ArticleScientific reports2021
Fully automated preoperative segmentation of temporal bone structures from clinical CT scans.
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 33 papers.
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Who cites it
33 citing papers in PubMed, 73 citations in OpenAlex.
- Synchrotron-Based Deep Learning Network of the Inner Ear: Development and Expert Validation.The Laryngoscope · 2026Article
- Clinician-Guided Deep Learning Segmentation of Skull Base Pneumatization on Computed Tomography Using 3D Slicer and MONAI Label.Life (Basel, Switzerland) · 2026Article
- Review
- Effects of Electrode Position on Vestibular Implant Performance in Rhesus Macaque.Journal of the Association for Research in Otolaryngology : JARO · 2026Article
- Preoperative semi-automatic segmentation in incus defects.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 · 2026Article
- Integration of Trackerless Surface Reconstruction-Based Surgical Navigation With Exoscopic Trans-Mastoid Surgery.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2026Article
- Intelligent diagnosis of ossicular chain malformations on CT: development and clinical efficacy of a cascaded AI framework.Frontiers in bioengineering and biotechnology · 2026Article
- Optimizing spatial normalization of multisubject inner ear MRI: comparison of different geometry-preserving co-registration approaches.Scientific reports · 2025Article
- The feasibility of endoscopic stapes surgery: A three-dimensional planning method.Laryngoscope investigative otolaryngology · 2025Review
- Article
- Application of Artificial Intelligence in Otology: Past, Present, and Future.Journal of clinical medicine · 2024Article
- Objective analysis of orbital rim fracture CT images using curve and area measurement.Scientific reports · 2024Article
- Training and validation of a deep learning U-net architecture general model for automated segmentation of inner ear from CT.European radiology experimental · 2024Article
- Clinical application of high-resolution spiral CT scanning in the diagnosis of auriculotemporal and ossicle.BMC medical imaging · 2024Article
- Surgical Navigation in the Anterior Skull Base Using 3-Dimensional Endoscopy and Surface Reconstruction.JAMA otolaryngology-- head & neck surgery · 2024Article
- Prevention of Cerebrospinal Fluid Leakage in the Anterior Transpetrosal Approach.Journal of clinical medicine · 2024Article
- Towards fully automated inner ear analysis with deep-learning-based joint segmentation and landmark detection framework.Scientific reports · 2023Article
- A Self-Configuring Deep Learning Network for Segmentation of Temporal Bone Anatomy in Cone-Beam CT Imaging.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2023Article
- Applications of Artificial Intelligence in Temporal Bone Imaging: Advances and Future Challenges.Cureus · 2023Review
- Automated Segmentation of Optical Coherence Tomography Images of the Human Tympanic Membrane Using Deep Learning.Algorithms · 2023Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Middle- and inner-ear surgery is a vital treatment option in hearing loss, infections, and tumors of the lateral skull base. Segmentation of otologic structures from computed tomography (CT) has many potential applications for improving surgical planning but can be an arduous and time-consuming task. We propose an end-to-end solution for the automated segmentation of temporal bone CT using convolutional neural networks (CNN). Using 150 manually segmented CT scans, a comparison of 3 CNN models (AH-Net, U-Net, ResNet) was conducted to compare Dice coefficient, Hausdorff distance, and speed of segmentation of the inner ear, ossicles, facial nerve and sigmoid sinus. Using AH-Net, the Dice coefficient was 0.91 for the inner ear; 0.85 for the ossicles; 0.75 for the facial nerve; and 0.86 for the sigmoid sinus. The average Hausdorff distance was 0.25, 0.21, 0.24 and 0.45 mm, respectively. Blinded experts assessed the accuracy of both techniques, and there was no statistical difference between the ratings for the two methods (p = 0.93). Objective and subjective assessment confirm good correlation between automated segmentation of otologic structures and manual segmentation performed by a specialist. This end-to-end automated segmentation pipeline can help to advance the systematic application of augmented reality, simulation, and automation in otologic procedures.
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