Abstract
Volumetric medical imaging lacks a standardised coordinate geometry which links image frame-of-reference to specific anatomical regions. This results in an inability to locate anatomy in medical images without visual assessment and precludes a variety of image analysis tasks which could benefit from a standardised, machine-readable coordinate system. In this work, a proposed geometric system that scales based on patient size is described and applied to a variety of cases in computed tomography imaging. Subsequently, a convolutional neural network is trained to associate axial slice CT image appearance with the standardised coordinate value along the patient superior-inferior axis. The trained neural network showed an accuracy of ± 12 mm in the ability to predict per-slice reference location and was relatively stable across all annotated regions ranging from brain to thighs. A version of the trained model along with scripts to perform network training in other applications are made available. Finally, a selection of potential use applications are illustrated including organ localisation, image registration initialisation, and scan length determination for auditing diagnostic reference levels.
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Data availability
Patient data used for training is not available, however scripts to perform training tasks with Tensorflow along with a copy of the trained AI model have been made available at https://github.com/jacksonmedphysics/AI-CT_to_Reference_Geometry.
Code availability
See above.
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Acknowledgements
Price Jackson is supported by a fellowship from the Victorian Cancer Agency.
Funding
Price Jackson is supported by the Victorian Cancer Agency on a Nursing and Allied Health Fellowship (CRFNAH17008: Application of modern image processing and machine learning techniques for individualised management and optimised use of targeted therapy in neuroendocrine cancer).
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The retrospective use of patient data for method development (creation of image analysis tools) is considered as part of best-practice optimisation and does not require specific ethics approval or patient consent according to our human research ethics committee.
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Jackson, P., Korte, J., McIntosh, L. et al. CT slice alignment to whole-body reference geometry by convolutional neural network. Phys Eng Sci Med 44, 1213–1219 (2021). https://doi.org/10.1007/s13246-021-01056-5
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DOI: https://doi.org/10.1007/s13246-021-01056-5