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

Zhen Li, Maria Elisabetta Mancini, Giovanni Monizzi, Daniele Andreini, Giancarlo Ferrigno, Jenny Dankelman, Elena De Momi

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
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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.
Original languageEnglish
Title of host publicationProceedings of the International Symposium on Medical Robotics (ISMR 2021)
Number of pages6
ISBN (Electronic)978-1-6654-0622-2
ISBN (Print)978-1-6654-0623-9
Publication statusPublished - 2021
Event2021 International Symposium on Medical Robotics (ISMR) - Atlanta, United States
Duration: 17 Nov 202119 Nov 2021


Conference2021 International Symposium on Medical Robotics (ISMR)
Country/TerritoryUnited States

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project
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.


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


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