271出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cag.2021.07.012
关键词
Diffusion tensor imaging; Magnetic resonance imaging; Corpus Callosum; Segmentation; Parcellation
资金
- National Council for Scientific and Technological Development (CNPq) [313598/2020-7]
- Sao Paulo Research Foundation (FAPESP) [2013/07559-3]
The Corpus Callosum (CC) is the largest white matter structure in the human brain, and analysis with diffusion tensor imaging (DTI) has provided new information about it. Most studies on CC with DTI focus on diffusion measures and structure segmentation, especially due to recent access to larger datasets requiring fully automated methods for segmentation and division.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据