ArticleProceedings of SPIE--the International Society for Optical Engineering2016
Autotract: Automatic cleaning and tracking of fibers.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Phylum level differences in the gut bacteria of nursing infants and growth-related decreases in iron reserves are associated with the microstructural organization of cortical white matter tracts in rhesus monkeys.Neurobiology of stress · 2026Article
- Article
- Diffusion Tensor Based White Matter Tract Atlases for Pediatric Populations.Frontiers in neuroscience · 2022Article
- Infantile Iron Deficiency Affects Brain Development in Monkeys Even After Treatment of Anemia.Frontiers in human neuroscience · 2021Article
- Topological principles and developmental algorithms might refine diffusion tractography.Brain structure & function · 2019Review
- TRAFIC: Fiber Tract Classification Using Deep Learning.Proceedings of SPIE--the International Society for Optical Engineering · 2018Article
- FADTTSter: Accelerating Hypothesis Testing With Functional Analysis of Diffusion Tensor Tract Statistics.Proceedings of SPIE--the International Society for Optical Engineering · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
We propose a new tool named Autotract to automate fiber tracking in diffusion tensor imaging (DTI). Autotract uses prior knowledge from a source DTI and a set of corresponding fiber bundles to extract new fibers for a target DTI. Autotract starts by aligning both DTIs and uses the source fibers as seed points to initialize a tractography algorithm. We enforce similarity between the propagated source fibers and automatically traced fibers by computing metrics such as fiber length and fiber distance between the bundles. By analyzing these metrics, individual fiber tracts can be pruned. As a result, we show that both bundles have similar characteristics. Additionally, we compare the automatically traced fibers against bundles previously generated and validated in the target DTI by an expert. This work is motivated by medical applications in which known bundles of fiber tracts in the human brain need to be analyzed for multiple datasets.
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Registered trials
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