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Cryo-balloon catheter localization in X-Ray fluoroscopy using U-net
- Source :
- International Journal of Computer Assisted Radiology and Surgery
- Publication Year :
- 2021
- Publisher :
- Springer International Publishing, 2021.
-
Abstract
- Purpose Automatic identification of interventional devices in X-ray (XR) fluoroscopy offers the potential of improved navigation during transcatheter endovascular procedures. This paper presents a prototype implementation of fully automatic 3D reconstruction of a cryo-balloon catheter during pulmonary vein isolation (PVI) procedures by deep learning approaches. Methods We employ convolutional neural networks (CNN) to automatically identify the cryo-balloon XR marker and catheter shaft in 2D fluoroscopy during PVI. Training data are generated exploiting established semiautomatic techniques, including template-matching and analytical graph building. A first network of U-net architecture uses a single grayscale XR image as input and yields the mask of the XR marker. A second network of the similar architecture is trained using the mask of the XR marker as additional input to the grayscale XR image for the segmentation of the cryo-balloon catheter shaft mask. The structures automatically identified in two 2D images with different angulations are then used to reconstruct the cryo-balloon in 3D. Results Automatic identification of the XR marker was successful in 78% of test cases and in 100% for the catheter shaft. Training of the model for prediction of the XR marker mask was successful with 3426 training samples. Incorporation of the XR marker mask as additional input for the model predicting the catheter shaft allowed to achieve good training result with only 805 training samples. The average prediction time per frame was 14.47 ms for the XR marker and 78.22 ms for the catheter shaft. Localization accuracy for the XR marker yielded on average 1.52 pixels or 0.56 mm. Conclusions In this paper, we report a novel method for automatic detection and 3D reconstruction of the cryo-balloon catheter shaft and marker from 2D fluoroscopic images. Initial evaluation yields promising results thus indicating the high potential of CNNs as alternatives to the current state-of-the-art solutions.
- Subjects :
- Catheters
Computer science
Short Communication
Biomedical Engineering
Health Informatics
02 engineering and technology
Unet
Grayscale
Convolutional neural network
Cryosurgery
030218 nuclear medicine & medical imaging
03 medical and health sciences
Semi-automatic annotation
0302 clinical medicine
0202 electrical engineering, electronic engineering, information engineering
medicine
Image Processing, Computer-Assisted
Fluoroscopy
Humans
Radiology, Nuclear Medicine and imaging
Segmentation
Computer vision
Pixel
medicine.diagnostic_test
business.industry
Deep learning
3D reconstruction
Balloon catheter
General Medicine
Computer Graphics and Computer-Aided Design
Computer Science Applications
Surgery, Computer-Assisted
020201 artificial intelligence & image processing
Surgery
Computer Vision and Pattern Recognition
Artificial intelligence
Automatic segmentation
Neural Networks, Computer
Reconstruction
business
Cryo-balloon
Subjects
Details
- Language :
- English
- ISSN :
- 18616429 and 18616410
- Volume :
- 16
- Issue :
- 8
- Database :
- OpenAIRE
- Journal :
- International Journal of Computer Assisted Radiology and Surgery
- Accession number :
- edsair.doi.dedup.....cbe5fbaba48fc9fb07c12beb52e35125