ArticleScientific reports2021
Automatic segmentation of inner ear on CT-scan using auto-context convolutional neural network.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 39 citations in OpenAlex.
- Synchrotron-Based Deep Learning Network of the Inner Ear: Development and Expert Validation.The Laryngoscope · 2026Article
- Artificial Intelligence-Assisted Inner Ear Computed Tomography Analysis: Radiomics-Based Comparison of Affected and Unaffected Ears in Idiopathic Sudden Sensorineural Hearing Loss.Journal of imaging informatics in medicine · 2026Article
- Demystifying machine learning approaches in digital bone imaging using microCT and HRpQCT.Bone reports · 2026Review
- Image-based deep learning in otology: a state-of-the-art review.Frontiers in neurology · 2026Review
- Training and validation of a deep learning U-net architecture general model for automated segmentation of inner ear from CT.European radiology experimental · 2024Article
- Advances in 3D Inner Ear Reconstruction Software for Cochlear Implants: A Comprehensive Review.Methods and protocols · 2024Review
- Landmark-based registration of a cochlear model to a human cochlea using conventional CT scans.Scientific reports · 2024Article
- Intelligent analysis and measurement of semicircular canal spatial attitude.Frontiers in neurology · 2024Article
- HDS-Net: Achieving fine-grained skin lesion segmentation using hybrid encoding and dynamic sparse attention.PloS one · 2024Article
- Towards fully automated inner ear analysis with deep-learning-based joint segmentation and landmark detection framework.Scientific reports · 2023Article
- Applications of Artificial Intelligence in Temporal Bone Imaging: Advances and Future Challenges.Cureus · 2023Review
- Interobserver variability of cochlear duct measurements in pediatric cochlear implant candidates.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2023Article
- Deep learning-based approach for the automatic segmentation of adult and pediatric temporal bone computed tomography images.Quantitative imaging in medicine and surgery · 2023Article
- A Web-Based Automated Image Processing Research Platform for Cochlear Implantation-Related Studies.Journal of clinical medicine · 2022Article
- IE-Vnet: Deep Learning-Based Segmentation of the Inner Ear's Total Fluid Space.Frontiers in neurology · 2022Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Temporal bone CT-scan is a prerequisite in most surgical procedures concerning the ear such as cochlear implants. The 3D vision of inner ear structures is crucial for diagnostic and surgical preplanning purposes. Since clinical CT-scans are acquired at relatively low resolutions, improved performance can be achieved by registering patient-specific CT images to a high-resolution inner ear model built from accurate 3D segmentations based on micro-CT of human temporal bone specimens. This paper presents a framework based on convolutional neural network for human inner ear segmentation from micro-CT images which can be used to build such a model from an extensive database. The proposed approach employs an auto-context based cascaded 2D U-net architecture with 3D connected component refinement to segment the cochlear scalae, semicircular canals, and the vestibule. The system was formulated on a data set composed of 17 micro-CT from public Hear-EU dataset. A Dice coefficient of 0.90 and Hausdorff distance of 0.74 mm were obtained. The system yielded precise and fast automatic inner-ear segmentations.
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