ArticleHuman brain mapping2021
Automated claustrum segmentation in human brain MRI using deep learning.
Article in Human brain mapping, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 24 citations in OpenAlex.
- A hierarchical framework for cortical and subcortical gray-matter parcellation across rodents, primates, and humans.Nature communications · 2026Article
- Multiscale characterization of the human claustrum from histology to MRI.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Artificial intelligence-based segmentation of perisinusoidal tissue along the superior sagittal sinus in human brain magnetic resonance imaging.Neuroradiology · 2026Article
- Comprehensive Segmentation of Deep Grey Nuclei From Structural MRI Data.Human brain mapping · 2025Article
- A Contrast-Agnostic Method for Ultra-High Resolution Claustrum Segmentation.Human brain mapping · 2025Article
- Claustrum Volume Is Reduced in Multiple Sclerosis and Predicts Disability.Annals of clinical and translational neurology · 2025Article
- TopoTxR: A topology-guided deep convolutional network for breast parenchyma learning on DCE-MRIs.Medical image analysis · 2025Article
- The human claustrum tracks slow waves during sleep.Nature communications · 2024Article
- Human Claustrum Connections: Robust In Vivo Detection by DWI-Based Tractography in Two Large Samples.Human brain mapping · 2024Article
- A Unified Pipeline for Simultaneous Brain Tumor Classification and Segmentation Using Fine-Tuned CNN and Residual UNet Architecture.Life (Basel, Switzerland) · 2024Article
- Deep learning segmentation results in precise delineation of the putamen in multiple system atrophy.European radiology · 2023Article
- Automatic brain structure segmentation forQuantitative imaging in medicine and surgery · 2023Article
- Efficient Claustrum Segmentation in T2-weighted Neonatal Brain MRI Using Transfer Learning from Adult Scans.Clinical neuroradiology · 2022Article
- Machine learning approaches for biomolecular, biophysical, and biomaterials research.Biophysics reviews · 2022Review
- Deep learning in neuroimaging data analysis: Applications, challenges, and solutions.Frontiers in neuroimaging · 2022Review
- Automated claustrum segmentation in human brain MRI using deep learning.Human brain mapping · 2021Article
- The claustrum and consciousness: An update.International journal of clinical and health psychology : IJCHPArticle
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the last two decades, neuroscience has produced intriguing evidence for a central role of the claustrum in mammalian forebrain structure and function. However, relatively few in vivo studies of the claustrum exist in humans. A reason for this may be the delicate and sheet-like structure of the claustrum lying between the insular cortex and the putamen, which makes it not amenable to conventional segmentation methods. Recently, Deep Learning (DL) based approaches have been successfully introduced for automated segmentation of complex, subcortical brain structures. In the following, we present a multi-view DL-based approach to segment the claustrum in T1-weighted MRI scans. We trained and evaluated the proposed method in 181 individuals, using bilateral manual claustrum annotations by an expert neuroradiologist as reference standard. Cross-validation experiments yielded median volumetric similarity, robust Hausdorff distance, and Dice score of 93.3%, 1.41 mm, and 71.8%, respectively, representing equal or superior segmentation performance compared to human intra-rater reliability. The leave-one-scanner-out evaluation showed good transferability of the algorithm to images from unseen scanners at slightly inferior performance. Furthermore, we found that DL-based claustrum segmentation benefits from multi-view information and requires a sample size of around 75 MRI scans in the training set. We conclude that the developed algorithm allows for robust automated claustrum segmentation and thus yields considerable potential for facilitating MRI-based research of the human claustrum. The software and models of our method are made publicly available.
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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.