UR-CarA-Net: A Cascaded Framework With Uncertainty Regularization for Automated Segmentation of Carotid Arteries on Black Blood MR Images-Reference-Cited by-同舟云学术

UR-CarA-Net: A Cascaded Framework With Uncertainty Regularization for Automated Segmentation of Carotid Arteries on Black Blood MR Images

Published:2023 Issue: Volume:11 Page:26637-26651
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Lavrova Elizaveta¹^ORCID,Salahuddin Zohaib²,Woodruff Henry C.²^ORCID,Kassem Mohamed³,Camarasa Robin⁴,Van Kolk Anja G. Der⁵^ORCID,Nederkoorn Paul J.⁶,Bos Daniel⁴,Hendrikse Jeroen⁵,Kooi M. Eline³^ORCID,Lambin Philippe²

Affiliation:

1. GIGA Cyclotron Research Centre In Vivo Imaging, University of Liège, Liège, Belgium

2. Department of Precision Medicine, The D-Laboratory, GROW-School for Oncology and Reproduction, Maastricht University, Maastricht, MD, The Netherlands

3. Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, HX, The Netherlands

4. Department of Radiology and Nuclear Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, GD, The Netherlands

5. Department of Radiology, University Medical Center Utrecht, Utrecht, CX, The Netherlands

6. Department of Neurology, Amsterdam UMC, Amsterdam, MB, The Netherlands

Funder

European Union’s Horizon 2020 Research and Innovation Program CHAIMELEON

EuCanImage

TRANSCAN Joint Transnational Call 2016

Innovative Medicines Initiative (IMI)-Optimal Treatment for Patients with Solid Tumours in Europe Through Artificial intelligence

Liege-Maastricht Imaging Valley Grant

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10073528.pdf?arnumber=10073528

Reference42 articles.

1. Cooperative carotid artery centerline extraction in MRI

2. Label propagation for 3D carotid vessel wall segmentation and atherosclerosis diagnosis;hu;arXiv 2208 13337,2022

3. Learning-based automated segmentation of the carotid artery vessel wall in dual-sequence MRI using subdivision surface fitting

4. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation

5. Quantification of atherosclerotic plaque components using in vivo MRI and supervised classifiers

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1. Learning carotid vessel wall segmentation in black-blood MRI using sparsely sampled cross-sections from 3D data;Journal of Medical Imaging;2024-07-12

2. Learning carotid vessel wall segmentation in black blood MRI using sparsely sampled cross-sections from 3D data;Medical Imaging 2024: Computer-Aided Diagnosis;2024-04-03

3. Uncertainty-Based Quality Assurance of Carotid Artery Wall Segmentation in Black-Blood MRI;Uncertainty for Safe Utilization of Machine Learning in Medical Imaging;2023