Generation of fluoroscopy-alike radiographs as alternative datasets for deep learning in interventional radiology.

Autor: Fum WKS; Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.; Division of Radiological Sciences, Singapore General Hospital, Outram Road, Singapore, 169608, Singapore., Md Shah MN; Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, 50603, Kuala Lumpur, Malaysia., Raja Aman RRA; Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, 50603, Kuala Lumpur, Malaysia., Abd Kadir KA; Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, 50603, Kuala Lumpur, Malaysia., Wen DW; Department of Vascular and Interventional Radiology, Singapore General Hospital, Outram Road, Singapore, 169608, Singapore., Leong S; Department of Vascular and Interventional Radiology, Singapore General Hospital, Outram Road, Singapore, 169608, Singapore., Tan LK; Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, 50603, Kuala Lumpur, Malaysia. lktan@um.edu.my.
Jazyk: angličtina
Zdroj: Physical and engineering sciences in medicine [Phys Eng Sci Med] 2023 Dec; Vol. 46 (4), pp. 1535-1552. Date of Electronic Publication: 2023 Sep 11.
DOI: 10.1007/s13246-023-01317-5
Abstrakt: In fluoroscopy-guided interventions (FGIs), obtaining large quantities of labelled data for deep learning (DL) can be difficult. Synthetic labelled data can serve as an alternative, generated via pseudo 2D projections of CT volumetric data. However, contrasted vessels have low visibility in simple 2D projections of contrasted CT data. To overcome this, we propose an alternative method to generate fluoroscopy-like radiographs from contrasted head CT Angiography (CTA) volumetric data. The technique involves segmentation of brain tissue, bone, and contrasted vessels from CTA volumetric data, followed by an algorithm to adjust HU values, and finally, a standard ray-based projection is applied to generate the 2D image. The resulting synthetic images were compared to clinical fluoroscopy images for perceptual similarity and subject contrast measurements. Good perceptual similarity was demonstrated on vessel-enhanced synthetic images as compared to the clinical fluoroscopic images. Statistical tests of equivalence show that enhanced synthetic and clinical images have statistically equivalent mean subject contrast within 25% bounds. Furthermore, validation experiments confirmed that the proposed method for generating synthetic images improved the performance of DL models in certain regression tasks, such as localizing anatomical landmarks in clinical fluoroscopy images. Through enhanced pseudo 2D projection of CTA volume data, synthetic images with similar features to real clinical fluoroscopic images can be generated. The use of synthetic images as an alternative source for DL datasets represents a potential solution to the application of DL in FGIs procedures.
(© 2023. Australasian College of Physical Scientists and Engineers in Medicine.)
Databáze: MEDLINE
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