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Kolbinger FR, El Nahhas OSM, Nackenhorst MC, Brostjan C, Eilenberg W, Busch A, Kather JN. Histopathological evaluation of abdominal aortic aneurysms with deep learning. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.23.24306178. [PMID: 38712033 PMCID: PMC11071559 DOI: 10.1101/2024.04.23.24306178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Computational analysis of histopathological specimens holds promise in identifying biomarkers, elucidating disease mechanisms, and streamlining clinical diagnosis. However, the application of deep learning techniques in vascular pathology remains underexplored. Here, we present a comprehensive evaluation of deep learning-based approaches to analyze digital whole-slide images of abdominal aortic aneurysm samples from 369 patients from three European centers. Deep learning demonstrated robust performance in predicting inflammatory characteristics, particularly in the adventitia, as well as fibrosis grade and remaining elastic fibers in the tunica media. Overall, this study represents the first comprehensive evaluation of computational pathology in vascular disease and has the potential to contribute to improved understanding of abdominal aortic aneurysm pathophysiology and personalization of treatment strategies, particularly when integrated with radiological phenotypes and clinical outcomes.
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Affiliation(s)
- Fiona R. Kolbinger
- Department of Visceral, Thoracic and Vascular Surgery, University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
- Else Kröner Fresenius Center for Digital Health (EKFZ), TUD Dresden University of Technology, Dresden, Germany
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
- Regenstrief Center for Healthcare Engineering (RCHE), Purdue University, West Lafayette, IN, USA
- Department of Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, IN, USA
| | - Omar S. M. El Nahhas
- Else Kröner Fresenius Center for Digital Health (EKFZ), TUD Dresden University of Technology, Dresden, Germany
| | | | - Christine Brostjan
- Division of Vascular Surgery, Department of General Surgery, Medical University of Vienna and Vienna General Hospital, Vienna, Austria
| | - Wolf Eilenberg
- Division of Vascular Surgery, Department of General Surgery, Medical University of Vienna and Vienna General Hospital, Vienna, Austria
| | - Albert Busch
- Department of Visceral, Thoracic and Vascular Surgery, University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Jakob Nikolas Kather
- Else Kröner Fresenius Center for Digital Health (EKFZ), TUD Dresden University of Technology, Dresden, Germany
- Department of Medicine I, University Hospital Dresden, Dresden, Germany
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
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Maas EJ, Nievergeld AHM, Fonken JHC, Thirugnanasambandam M, van Sambeek MRHM, Lopata RGP. 3D-Ultrasound Based Mechanical and Geometrical Analysis of Abdominal Aortic Aneurysms and Relationship to Growth. Ann Biomed Eng 2023; 51:2554-2565. [PMID: 37410199 PMCID: PMC10598132 DOI: 10.1007/s10439-023-03301-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 06/22/2023] [Indexed: 07/07/2023]
Abstract
The heterogeneity of progression of abdominal aortic aneurysms (AAAs) is not well understood. This study investigates which geometrical and mechanical factors, determined using time-resolved 3D ultrasound (3D + t US), correlate with increased growth of the aneurysm. The AAA diameter, volume, wall curvature, distensibility, and compliance in the maximal diameter region were determined automatically from 3D + t echograms of 167 patients. Due to limitations in the field-of-view and visibility of aortic pulsation, measurements of the volume, compliance of a 60 mm long region and the distensibility were possible for 78, 67, and 122 patients, respectively. Validation of the geometrical parameters with CT showed high similarity, with a median similarity index of 0.92 and root-mean-square error (RMSE) of diameters of 3.5 mm. Investigation of Spearman correlation between parameters showed that the elasticity of the aneurysms decreases slightly with diameter (p = 0.034) and decreases significantly with mean arterial pressure (p < 0.0001). The growth of a AAA is significantly related to its diameter, volume, compliance, and surface curvature (p < 0.002). Investigation of a linear growth model showed that compliance is the best predictor for upcoming AAA growth (RMSE 1.70 mm/year). To conclude, mechanical and geometrical parameters of the maximally dilated region of AAAs can automatically and accurately be determined from 3D + t echograms. With this, a prediction can be made about the upcoming AAA growth. This is a step towards more patient-specific characterization of AAAs, leading to better predictability of the progression of the disease and, eventually, improved clinical decision making about the treatment of AAAs.
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Affiliation(s)
- Esther Jorien Maas
- PULS/e Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
- Department of Vascular Surgery, Catharina Hospital Eindhoven, Eindhoven, The Netherlands.
| | - Arjet Helena Margaretha Nievergeld
- PULS/e Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Department of Vascular Surgery, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - Judith Helena Cornelia Fonken
- PULS/e Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Department of Vascular Surgery, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - Mirunalini Thirugnanasambandam
- PULS/e Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Department of Vascular Surgery, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - Marc Rodolph Henricus Maria van Sambeek
- PULS/e Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Department of Vascular Surgery, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
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Lyu Z, King K, Rezaeitaleshmahalleh M, Pienta D, Mu N, Zhao C, Zhou W, Jiang J. Deep-learning-based image segmentation for image-based computational hemodynamic analysis of abdominal aortic aneurysms: a comparison study. Biomed Phys Eng Express 2023; 9:067001. [PMID: 37625388 DOI: 10.1088/2057-1976/acf3ed] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 08/25/2023] [Indexed: 08/27/2023]
Abstract
Computational hemodynamics is increasingly being used to quantify hemodynamic characteristics in and around abdominal aortic aneurysms (AAA) in a patient-specific fashion. However, the time-consuming manual annotation hinders the clinical translation of computational hemodynamic analysis. Thus, we investigate the feasibility of using deep-learning-based image segmentation methods to reduce the time required for manual segmentation. Two of the latest deep-learning-based image segmentation methods, ARU-Net and CACU-Net, were used to test the feasibility of automated computer model creation for computational hemodynamic analysis. Morphological features and hemodynamic metrics of 30 computed tomography angiography (CTA) scans were compared between pre-dictions and manual models. The DICE score for both networks was 0.916, and the correlation value was above 0.95, indicating their ability to generate models comparable to human segmentation. The Bland-Altman analysis shows a good agreement between deep learning and manual segmentation results. Compared with manual (computational hemodynamics) model recreation, the time for automated computer model generation was significantly reduced (from ∼2 h to ∼10 min). Automated image segmentation can significantly reduce time expenses on the recreation of patient-specific AAA models. Moreover, our study showed that both CACU-Net and ARU-Net could accomplish AAA segmentation, and CACU-Net outperformed ARU-Net in terms of accuracy and time-saving.
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Affiliation(s)
- Zonghan Lyu
- Biomedical Engineering, Michigan Technological University, Houghton, Michigan, MI, United States of America
- Joint Center for Biocomputing and Digital Health, Health Research Institute and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, Michigan, MI, United States of America
| | - Kristin King
- Biomedical Engineering, Michigan Technological University, Houghton, Michigan, MI, United States of America
- Joint Center for Biocomputing and Digital Health, Health Research Institute and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, Michigan, MI, United States of America
| | - Mostafa Rezaeitaleshmahalleh
- Biomedical Engineering, Michigan Technological University, Houghton, Michigan, MI, United States of America
- Joint Center for Biocomputing and Digital Health, Health Research Institute and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, Michigan, MI, United States of America
| | - Drew Pienta
- Joint Center for Biocomputing and Digital Health, Health Research Institute and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, Michigan, MI, United States of America
- Applied Computing, Michigan Technological University, Houghton, Michigan, MI, United States of America
| | - Nan Mu
- Biomedical Engineering, Michigan Technological University, Houghton, Michigan, MI, United States of America
- Joint Center for Biocomputing and Digital Health, Health Research Institute and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, Michigan, MI, United States of America
| | - Chen Zhao
- Joint Center for Biocomputing and Digital Health, Health Research Institute and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, Michigan, MI, United States of America
- Applied Computing, Michigan Technological University, Houghton, Michigan, MI, United States of America
| | - Weihua Zhou
- Joint Center for Biocomputing and Digital Health, Health Research Institute and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, Michigan, MI, United States of America
- Applied Computing, Michigan Technological University, Houghton, Michigan, MI, United States of America
| | - Jingfeng Jiang
- Biomedical Engineering, Michigan Technological University, Houghton, Michigan, MI, United States of America
- Joint Center for Biocomputing and Digital Health, Health Research Institute and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, Michigan, MI, United States of America
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, MN, United States of America
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Rezaeitaleshmahalleh M, Lyu Z, Mu N, Zhang X, Rasmussen TE, McBane RD, Jiang J. Characterization of small abdominal aortic aneurysms' growth status using spatial pattern analysis of aneurismal hemodynamics. Sci Rep 2023; 13:13832. [PMID: 37620387 PMCID: PMC10449842 DOI: 10.1038/s41598-023-40139-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 08/05/2023] [Indexed: 08/26/2023] Open
Abstract
Aneurysm hemodynamics is known for its crucial role in the natural history of abdominal aortic aneurysms (AAA). However, there is a lack of well-developed quantitative assessments for disturbed aneurysmal flow. Therefore, we aimed to develop innovative metrics for quantifying disturbed aneurysm hemodynamics and evaluate their effectiveness in predicting the growth status of AAAs, specifically distinguishing between fast-growing and slowly-growing aneurysms. The growth status of aneurysms was classified as fast (≥ 5 mm/year) or slow (< 5 mm/year) based on serial imaging over time. We conducted computational fluid dynamics (CFD) simulations on 70 patients with computed tomography (CT) angiography findings. By converting hemodynamics data (wall shear stress and velocity) located on unstructured meshes into image-like data, we enabled spatial pattern analysis using Radiomics methods, referred to as "Hemodynamics-informatics" (i.e., using informatics techniques to analyze hemodynamic data). Our best model achieved an AUROC of 0.93 and an accuracy of 87.83%, correctly identifying 82.00% of fast-growing and 90.75% of slowly-growing AAAs. Compared with six classification methods, the models incorporating hemodynamics-informatics exhibited an average improvement of 8.40% in AUROC and 7.95% in total accuracy. These preliminary results indicate that hemodynamics-informatics correlates with AAAs' growth status and aids in assessing their progression.
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Affiliation(s)
- Mostafa Rezaeitaleshmahalleh
- Department of Biomedical Engineering, Michigan Technological University, Houghton, MI, USA
- Joint Center for Biocomputing and Digital Health, Health Research Institute, and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, MI, USA
| | - Zonghan Lyu
- Department of Biomedical Engineering, Michigan Technological University, Houghton, MI, USA
- Joint Center for Biocomputing and Digital Health, Health Research Institute, and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, MI, USA
| | - Nan Mu
- Department of Biomedical Engineering, Michigan Technological University, Houghton, MI, USA
- Joint Center for Biocomputing and Digital Health, Health Research Institute, and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, MI, USA
| | - Xiaoming Zhang
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Todd E Rasmussen
- Division of Vascular and Endovascular Surgery, Mayo Clinic, Rochester, MN, USA
| | - Robert D McBane
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Jingfeng Jiang
- Department of Biomedical Engineering, Michigan Technological University, Houghton, MI, USA.
- Joint Center for Biocomputing and Digital Health, Health Research Institute, and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, MI, USA.
- Department of Radiology, Mayo Clinic, Rochester, MN, USA.
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Liang L, Liu M, Elefteriades J, Sun W. PyTorch-FEA: Autograd-enabled finite element analysis methods with applications for biomechanical analysis of human aorta. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 238:107616. [PMID: 37230048 PMCID: PMC10330852 DOI: 10.1016/j.cmpb.2023.107616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 05/15/2023] [Accepted: 05/16/2023] [Indexed: 05/27/2023]
Abstract
BACKGROUND AND OBJECTIVES Finite-element analysis (FEA) is widely used as a standard tool for stress and deformation analysis of solid structures, including human tissues and organs. For instance, FEA can be applied at a patient-specific level to assist in medical diagnosis and treatment planning, such as risk assessment of thoracic aortic aneurysm rupture/dissection. These FEA-based biomechanical assessments often involve both forward and inverse mechanics problems. Current commercial FEA software packages (e.g., Abaqus) and inverse methods exhibit performance issues in either accuracy or speed. METHODS In this study, we propose and develop a new library of FEA code and methods, named PyTorch-FEA, by taking advantage of autograd, an automatic differentiation mechanism in PyTorch. We develop a class of PyTorch-FEA functionalities to solve forward and inverse problems with improved loss functions, and we demonstrate the capability of PyTorch-FEA in a series of applications related to human aorta biomechanics. In one of the inverse methods, we combine PyTorch-FEA with deep neural networks (DNNs) to further improve performance. RESULTS We applied PyTorch-FEA in four fundamental applications for biomechanical analysis of human aorta. In the forward analysis, PyTorch-FEA achieved a significant reduction in computational time without compromising accuracy compared with Abaqus, a commercial FEA package. Compared to other inverse methods, inverse analysis with PyTorch-FEA achieves better performance in either accuracy or speed, or both if combined with DNNs. CONCLUSIONS We have presented PyTorch-FEA, a new library of FEA code and methods, representing a new approach to develop FEA methods to forward and inverse problems in solid mechanics. PyTorch-FEA eases the development of new inverse methods and enables a natural integration of FEA and DNNs, which will have numerous potential applications.
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Affiliation(s)
- Liang Liang
- Department of Computer Science, University of Miami, Coral Gables, FL, United States.
| | - Minliang Liu
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - John Elefteriades
- Aortic Institute, School of Medicine, Yale University, New Haven, CT, United States
| | - Wei Sun
- Sutra Medical Inc, Lake Forest, CA, United States
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The mechanism and therapy of aortic aneurysms. Signal Transduct Target Ther 2023; 8:55. [PMID: 36737432 PMCID: PMC9898314 DOI: 10.1038/s41392-023-01325-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 12/15/2022] [Accepted: 01/14/2023] [Indexed: 02/05/2023] Open
Abstract
Aortic aneurysm is a chronic aortic disease affected by many factors. Although it is generally asymptomatic, it poses a significant threat to human life due to a high risk of rupture. Because of its strong concealment, it is difficult to diagnose the disease in the early stage. At present, there are no effective drugs for the treatment of aneurysms. Surgical intervention and endovascular treatment are the only therapies. Although current studies have discovered that inflammatory responses as well as the production and activation of various proteases promote aortic aneurysm, the specific mechanisms remain unclear. Researchers are further exploring the pathogenesis of aneurysms to find new targets for diagnosis and treatment. To better understand aortic aneurysm, this review elaborates on the discovery history of aortic aneurysm, main classification and clinical manifestations, related molecular mechanisms, clinical cohort studies and animal models, with the ultimate goal of providing insights into the treatment of this devastating disease. The underlying problem with aneurysm disease is weakening of the aortic wall, leading to progressive dilation. If not treated in time, the aortic aneurysm eventually ruptures. An aortic aneurysm is a local enlargement of an artery caused by a weakening of the aortic wall. The disease is usually asymptomatic but leads to high mortality due to the risk of artery rupture.
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Geronzi L, Haigron P, Martinez A, Yan K, Rochette M, Bel-Brunon A, Porterie J, Lin S, Marin-Castrillon DM, Lalande A, Bouchot O, Daniel M, Escrig P, Tomasi J, Valentini PP, Biancolini ME. Assessment of shape-based features ability to predict the ascending aortic aneurysm growth. Front Physiol 2023; 14:1125931. [PMID: 36950300 PMCID: PMC10025384 DOI: 10.3389/fphys.2023.1125931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 02/24/2023] [Indexed: 03/08/2023] Open
Abstract
The current guidelines for the ascending aortic aneurysm (AsAA) treatment recommend surgery mainly according to the maximum diameter assessment. This criterion has already proven to be often inefficient in identifying patients at high risk of aneurysm growth and rupture. In this study, we propose a method to compute a set of local shape features that, in addition to the maximum diameter D, are intended to improve the classification performances for the ascending aortic aneurysm growth risk assessment. Apart from D, these are the ratio DCR between D and the length of the ascending aorta centerline, the ratio EILR between the length of the external and the internal lines and the tortuosity T. 50 patients with two 3D acquisitions at least 6 months apart were segmented and the growth rate (GR) with the shape features related to the first exam computed. The correlation between them has been investigated. After, the dataset was divided into two classes according to the growth rate value. We used six different classifiers with input data exclusively from the first exam to predict the class to which each patient belonged. A first classification was performed using only D and a second with all the shape features together. The performances have been evaluated by computing accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUROC) and positive (negative) likelihood ratio LHR+ (LHR-). A positive correlation was observed between growth rate and DCR (r = 0.511, p = 1.3e-4) and between GR and EILR (r = 0.472, p = 2.7e-4). Overall, the classifiers based on the four metrics outperformed the same ones based only on D. Among the diameter-based classifiers, k-nearest neighbours (KNN) reported the best accuracy (86%), sensitivity (55.6%), AUROC (0.74), LHR+ (7.62) and LHR- (0.48). Concerning the classifiers based on the four shape features, we obtained the best accuracy (94%), sensitivity (66.7%), specificity (100%), AUROC (0.94), LHR+ (+∞) and LHR- (0.33) with support vector machine (SVM). This demonstrates how automatic shape features detection combined with risk classification criteria could be crucial in planning the follow-up of patients with ascending aortic aneurysm and in predicting the possible dangerous progression of the disease.
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Affiliation(s)
- Leonardo Geronzi
- Department of Enterprise Engineering “Mario Lucertini”, University of Rome Tor Vergata, Rome, Italy
- Ansys France, Villeurbanne, France
- *Correspondence: Leonardo Geronzi,
| | - Pascal Haigron
- LTSI–UMR 1099, CHU Rennes, Inserm, University of Rennes, Rennes, France
| | - Antonio Martinez
- Department of Enterprise Engineering “Mario Lucertini”, University of Rome Tor Vergata, Rome, Italy
- Ansys France, Villeurbanne, France
| | - Kexin Yan
- Ansys France, Villeurbanne, France
- LaMCoS, Laboratoire de Mécanique des Contacts et des Structures, CNRS UMR5259, INSA Lyon, University of Lyon, Villeurbanne, France
| | | | - Aline Bel-Brunon
- LaMCoS, Laboratoire de Mécanique des Contacts et des Structures, CNRS UMR5259, INSA Lyon, University of Lyon, Villeurbanne, France
| | - Jean Porterie
- Cardiac Surgery Department, Rangueil University Hospital, Toulouse, France
| | - Siyu Lin
- IMVIA Laboratory, University of Burgundy, Dijon, France
- Medical Imaging Department, University Hospital of Dijon, Dijon, France
| | - Diana Marcela Marin-Castrillon
- IMVIA Laboratory, University of Burgundy, Dijon, France
- Medical Imaging Department, University Hospital of Dijon, Dijon, France
| | - Alain Lalande
- IMVIA Laboratory, University of Burgundy, Dijon, France
- Medical Imaging Department, University Hospital of Dijon, Dijon, France
| | - Olivier Bouchot
- Department of Cardio-Vascular and Thoracic Surgery, University Hospital of Dijon, Dijon, France
| | - Morgan Daniel
- LTSI–UMR 1099, CHU Rennes, Inserm, University of Rennes, Rennes, France
| | - Pierre Escrig
- LTSI–UMR 1099, CHU Rennes, Inserm, University of Rennes, Rennes, France
| | - Jacques Tomasi
- LTSI–UMR 1099, CHU Rennes, Inserm, University of Rennes, Rennes, France
| | - Pier Paolo Valentini
- Department of Enterprise Engineering “Mario Lucertini”, University of Rome Tor Vergata, Rome, Italy
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