@article{Bianchi2023,

title = {Automated Orientation and Registration of Cone-Beam Computed Tomography Scans.},

author = {Anchling L and Hutin N and Huang Y and Barone S and Roberts S and Miranda F and et al},

url = {https://doi.org/10.1007/978-3-031-45249-9_5},

doi = {10.1007/978-3-031-45249-9_5},

isbn = {978-3-031-45249-9},

year  = {2023},

date = {2023-10-09},

urldate = {2023-10-09},

journal = {Lecture Notes in Computer Science},

volume = {14242},

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 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.},

keywords = {3D CBCT scans, Deep Learning, Image processing, medical image registration, standardized orientation},

pubstate = {published},

tppubtype = {article}

}