ArticleRadiology. Artificial intelligence2025
Automated Deep Learning-based Segmentation of the Dentate Nucleus Using Quantitative Susceptibility Mapping MRI.
Article in Radiology. Artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04349514 (A Natural History Study to TRACK Brain and Spinal Cord Changes in Individuals with Friedreich Ataxia), which is not on this map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Natural History Study to TRACK Brain and Spinal Cord Changes in Individuals with Friedreich Ataxia (TRACK-FA)
Who cites it
3 citing papers in PubMed.
- Advancing Cerebro-cerebellar Network Imaging with Ultra-high Field (7T) MRI: Progress and Future Challenges.Cerebellum (London, England) · 2026Review
- APEX-NET: automated pancreatic evaluation network using early non-contrast CT.European radiology · 2026Article
- White matter changes in reward circuits of amyotrophic lateral sclerosis: a fixel-based study of appetite loss.BMC medicine · 2026Article
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Authors and funding
38 authors.
Funding
Abstract
Purpose To develop a dentate nucleus (DN) segmentation tool using deep learning applied to brain MRI-based quantitative susceptibility mapping (QSM) images. Materials and Methods Brain QSM images from healthy controls and individuals with cerebellar ataxia or multiple sclerosis were collected from nine different datasets (2016-2023) worldwide for this retrospective study (ClinicalTrials.gov identifier: NCT04349514). Manual delineation of the DN was performed by experienced raters. Automated segmentation performance was evaluated against manual reference segmentations following training with several deep learning architectures. A two-step approach was used, consisting of a localization model followed by DN segmentation. Performance metrics included intraclass correlation coefficient (ICC), Dice score, and Pearson correlation coefficient. Results The training and testing datasets comprised 328 individuals (age range, 11-64 years; 171 female individuals), including 141 healthy individuals and 187 with cerebellar ataxia or multiple sclerosis. The manual tracing protocol produced reference standards with high intrarater (average ICC, 0.91) and interrater reliability (average ICC, 0.78). Initial deep learning architecture exploration indicated that the nnU-Net framework performed best. The two-step localization plus segmentation pipeline achieved a Dice score of 0.90 ± 0.03 (SD) and 0.89 ± 0.04 for left and right DN segmentation, respectively. In external testing, the proposed algorithm outperformed the current leading automated tool (mean Dice scores for left and right DN, 0.86 ± 0.04 vs 0.57 ± 0.22 [
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.