ArticleOtolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery2023
A Self-Configuring Deep Learning Network for Segmentation of Temporal Bone Anatomy in Cone-Beam CT Imaging.
Article in Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Synchrotron-Based Deep Learning Network of the Inner Ear: Development and Expert Validation.The Laryngoscope · 2026Article
- Image-based deep learning in otology: a state-of-the-art review.Frontiers in neurology · 2026Review
- 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
- Deep multimodal state-space fusion of endoscopic-radiomic and clinical data for survival prediction in colorectal cancer.NPJ digital medicine · 2025Article
- Manual and automated facial de-identification techniques for patient imaging with preservation of sinonasal anatomy.International journal of computer assisted radiology and surgery · 2025Article
- Deep Learning Pipeline for Automated Assessment of Distances Between Tonsillar Tumors and the Internal Carotid Artery.Head & neck · 2025Article
- Automated facial nerve identification in microsurgery with an improved unet.Journal of robotic surgery · 2025Article
- Artificial Intelligence-Powered Quality Assurance: Transforming Diagnostics, Surgery, and Patient Care-Innovations, Limitations, and Future Directions.Life (Basel, Switzerland) · 2025Review
- Artificial intelligence model for automatic 3-dimensional reconstruction of ossicular chain and bony labyrinth from high-resolution CT.Radiology advances · 2025Article
- Application of Artificial Intelligence in Otology: Past, Present, and Future.Journal of clinical medicine · 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
- Training and validation of a deep learning U-net architecture general model for automated segmentation of inner ear from CT.European radiology experimental · 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
- Automated Segmentation of Optical Coherence Tomography Images of the Human Tympanic Membrane Using Deep Learning.Algorithms · 2023Article
- Deep learning-driven MRI trigeminal nerve segmentation with SEVB-net.Frontiers in neuroscience · 2023Article
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Authors and funding
9 authors.
Funding
Abstract
objectivePreoperative planning for otologic or neurotologic procedures often requires manual segmentation of relevant structures, which can be tedious and time-consuming. Automated methods for segmenting multiple geometrically complex structures can not only streamline preoperative planning but also augment minimally invasive and/or robot-assisted procedures in this space. This study evaluates a state-of-the-art deep learning pipeline for semantic segmentation of temporal bone anatomy. STUDY
designA descriptive study of a segmentation network.
settingAcademic institution.
methodsA total of 15 high-resolution cone-beam temporal bone computed tomography (CT) data sets were included in this study. All images were co-registered, with relevant anatomical structures (eg, ossicles, inner ear, facial nerve, chorda tympani, bony labyrinth) manually segmented. Predicted segmentations from no new U-Net (nnU-Net), an open-source 3-dimensional semantic segmentation neural network, were compared against ground-truth segmentations using modified Hausdorff distances (mHD) and Dice scores.
resultsFivefold cross-validation with nnU-Net between predicted and ground-truth labels were as follows: malleus (mHD: 0.044 ± 0.024 mm, dice: 0.914 ± 0.035), incus (mHD: 0.051 ± 0.027 mm, dice: 0.916 ± 0.034), stapes (mHD: 0.147 ± 0.113 mm, dice: 0.560 ± 0.106), bony labyrinth (mHD: 0.038 ± 0.031 mm, dice: 0.952 ± 0.017), and facial nerve (mHD: 0.139 ± 0.072 mm, dice: 0.862 ± 0.039). Comparison against atlas-based segmentation propagation showed significantly higher Dice scores for all structures (p < .05).
conclusionUsing an open-source deep learning pipeline, we demonstrate consistently submillimeter accuracy for semantic CT segmentation of temporal bone anatomy compared to hand-segmented labels. This pipeline has the potential to greatly improve preoperative planning workflows for a variety of otologic and neurotologic procedures and augment existing image guidance and robot-assisted systems for the temporal bone.
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