ArticleFrontiers in computational neuroscience2024
Deep learning for automatic segmentation of vestibular schwannoma: a retrospective study from multi-center routine MRI.
Article in Frontiers in computational neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep Learning Models for Radiomics-Based Segmentation of Vestibular Schwannoma on Magnetic Resonance Imaging: A Systematic Review and Meta-analysis.Journal of imaging informatics in medicine · 2026Pooled it
- Magnetic resonance imaging and the evaluation of vestibular schwannomas: a systematic review.Neuroradiology · 2026Pooled it
- Consensus on the use of artificial intelligence in the management and measurement of vestibular schwannomas: A protocol for a modified delphi consensus.Neuroradiology · 2026Article
- Utility of Artificial Intelligence in Stereotactic Radiosurgery for Vestibular Schwannomas: A Systematic Review.Asian journal of neurosurgery · 2026Review
- Artificial intelligence for personalized management of vestibular schwannoma: a multidisciplinary clinical implementation study.JAMIA open · 2026Article
- All-in-one medical image-to-image translation.Cell reports methods · 2025Article
- Towards clinical implementation of automated segmentation of vestibular schwannomas: a reliability study comparing AI and human performance.Neuroradiology · 2025Article
- The Real-World Impact of Vestibular Schwannoma Fully Automated Volume Measures on the Evaluation of Size Change and Clinical Management Outcomes in a Multidisciplinary Meeting Setting.The journal of international advanced otology · 2025Article
- Comparison of 1D and 3D volume measurement techniques in NF2-associated vestibular schwannoma monitoring.Scientific reports · 2025Article
- Evaluating the effect of voxel size on the accuracy of 3D volumetric analysis measurements of brain tumors.Frontiers in radiology · 2025Article
- Application of Artificial Intelligence in Otology: Past, Present, and Future.Journal of clinical medicine · 2024Article
- Deep learning for automatic segmentation of vestibular schwannoma: a retrospective study from multi-center routine MRI.Frontiers in computational neuroscience · 2024Article
- A WEb-Accessible comprehensiVE platform for automatic vestibular schwannoma segmentation and longitudinal volumetric tracking.Neuro-oncology advancesArticle
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14 authors.
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Abstract
Automatic segmentation of vestibular schwannoma (VS) from routine clinical MRI has potential to improve clinical workflow, facilitate treatment decisions, and assist patient management. Previous work demonstrated reliable automatic segmentation performance on datasets of standardized MRI images acquired for stereotactic surgery planning. However, diagnostic clinical datasets are generally more diverse and pose a larger challenge to automatic segmentation algorithms, especially when post-operative images are included. In this work, we show for the first time that automatic segmentation of VS on routine MRI datasets is also possible with high accuracy. We acquired and publicly release a curated multi-center routine clinical (MC-RC) dataset of 160 patients with a single sporadic VS. For each patient up to three longitudinal MRI exams with contrast-enhanced T1-weighted (ceT1w) (
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