Abstract
Automated clinical decision support systems rely on accurate analysis of three-dimensional (3D) medical and dental images to assist clinicians in diagnosis, treatment planning, intervention, and assessment of growth and treatment effects. However, analyzing longitudinal 3D images requires standardized orientation and registration, which can be laborious and error-prone tasks dependent on structures of reference for registration. This paper proposes two novel tools to automatically perform the orientation and registration of 3D Cone-Beam Computed Tomography (CBCT) scans with high accuracy (<3\(^\circ \) and <2 mm of angular and linear errors when compared to expert clinicians). These tools have undergone rigorous testing, and are currently being evaluated by clinicians who utilize the 3D Slicer open-source platform. Our work aims to reduce the sources of error in the 3D medical image analysis workflow by automating these operations. These methods combine conventional image processing approaches and Artificial Intelligence (AI) based models trained and tested on de-identified CBCT volumetric images. Our results showed robust performance for standardized and reproducible image orientation and registration that provide a more complete understanding of individual patient facial growth and response to orthopedic treatment in less than 5 min.
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A Appendix
A Appendix
Maxillary standardized orientation with Occlusal and Midsagittal plane. With both the 3D rendering and the different slice views.
Schematic representation of the MONAI Densenet used for detecting the orientation of CBCT scan. The directional vector prediction of the current input scan is in blue while the goal orientation is in red. (Color figure online)
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Anchling, L. et al. (2023). Automated Orientation and Registration of Cone-Beam Computed Tomography Scans. In: Wesarg, S., et al. Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging. CLIP EPIMI FAIMI 2023 2023 2023. Lecture Notes in Computer Science, vol 14242. Springer, Cham. https://doi.org/10.1007/978-3-031-45249-9_5
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DOI: https://doi.org/10.1007/978-3-031-45249-9_5
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