ArticleInternational journal of computer assisted radiology and surgery2020
Retrospective in silico evaluation of optimized preoperative planning for temporal bone surgery.
Article in International journal of computer assisted radiology and surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 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
- LL-MAROCO: A Large Language Model-Assisted Robotic System for Oral and Craniomaxillofacial Osteotomy.Bioengineering (Basel, Switzerland) · 2025Article
- Application of Artificial Intelligence in Otology: Past, Present, and Future.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
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Authors and funding
7 authors.
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Abstract
purposeRobot-assisted surgery at the temporal bone utilizing a flexible drilling unit would allow safer access to clinical targets such as the cochlea or the internal auditory canal by navigating along nonlinear trajectories. One key sub-step for clinical realization of such a procedure is automated preoperative surgical planning that incorporates both segmentation of risk structures and optimized trajectory planning.
methodsWe automatically segment risk structures using 3D U-Nets with probabilistic active shape models. For nonlinear trajectory planning, we adapt bidirectional rapidly exploring random trees on Bézier Splines followed by sequential convex optimization. Functional evaluation, assessing segmentation quality based on the subsequent trajectory planning step, shows the suitability of our novel segmentation approach for this two-step preoperative pipeline.
resultsBased on 24 data sets of the temporal bone, we perform a functional evaluation of preoperative surgical planning. Our experiments show that the automated segmentation provides safe and coherent surface models that can be used in collision detection during motion planning. The source code of the algorithms will be made publicly available.
conclusionOptimized trajectory planning based on shape regularized segmentation leads to safe access canals for temporal bone surgery. Functional evaluation shows the promising results for both 3D U-Net and Bézier Spline trajectories.
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