FODSeg: a deep learning framework for tract-specific white matter segmentation from full angular distributions

Overall, FODSeg advances white matter tract segmentation by leveraging the full richness of the fODF signal while improving accuracy, specificity, and anatomical consistency.

Authors

Institutions

Publication Details

Journal
Frontiers in Neuroscience
Published
2026-01-12
DOI
https://doi.org/10.3389/fnins.2025.1734498
Citations
1
Primary Topic
Advanced Neuroimaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
31.92

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

FODSeg: a deep learning framework for tract-specific white matter segmentation from full angular distributions

H. Li, Nehal A. Parikh, Ankita Joshi, Lili He
1 citations
Frontiers in Neuroscience
Advanced Neuroimaging Techniques and Applications
31.92
article

FODSeg: a deep learning framework for tract-specific white matter segmentation from full angular distributions

H. Li, Nehal A. Parikh, Ankita Joshi, Lili He
article en
1 citations

Abstract

Overall, FODSeg advances white matter tract segmentation by leveraging the full richness of the fODF signal while improving accuracy, specificity, and anatomical consistency.

Frontiers in NeuroscienceVol. 19
Cincinnati Children's Hospital Medical Center (US), University of Cincinnati (US), University of Cincinnati Medical Center (US)
National Institutes of Health, McDonnell Center for Systems Neuroscience
Openalex Percentile: Top 2%
Advanced Neuroimaging Techniques and Applications
31.92
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.