CT slice alignment to whole-body reference geometry by convolutional neural network

Jackson, Price, Korte, James, McIntosh, Lachlan, Kron, Tomas, Ellul, Jason, Li, Jason, and Hardcastle, Nicholas (2021) CT slice alignment to whole-body reference geometry by convolutional neural network. Physical and Engineering Sciences in Medicine, 44 (4). pp. 1213-1219.

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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.

Item ID: 75271
Item Type: Article (Research - C1)
ISSN: 2662-4737
Keywords: Alignment, Computed tomography, Neural networks
Copyright Information: © Australasian College of Physical Scientists and Engineers in Medicine 2021
Date Deposited: 18 Aug 2022 01:17
FoR Codes: 40 ENGINEERING > 4003 Biomedical engineering > 400304 Biomedical imaging @ 30%
32 BIOMEDICAL AND CLINICAL SCIENCES > 3202 Clinical sciences > 320222 Radiology and organ imaging @ 30%
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461104 Neural networks @ 40%
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