Model-to-Image Registration via Deep Learning towards Image-Guided Endovascular Interventions

Model-to-Image Registration via Deep Learning towards Image-Guided Endovascular Interventions

Li, Z. (TU Delft Medical Instruments & Bio-Inspired Technology; Politecnico di Milano)
Mancini, Maria Elisabetta (IRCCS)
Monizzi, Giovanni (IRCCS)
Andreini, Daniele (IRCCS; University of Milan)
Ferrigno, Giancarlo (Politecnico di Milano)
Dankelman, J. (TU Delft Medical Instruments & Bio-Inspired Technology)
De Momi, Elena (Politecnico di Milano)

2021

Cardiologists highlight the need for an intra-operative 3D visualization to assist interventions. The intra-operative 2D X-ray/Digital Subtraction Angiography (DSA) images in the standard clinical workflow limit cardiologists’ views significantly. Compared with image-to-image registration, model-to-image registration is an essential approach taking advantage of the reuse of pre-operative 3D models reconstructed from Computed Tomography Angiography (CTA) images. Traditional optimized-based registration methods suffer severely from high computational complexity. Moreover, the consequence of lacking ground truth for learning-based registration approaches should not be neglected. To overcome these challenges, we introduce a model-to-image registration framework via deep learning for image-guided endovascular catheterization. This work performs autonomous vessel segmentation from intra-operative fluoroscopy images via a deep residual U-net and a model-to-image matching via a convolutional neural network. For this study, image data were collected from 10 patients who performed Transcatheter Aortic Valve Implantation (TAVI) procedures. It was found that vessel segmentation of test data results in median values of Dice Similarity Coefficient, Precision, and Recall of (0.75, 0.58, 0.67) for femoral artery, and (0.71, 0.56, 0.74) for aortic root. The segmentation network behaves better than manual annotation, and it recognizes part of vessels that were not labeled manually. Image matching between the transformed moving image and the fixed image results in a median value of Recall of 0.90. The proposed approach achieves a good accuracy of vessel segmentation and a good recall value of model-to-image matching.

Endovascular Catheterization
Image-guided Interventions
Image Registration
Deep Learning
Convolutional Neural Network

http://resolver.tudelft.nl/uuid:a207a943-0e56-4dbb-bfde-2d668a43b81a

https://doi.org/10.1109/ISMR48346.2021.9661511

IEEE

2022-07-03

978-1-6654-0623-9

Proceedings of the International Symposium on Medical Robotics (ISMR 2021)

2021 International Symposium on Medical Robotics (ISMR), 2021-11-17 → 2021-11-19, Atlanta, United States

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2021 Z. Li, Maria Elisabetta Mancini, Giovanni Monizzi, Daniele Andreini, Giancarlo Ferrigno, J. Dankelman, Elena De Momi