ArticleOtolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery2022
Automated Registration-Based Temporal Bone Computed Tomography Segmentation for Applications in Neurotologic Surgery.
Article in Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- AI-Driven Innovations in Hearing Health: A Review of Artificial Intelligence Applications in Audiology and Hearing Technologies.Current aging science · 2026Review
- Intelligent diagnosis of ossicular chain malformations on CT: development and clinical efficacy of a cascaded AI framework.Frontiers in bioengineering and biotechnology · 2026Article
- Manual and automated facial de-identification techniques for patient imaging with preservation of sinonasal anatomy.International journal of computer assisted radiology and surgery · 2025Article
- Systematic and Individualized Preparation of External Ear Canal Implants: Development and Validation of an Efficient and Accurate Automated Segmentation System.Journal of imaging · 2025Article
- Exploration of Automated Measurement for Ossicular Chains Based on 3-Dimensional Geometric Information.Cyborg and bionic systems (Washington, D.C.) · 2025Article
- A novel therapeutic pathway to the human cochlear nerve.Scientific reports · 2024Article
- A Label-Efficient Framework for Automated Sinonasal CT Segmentation in Image-Guided Surgery.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2024Article
- Autonomous Robotic Systems in Otolaryngology-Head and Neck Surgery.Otolaryngologic clinics of North America · 2024Review
- Training and validation of a deep learning U-net architecture general model for automated segmentation of inner ear from CT.European radiology experimental · 2024Article
- A Deep Learning Framework for Analysis of the Eustachian Tube and the Internal Carotid Artery.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2024Article
- Evaluation of subarcuate canal on CT images in the perspective of clinical basis.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 · 2024Article
- Fully immersive virtual reality for skull-base surgery: surgical training and beyond.International journal of computer assisted radiology and surgery · 2024Article
- Deep Learning Techniques and Imaging in Otorhinolaryngology-A State-of-the-Art Review.Journal of clinical medicine · 2023Review
- Microsurgery Robots: Applications, Design, and Development.Sensors (Basel, Switzerland) · 2023Review
- 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
- Augmented Reality in Otology/Neurotology: A Scoping Review with Implications for Practice and Education.The Laryngoscope · 2023Article
- Diagnosis, Treatment, and Management of Otitis Media with Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2023Review
- TAToo: vision-based joint tracking of anatomy and tool for skull-base surgery.International journal of computer assisted radiology and surgery · 2023Article
- Development and In-Silico and Ex-Vivo Validation of a Software for a Semi-Automated Segmentation of the Round Window Niche to Design a Patient Specific Implant to Treat Inner Ear Disorders.Journal of imaging · 2023Article
- Application value of a deep learning method based on a 3D V-Net convolutional neural network in the recognition and segmentation of the auditory ossicles.Frontiers in neuroinformatics · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
objectiveThis study investigates the accuracy of an automated method to rapidly segment relevant temporal bone anatomy from cone beam computed tomography (CT) images. Implementation of this segmentation pipeline has potential to improve surgical safety and decrease operative time by augmenting preoperative planning and interfacing with image-guided robotic surgical systems. STUDY
designDescriptive study of predicted segmentations.
settingAcademic institution.
methodsWe have developed a computational pipeline based on the symmetric normalization registration method that predicts segmentations of anatomic structures in temporal bone CT scans using a labeled atlas. To evaluate accuracy, we created a data set by manually labeling relevant anatomic structures (eg, ossicles, labyrinth, facial nerve, external auditory canal, dura) for 16 deidentified high-resolution cone beam temporal bone CT images. Automated segmentations from this pipeline were compared against ground-truth manual segmentations by using modified Hausdorff distances and Dice scores. Runtimes were documented to determine the computational requirements of this method.
resultsModified Hausdorff distances and Dice scores between predicted and ground-truth labels were as follows: malleus (0.100 ± 0.054 mm; Dice, 0.827 ± 0.068), incus (0.100 ± 0.033 mm; Dice, 0.837 ± 0.068), stapes (0.157 ± 0.048 mm; Dice, 0.358 ± 0.100), labyrinth (0.169 ± 0.100 mm; Dice, 0.838 ± 0.060), and facial nerve (0.522 ± 0.278 mm; Dice, 0.567 ± 0.130). A quad-core 16GB RAM workstation completed this segmentation pipeline in 10 minutes.
conclusionsWe demonstrated submillimeter accuracy for automated segmentation of temporal bone anatomy when compared against hand-segmented ground truth using our template registration pipeline. This method is not dependent on the training data volume that plagues many complex deep learning models. Favorable runtime and low computational requirements underscore this method's translational potential.
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Registered trials
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.