Fum, Wilbur K. S. and Md Shah, Mohammad Nazri and Aman, Raja Rizal Azman Raja and Abd Kadir, Khairul Azmi and Leong, Sum and Tan, Li Kuo (2024) Automatic localization of anatomical landmarks in head cine fluoroscopy images via deep learning. Medical Physics, 51 (10). pp. 7191-7205. ISSN 0094-2405, DOI https://doi.org/10.1002/mp.17349.
Full text not available from this repository.Abstract
BackgroundFluoroscopy guided interventions (FGIs) pose a risk of prolonged radiation exposure; personalized patient dosimetry is necessary to improve patient safety during these procedures. However, current FGIs systems do not capture the precise exposure regions of the patient, making it challenging to perform patient-procedure-specific dosimetry. Thus, there is a pressing need to develop approaches to extract and use this information to enable personalized radiation dosimetry for interventional procedures.PurposeTo propose a deep learning (DL) approach for the automatic localization of 3D anatomical landmarks on randomly collimated and magnified 2D head fluoroscopy images.Materials and methodsThe model was developed with datasets comprising 800 000 pseudo 2D synthetic images (mixture of vessel-enhanced and non-enhancement), each with 55 annotated anatomical landmarks (two are landmarks for eye lenses), generated from 135 retrospectively collected head computed tomography (CT) volumetric data. Before training, dynamic random cropping was performed to mimic the varied field-size collimation in FGI procedures. Gaussian-distributed additive noise was applied to each individual image to enhance the robustness of the DL model in handling image degradation that may occur during clinical image acquisition in a clinical environment. The model was trained with 629 370 synthetic images for approximately 275 000 iterations and evaluated against a synthetic image test set and a clinical fluoroscopy test set.ResultsThe model shows good performance in estimating in- and out-of-image landmark positions and shows feasibility to instantiate the skull shape. The model successfully detected 96.4% and 92.5% 2D and 3D landmarks, respectively, within a 10 mm error on synthetic test images. It demonstrated an average of 3.6 +/- 2.3 mm mean radial error and successfully detected 96.8% 2D landmarks within 10 mm error on clinical fluoroscopy images.ConclusionOur deep-learning model successfully localizes anatomical landmarks and estimates the gross shape of skull structures from collimated 2D projection views. This method may help identify the exposure region required for patient-specific organ dosimetry in FGIs procedures.
| Item Type: | Article |
|---|---|
| Funders: | Fundamental Research Grant Scheme (FRGS) (FRGS/1/2020/SKK0/UM/02/30) |
| Uncontrolled Keywords: | deep learning; fluoroscopy guided interventions; landmarks localization |
| Subjects: | R Medicine > R Medicine (General) |
| Divisions: | Faculty of Medicine Faculty of Medicine > Biomedical Imaging Department |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 10 Apr 2025 07:32 |
| Last Modified: | 10 Apr 2025 07:32 |
| URI: | http://eprints.um.edu.my/id/eprint/46659 |
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