ArticleFrontiers in neurology2022
IE-Vnet: Deep Learning-Based Segmentation of the Inner Ear's Total Fluid Space.
Article in Frontiers in neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Deep Learning-Based Inner Ear Subregion Segmentation in 3D T2-Weighted MRI Using Label-Preserving Data Augmentation.NMR in biomedicine · 2026Article
- Image-based deep learning in otology: a state-of-the-art review.Frontiers in neurology · 2026Review
- [Artificial intelligence applications in Ménière's disease].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2025Review
- Imaging endolymphatic space of the inner ear in vestibular migraine.Journal of neurology, neurosurgery, and psychiatry · 2025Article
- Optimizing spatial normalization of multisubject inner ear MRI: comparison of different geometry-preserving co-registration approaches.Scientific reports · 2025Article
- Automatic segmentation of dura for quantitative analysis of lumbar stenosis: A deep learning study with 518 CT myelograms.Journal of applied clinical medical physics · 2024Article
- Deep Learning: A Primer for Neurosurgeons.Advances in experimental medicine and biology · 2024Review
- Towards fully automated inner ear analysis with deep-learning-based joint segmentation and landmark detection framework.Scientific reports · 2023Article
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5 authors.
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Abstract
Background: Methods: The model was based on a V-Net architecture (IE-Vnet) and a multivariate (MR scans: T1, T2, FLAIR, SPACE) training dataset (D1, 179 consecutive patients with peripheral vestibulocochlear syndromes). Ground-truth TFS masks were generated in a semi-manual, atlas-assisted approach. IE-Vnet model segmentation performance, generalizability, and robustness to domain shift were evaluated on four heterogenous test datasets (D2-D5, Results: The IE-Vnet model predicted TFS masks with consistently high congruence to the ground-truth in all test datasets (Dice overlap coefficient: 0.9 ± 0.02, Hausdorff maximum surface distance: 0.93 ± 0.71 mm, mean surface distance: 0.022 ± 0.005 mm) without significant difference concerning side (two-sided Wilcoxon signed-rank test, Conclusion: IE-Vnet TFS segmentation demonstrated high accuracy, robustness toward domain shift, and rapid prediction times. Its output works seamlessly with a previously published open-source pipeline for automatic ELS segmentation. IE-Vnet could serve as a core tool for high-volume trans-institutional studies of the inner ear. Code and pre-trained models are available free and open-source under https://github.com/pydsgz/IEVNet.
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