BRIEF RESEARCH REPORT article
Front. Cardiovasc. Med.
Sec. Cardiovascular Imaging
Volume 12 - 2025 | doi: 10.3389/fcvm.2025.1602780
This article is part of the Research TopicAdvances in Medical Imaging and Artificial Intelligence: Diagnosis and TreatmentView all 3 articles
Optimized aortic root segmentation during transcatheter aortic valve implantation
Provisionally accepted
Nikita V. Laptev1*
Olga M. Gerget2*Julia K. Belova3Evgeny E. Vasilchenko1
Mikhail A. Chernyavskiy3
Viacheslav V. Danilov4*- 1Siberian State Medical University, Tomsk, Russia
- 2V. A. Trapeznikov Institute of Control Sciences (RAS), Moscow, Moscow Oblast, Russia
- 3Almazov National Medical Research Centre, Saint Petersburg, Russia
- 4Pompeu Fabra University, Barcelona, Catalonia, Spain
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Transcatheter aortic valve implantation (TAVI) is a highly effective treatment for patients with severe aortic stenosis. Accurate valve positioning is critical for successful TAVI, and highly accurate real-time visualization - with minimal use of contrast - is especially important for patients with chronic kidney disease. Under fluoroscopic conditions, which often suffer from low contrast, high noise and artifacts, automatic segmentation of anatomical structures using convolutional neural networks (CNNs) can significantly improve the accuracy of valve positioning. This paper presents a comparative analysis of various CNN architectures for automatic aortic root segmentation on angiographic images, with the aim of optimizing the TAVI process. The experimental evaluation included models such as FPN, U-Net++, DeepLabV3+, LinkNet, MA-Net, and PSPNet, all trained and tested with optimally tuned hyperparameters. During training dynamics, DeepLabV3+ and U-Net++ showed stable convergence with median Dice scores around 0.88. However, when evaluated at the patient level, MA-Net and PSPNet outperformed all other models, achieving Dice coefficients of 0.942 and 0.936, and an average symmetric surface distance of 4.1 mm. The findings underscore the potential of incorporating automatic segmentation methods into decision-support systems for cardiac surgery - reducing contrast agent use, minimizing surgical risks, and improving valve positioning accuracy. Future work will focus on expanding the dataset, exploring additional architectures, and adapting the models for real-time application.
Keywords: automatic segmentation, Aortic root, Angiographic images, TAVI, Convolutional Neural Networks
Received: 30 Mar 2025; Accepted: 14 Oct 2025.
Copyright: © 2025 Laptev, Gerget, Belova, Vasilchenko, Chernyavskiy and Danilov. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Nikita V. Laptev, nikitalaptev77@gmail.com
Olga M. Gerget, olgagerget@mail.ru
Viacheslav V. Danilov, viacheslav.v.danilov@gmail.com
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