كتاب
Model-to-Image Registration via Deep Learning towards Image-Guided Endovascular Interventions
| العنوان: | Model-to-Image Registration via Deep Learning towards Image-Guided Endovascular Interventions |
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| المؤلفون: | Li Z., Mancini M. E., Monizzi G., Andreini D., Ferrigno G., Dankelman J., De Momi E. |
| المساهمون: | Z. Li, M.E. Mancini, G. Monizzi, D. Andreini, G. Ferrigno, J. Dankelman, E. De Momi |
| بيانات النشر: | Institute of Electrical and Electronics Engineers Inc. |
| سنة النشر: | 2021 |
| المجموعة: | The University of Milan: Archivio Istituzionale della Ricerca (AIR) |
| مصطلحات موضوعية: | Convolutional Neural Network, Deep Learning, Endovascular Catheterization, Image Registration, Image-guided Interventions, Settore MED/11 - Malattie dell'Apparato Cardiovascolare |
| الوصف: | 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. |
| نوع الوثيقة: | book part |
| اللغة: | English |
| العلاقة: | info:eu-repo/semantics/altIdentifier/isbn/9781665406222; info:eu-repo/semantics/altIdentifier/wos/WOS:000786415500015; ispartofbook:2021 International Symposium on Medical Robotics, ISMR 2021; International Symposium on Medical Robotics, ISMR; firstpage:1; lastpage:6; numberofpages:6; https://hdl.handle.net/2434/1001774Test; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85124801011 |
| DOI: | 10.1109/ISMR48346.2021.9661511 |
| الإتاحة: | https://doi.org/10.1109/ISMR48346.2021.9661511Test https://hdl.handle.net/2434/1001774Test |
| حقوق: | info:eu-repo/semantics/closedAccess |
| رقم الانضمام: | edsbas.FED57819 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1109/ISMR48346.2021.9661511 |
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