ArticleBiomedical engineering letters2021
Deep learning approach for the segmentation of aneurysmal ascending aorta.
Article in Biomedical engineering letters, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 56 citations in OpenAlex.
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- Automatic Diagnosis, Classification, and Segmentation of Abdominal Aortic Aneurysm and Dissection from Computed Tomography Images.Diagnostics (Basel, Switzerland) · 2025Article
- Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues.Journal of imaging · 2025Review
- A Synergistic Overview between Microfluidics and Numerical Research for Vascular Flow and Pathological Investigations.Sensors (Basel, Switzerland) · 2024Review
- PSMA-positive prostatic volume prediction with deep learning based on T2-weighted MRI.La Radiologia medica · 2024Article
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- Fully automated pipeline for measurement of the thoracic aorta using joint segmentation and localization neural network.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Bi-DCNet: Bilateral Network with Dilated Convolutions for Left Ventricle Segmentation.Life (Basel, Switzerland) · 2023Article
- Practical review on photoacoustic computed tomography using curved ultrasound array transducer.Biomedical engineering letters · 2022Review
- Medical Image-Based Computational Fluid Dynamics and Fluid-Structure Interaction Analysis in Vascular Diseases.Frontiers in bioengineering and biotechnology · 2022Review
- Development and validation of the 3D U-Net algorithm for segmentation of pelvic lymph nodes on diffusion-weighted images.BMC medical imaging · 2021Article
- Customized Efficient Neural Network for COVID-19 Infected Region Identification in CT Images.Journal of imaging · 2021Article
- Synthetic CT generation from weakly paired MR images using cycle-consistent GAN for MR-guided radiotherapy.Biomedical engineering letters · 2021Article
- Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications.Journal of imaging · 2021Article
- Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging.Applied sciences (Basel, Switzerland) · 2021Article
- Automated Segmental Analysis of Fully Quantitative Myocardial Blood Flow Maps by First-Pass Perfusion Cardiovascular Magnetic Resonance.IEEE access : practical innovations, open solutions · 2021Article
- In-Series U-Net Network to 3D Tumor Image Reconstruction for Liver Hepatocellular Carcinoma Recognition.Diagnostics (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 2 countries.
Funding
Abstract
Diagnosis of ascending thoracic aortic aneurysm (ATAA) is based on the measurement of the maximum aortic diameter, but size is not a good predictor of the risk of adverse events. There is growing interest in the development of novel image-derived risk strategies to improve patient risk management towards a highly individualized level. In this study, the feasibility and efficacy of deep learning for the automatic segmentation of ATAAs was investigated using UNet, ENet, and ERFNet techniques. Specifically, CT angiography done on 72 patients with ATAAs and different valve morphology (i.e., tricuspid aortic valve, TAV, and bicuspid aortic valve, BAV) were semi-automatically segmented with Mimics software (Materialize NV, Leuven, Belgium), and then used for training of the tested deep learning models. The segmentation performance in terms of accuracy and time inference were compared using several parameters. All deep learning models reported a dice score higher than 88%, suggesting a good agreement between predicted and manual ATAA segmentation. We found that the ENet and UNet are more accurate than ERFNet, with the ENet much faster than UNet. This study demonstrated that deep learning models can rapidly segment and quantify the 3D geometry of ATAAs with high accuracy, thereby facilitating the expansion into clinical workflow of personalized approach to the management of patients with ATAAs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.