This study evaluates two approaches applied to computed tomography (CT) images of patients with abdominal aortic aneurysm: one deterministic, based on tools of Approximation Theory, and one based on Artificial Intelligence. Both aim to segment the basal CT images to extract the patent area of the aortic vessel, in order to propose an alternative to nephrotoxic contrast agents for diagnosing this pathology. While the deterministic approach employs sampling Kantorovich operators and the theory behind, leveraging the reconstruction and enhancement capabilities of these operators applied to images, the artificial intelligence-based approach lays on a U-net neural network. The results obtained from testing the two methods have been compared numerically and visually to assess their performances, demonstrating that both models yield accurate results.
3D convolutional neural network for abdominal aortic aneurysm segmentation
Vicomtech Foundation（维克姆泰克基金会）；Biodonostia Health Research Institute（比奥多诺斯蒂亚健康研究所）；Universitat Pompeu Fabra（庞培法布拉大学）；Donostia University Hospital（多诺斯蒂亚大学医院）；ICREA（加泰罗尼亚高等研究院）
Abdominal Aortic Aneurysm Segmentation with a Small Number of Training Subjects
The Hamlyn Centre for Robotic Surgery, Imperial College London, UK（伦敦帝国理工学院哈姆林机器人外科中心）；Academic Division of Surgery, Imperial College London, UK（伦敦帝国理工学院外科系）；Regional Vascular Unit, St Mary’s Hospital, London, UK（伦敦圣玛丽医院区域血管科）
Anatomy-Informed Deep Learning for Abdominal Aortic Aneurysm Segmentation
Faculty of Mathematics, Informatics and Technology, University of Applied Sciences Koblenz（科布伦茨应用科学大学数学、信息学与技术学院）；Department of Radiology, Herz-Jesu-Krankenhaus（赫茨-耶苏医院放射科）
Deep Supervision by Gaussian Pseudo-label-based Morphological Attention for Abdominal Aorta Segmentation in Non-Contrast CTs
Univ Rennes（雷恩大学）；CHU Rennes（雷恩大学医院）；Inserm（法国国家健康与医学研究院）；Centre de Recherche en Information Biomédicale Sino-français (CRIBs)（中法生物医学信息研究中心）；Southeast University（东南大学）
DeepAAA: clinically applicable and generalizable detection of abdominal aortic aneurysm using deep learning
Nuance Communications Inc.（Nuance通信公司）；MGH and BWH Center for Clinical Data Science（麻省总医院与布莱根妇女医院临床数据科学中心）；Brigham and Women’s Hospital (BWH)（布莱根妇女医院）；Massachusetts General Hospital (MGH)（麻省总医院）
Detection and Visualization of Endoleaks in CT Data for Monitoring of Thoracic and Abdominal Aortic Aneurysm Stents
Siemens Medical Solutions（西门子医疗解决方案）；Friedrich-Alexander University of Erlangen-Nuremberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Philipps-University of Marburg（马尔堡菲利普大学）
Wall Shear Stress Estimation in Abdominal Aortic Aneurysms: Towards Generalisable Neural Surrogate Models
organization= Department of Applied Mathematics, Technical Medical Centre, University of Twente , addressline= Drienerlolaan 5 , city= Enschede , postcode= 7522 NB , country= The Netherlands；organization= Cardiovascular Health Technology Centre, University of Twente , addressline= Drienerlolaan 5 , city= Enschede , postcode= 7522 NB , country= The Netherlands；organization= Department of Computer Science, Technical University of Munich , adressline= Boltzmannstr. 3 , city= Garching , postcode= 85748 , country= Germany；organization= Munich Center for Machine Learning , adressline= Boltzmannstr. 3 , city= Garching , postcode= 85748 , country= Germany；organization= Department of Surgery, Amsterdam UMC, Location University of Amsterdam , addressline= Meibergdreef 9 , city= Amsterdam , country= The Netherlands；organization= Department of Surgery, Amsterdam UMC, Location Vrije Universiteit Amsterdam , addressline= De Boelelaan 5 , city= Amsterdam , postcode= 1083 HJ , country= The Netherlands；organization= Amsterdam Cardiovascular Sciences, Atherosclerosis \& Aortic diseases , city= Amsterdam , country= The Netherlands
DeepVox and SAVE-CT: a contrast- and dose-independent 3D deep learning approach for thoracic aorta segmentation and aneurysm prediction using computed tomography scans
Albert Einstein Israeli Institute of Education and Research（阿尔伯特·爱因斯坦以色列教育与研究学院）；Health Innovation Techcenter（健康创新技术中心）；Department of Radiology（放射科）；Department of Vascular and Endovascular Surgery（血管与血管外科）
本研究提出 DeepVox 分割模型和 SAVE-CT 分类模型，在独立于对比剂和剂量的 3D CT 扫描上实现全自动胸主动脉瘤筛查，分割和分类测试集表现优异，有望降低死亡率并优化放射科评估队列。
Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using deep convolutional neural networks
Vicomtech Foundation（Vicomtech基金会）；Biodonostia Health Research Institute（Biodonostia健康研究所）；Universitat Pompeu Fabra（庞培法布拉大学）；Hospital Universitario Donostia（多诺斯蒂亚大学医院）；ICREA（加泰罗尼亚研究与高级研究学院）
Geometric deep learning for local growth prediction on abdominal aortic aneurysm surfaces
Department of Applied Mathematics, Technical Medical Centre, University of Twente（应用数学系、技术医学中心、埃因霍温理工大学）；Department of Surgery, Amsterdam University medical center, Location University of Amsterdam（外科系、阿姆斯特丹大学医学中心、阿姆斯特丹大学）；Department of Surgery, Amsterdam University medical center, location Vrije Universiteit Amsterdam（外科系、阿姆斯特丹大学医学中心、自由大学）；Amsterdam Cardiovascular Sciences, Atherosclerosis and Aortic Diseases（阿姆斯特丹心血管科学、动脉粥样硬化和腹主动脉瘤疾病）
Contrast-agent-induced deterministic component of CT-density in the abdominal aorta during routine angiography: proof of concept study
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Department of Health Care of Moscow（莫斯科卫生部诊断与远程医疗技术研究与实践临床中心）；Bauman Moscow State Technical University（鲍曼莫斯科国立技术大学）；Federal Research Center "Computer Science and Control" of Russian Academy of Sciences（俄罗斯科学院计算机科学与控制联邦研究中心）；I.M. Sechenov First Moscow State Medical University (Sechenov University)（谢切诺夫第一莫斯科国立医科大学（谢切诺夫大学））
Computer-aided shape features extraction and regression models for predicting the ascending aortic aneurysm growth rate
University of Rome Tor Vergata（罗马第二大学）；Ansys France（安sys法国公司）；University of Lyon（里昂大学）；University of Rennes（雷恩大学）；University of Burgundy（勃艮第大学）；University Hospital of Dijon（第戎大学医院）；Rangueil University Hospital（朗格伊大学医院）
CACTUSS: Common Anatomical CT-US Space for US examinations
Technical University of Munich（慕尼黑工业大学）；Sharif University of Technology（谢里夫理工大学）；Klinikum rechts der Isar（慕尼黑 Rechts der Isar 医院）；John Hopkins University（约翰·霍普金斯大学）
Comp2Comp: Open-Source Software with FDA-Cleared Artificial Intelligence Algorithms for Computed Tomography Image Analysis
Stanford Mussallem Center for Biodesign（斯坦福穆萨勒姆生物设计中心）；Stanford Center for Artificial Intelligence in Medicine and Imaging（斯坦福医学与影像人工智能中心）；Stanford Department of Radiology（斯坦福放射科）；Stanford Division of Vascular Surgery, Department of Surgery（斯坦福血管外科 division，外科部）；University of Mississippi Medical Center Department of Data Science（密苏里大学医学中心数据科学部）
A Deep Learning Approach to Automate High-Resolution Blood Vessel Reconstruction on Computerized Tomography Images With or Without the Use of Contrast Agent
AI-based Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo
Graz University of Technology（格拉茨工业大学）；Computer Algorithms for Medicine Laboratory（医学计算机算法实验室）；University Medicine Essen (AöR)（埃森大学医院（AöR））；Affiliated Dongyang Hospital of Wenzhou Medical University（温州医科大学附属东阳医院）
Region of interest detection for efficient aortic segmentation
Vrije Universiteit Brussel (VUB)（自由大学布鲁塞尔（VUB））；Department of Electronics and Informatics (ETRO)（电子与信息学院（ETRO））；Interuniversity Institute of Bioinformatics in Brussels（布鲁塞尔大学间生物信息研究所）；imec；Université Libre de Bruxelles (ULB)（自由大学布鲁塞尔（ULB））；Machine Learning Group（机器学习组）；Universitair Ziekenhuis Brussel (UZ Brussel)（布鲁塞尔大学医院（UZ Brussel））；Department of Radiology（放射科）
