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Nannini G, Saitta S, Baggiano A, Maragna R, Mushtaq S, Pontone G, Redaelli A. A fully automated deep learning approach for coronary artery segmentation and comprehensive characterization. APL Bioeng 2024; 8:016103. [PMID: 38269204 PMCID: PMC10807932 DOI: 10.1063/5.0181281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 01/04/2024] [Indexed: 01/26/2024] Open
Abstract
Coronary computed tomography angiography (CCTA) allows detailed assessment of early markers associated with coronary artery disease (CAD), such as coronary artery calcium (CAC) and tortuosity (CorT). However, their analysis can be time-demanding and biased. We present a fully automated pipeline that performs (i) coronary artery segmentation and (ii) CAC and CorT objective analysis. Our method exploits supervised learning for the segmentation of the lumen, and then, CAC and CorT are automatically quantified. 281 manually annotated CCTA images were used to train a two-stage U-Net-based architecture. The first stage employed a 2.5D U-Net trained on axial, coronal, and sagittal slices for preliminary segmentation, while the second stage utilized a multichannel 3D U-Net for refinement. Then, a geometric post-processing was implemented: vessel centerlines were extracted, and tortuosity score was quantified as the count of branches with three or more bends with change in direction forming an angle >45°. CAC scoring relied on image attenuation. CAC was detected by setting a patient specific threshold, then a region growing algorithm was applied for refinement. The application of the complete pipeline required <5 min per patient. The model trained for coronary segmentation yielded a Dice score of 0.896 and a mean surface distance of 1.027 mm compared to the reference ground truth. Tracts that presented stenosis were correctly segmented. The vessel tortuosity significantly increased locally, moving from proximal, to distal regions (p < 0.001). Calcium volume score exhibited an opposite trend (p < 0.001), with larger plaques in the proximal regions. Volume score was lower in patients with a higher tortuosity score (p < 0.001). Our results suggest a linked negative correlation between tortuosity and calcific plaque formation. We implemented a fast and objective tool, suitable for population studies, that can help clinician in the quantification of CAC and various coronary morphological parameters, which is helpful for CAD risk assessment.
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Affiliation(s)
- Guido Nannini
- Department of Electronics Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Simone Saitta
- Department of Electronics Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | | | - Riccardo Maragna
- Department of Perioperative Cardiology and Cardiovascular Imaging D, Centro Cardiologico Monzino IRCCS, Italy
| | - Saima Mushtaq
- Department of Perioperative Cardiology and Cardiovascular Imaging D, Centro Cardiologico Monzino IRCCS, Italy
| | | | - Alberto Redaelli
- Department of Electronics Information and Bioengineering, Politecnico di Milano, Milan, Italy
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He F, Qi X, Feng Q, Zhang Q, Pan N, Yang C, Liu S. Research on augmented reality navigation of in vitro fenestration of stent-graft based on deep learning and virtual-real registration. Comput Assist Surg (Abingdon) 2023; 28:2289339. [PMID: 38059572 DOI: 10.1080/24699322.2023.2289339] [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] [Indexed: 12/08/2023] Open
Abstract
OBJECTIVES In vitro fenestration of stent-graft (IVFS) demands high-precision navigation methods to achieve optimal surgical outcomes. This study aims to propose an augmented reality (AR) navigation method for IVFS, which can provide in situ overlay display to locate fenestration positions. METHODS We propose an AR navigation method to assist doctors in performing IVFS. A deep learning-based aorta segmentation algorithm is used to achieve automatic and rapid aorta segmentation. The Vuforia-based virtual-real registration and marker recognition algorithm are integrated to ensure accurate in situ AR image. RESULTS The proposed method can provide three-dimensional in situ AR image, and the fiducial registration error after virtual-real registration is 2.070 mm. The aorta segmentation experiment obtains dice similarity coefficient of 91.12% and Hausdorff distance of 2.59, better than conventional algorithms before improvement. CONCLUSIONS The proposed method can intuitively and accurately locate fenestration positions, and therefore can assist doctors in performing IVFS.
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Affiliation(s)
- Fengfeng He
- Institute of Biomedical Engineering, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaoyu Qi
- Department of Vascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qingmin Feng
- Institute of Biomedical Engineering, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qiang Zhang
- Institute of Biomedical Engineering, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ning Pan
- School of Biomedical Engineering, South-Central Minzu University, Wuhan, China
| | - Chao Yang
- Department of Vascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shenglin Liu
- Institute of Biomedical Engineering, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Lareyre F, Yeung KK, Guzzi L, Di Lorenzo G, Chaudhuri A, Behrendt CA, Spanos K, Raffort J. Artificial intelligence in vascular surgical decision making. Semin Vasc Surg 2023; 36:448-453. [PMID: 37863619 DOI: 10.1053/j.semvascsurg.2023.05.004] [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: 01/02/2023] [Revised: 04/17/2023] [Accepted: 05/24/2023] [Indexed: 10/22/2023]
Abstract
Despite advances in prevention, detection, and treatment, cardiovascular disease is a leading cause of mortality and represents a major health problem worldwide. Artificial intelligence and machine learning have brought new insights to the management of vascular diseases by allowing analysis of huge and complex datasets and by offering new techniques to develop advanced imaging analysis. Artificial intelligence-based applications have the potential to improve prognostic evaluation and evidence-based decision making and contribute to vascular therapeutic decision making. In this scoping review, we provide an overview on how artificial intelligence could help in vascular surgical clinical decision making, highlighting potential benefits, current limitations, and future challenges.
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Affiliation(s)
- Fabien Lareyre
- Department of Vascular Surgery, Hospital of Antibes Juan-les-Pins, Antibes, France; Université Côte d'Azur, INSERM U1065, C3M, Nice, France.
| | - Kak Khee Yeung
- Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Department of Surgery, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands
| | - Lisa Guzzi
- Institute 3IA Côte d'Azur, Université Côte d'Azur, Côte d'Azur, France; Epione Team, Inria, Université Côte d'Azur, Sophia Antipolis, France
| | - Gilles Di Lorenzo
- Department of Vascular Surgery, Hospital of Antibes Juan-les-Pins, Antibes, France
| | - Arindam Chaudhuri
- Bedfordshire-Milton Keynes Vascular Centre, Bedfordshire Hospitals NHS Foundation Trust, Bedford, UK
| | - Christian-Alexander Behrendt
- Brandenburg Medical School Theodor-Fontane, Neuruppin, Germany; Department of Vascular and Endovascular Surgery, Asklepios Medical School Hamburg, Asklepios Clinic Wandsbek, Hamburg, Germany
| | - Konstantinos Spanos
- Department of Vascular Surgery, School of Health Sciences, Faculty of Medicine, University Hospital of Larissa, University of Thessaly, Larissa, Greece
| | - Juliette Raffort
- Université Côte d'Azur, INSERM U1065, C3M, Nice, France; Institute 3IA Côte d'Azur, Université Côte d'Azur, Côte d'Azur, France; Clinical Chemistry Laboratory, University Hospital of Nice, Nice, France
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Saitta S, Sturla F, Gorla R, Oliva OA, Votta E, Bedogni F, Redaelli A. A CT-based deep learning system for automatic assessment of aortic root morphology for TAVI planning. Comput Biol Med 2023; 163:107147. [PMID: 37329622 DOI: 10.1016/j.compbiomed.2023.107147] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 05/18/2023] [Accepted: 06/06/2023] [Indexed: 06/19/2023]
Abstract
Accurate planning of transcatheter aortic valve implantation (TAVI) is important to minimize complications, and it requires anatomic evaluation of the aortic root (AR), commonly performed through 3D computed tomography (CT) image analysis. Currently, there is no standard automated solution for this process. Two convolutional neural networks with 3D U-Net architectures (model 1 and model 2) were trained on 310 CT scans for AR analysis. Model 1 performs AR segmentation and model 2 identifies the aortic annulus and sinotubular junction (STJ) contours. After training, the two models were integrated into a fully automated pipeline for geometric analysis of the AR. Results were validated against manual measurements of 178 TAVI candidates. The trained CNNs segmented the AR, annulus, and STJ effectively, resulting in mean Dice scores of 0.93 for the AR, and mean surface distances of 0.73 mm and 0.99 mm for the annulus and STJ, respectively. Automatic measurements were in good agreement with manual annotations, yielding annulus diameters that differed by 0.52 [-2.96, 4.00] mm (bias and 95% limits of agreement for manual minus algorithm). Evaluating the area-derived diameter, bias, and limits of agreement were 0.07 [-0.25, 0.39] mm. STJ and sinuses diameters computed by the automatic method yielded differences of 0.16 [-2.03, 2.34] and 0.1 [-2.93, 3.13] mm, respectively. The proposed tool is a fully automatic solution to quantify morphological biomarkers for pre-TAVI planning. The method was validated against manual annotation from clinical experts and showed to be quick and effective in assessing AR anatomy, with potential for time and cost savings.
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Affiliation(s)
- Simone Saitta
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Francesco Sturla
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy; 3D and Computer Simulation Laboratory, IRCCS Policlinico San Donato, San Donato Milanese, Italy.
| | - Riccardo Gorla
- Department of Clinical and Interventional Cardiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy
| | - Omar A Oliva
- Department of Clinical and Interventional Cardiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy
| | - Emiliano Votta
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy; 3D and Computer Simulation Laboratory, IRCCS Policlinico San Donato, San Donato Milanese, Italy
| | - Francesco Bedogni
- Department of Clinical and Interventional Cardiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy
| | - Alberto Redaelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
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Saitta S, Maga L, Armour C, Votta E, O'Regan DP, Salmasi MY, Athanasiou T, Weinsaft JW, Xu XY, Pirola S, Redaelli A. Data-driven generation of 4D velocity profiles in the aneurysmal ascending aorta. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 233:107468. [PMID: 36921465 DOI: 10.1016/j.cmpb.2023.107468] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 02/15/2023] [Accepted: 03/05/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Numerical simulations of blood flow are a valuable tool to investigate the pathophysiology of ascending thoratic aortic aneurysms (ATAA). To accurately reproduce in vivo hemodynamics, computational fluid dynamics (CFD) models must employ realistic inflow boundary conditions (BCs). However, the limited availability of in vivo velocity measurements, still makes researchers resort to idealized BCs. The aim of this study was to generate and thoroughly characterize a large dataset of synthetic 4D aortic velocity profiles sampled on a 2D cross-section along the ascending aorta with features similar to clinical cohorts of patients with ATAA. METHODS Time-resolved 3D phase contrast magnetic resonance (4D flow MRI) scans of 30 subjects with ATAA were processed through in-house code to extract anatomically consistent cross-sectional planes along the ascending aorta, ensuring spatial alignment among all planes and interpolating all velocity fields to a reference configuration. Velocity profiles of the clinical cohort were extensively characterized by computing flow morphology descriptors of both spatial and temporal features. By exploiting principal component analysis (PCA), a statistical shape model (SSM) of 4D aortic velocity profiles was built and a dataset of 437 synthetic cases with realistic properties was generated. RESULTS Comparison between clinical and synthetic datasets showed that the synthetic data presented similar characteristics as the clinical population in terms of key morphological parameters. The average velocity profile qualitatively resembled a parabolic-shaped profile, but was quantitatively characterized by more complex flow patterns which an idealized profile would not replicate. Statistically significant correlations were found between PCA principal modes of variation and flow descriptors. CONCLUSIONS We built a data-driven generative model of 4D aortic inlet velocity profiles, suitable to be used in computational studies of blood flow. The proposed software system also allows to map any of the generated velocity profiles to the inlet plane of any virtual subject given its coordinate set.
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Affiliation(s)
- Simone Saitta
- Department of Information, Electronics and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Ludovica Maga
- Department of Information, Electronics and Bioengineering, Politecnico di Milano, Milan, Italy; Department of Chemical Engineering, Imperial College London, London, UK
| | - Chloe Armour
- Department of Chemical Engineering, Imperial College London, London, UK
| | - Emiliano Votta
- Department of Information, Electronics and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| | - M Yousuf Salmasi
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Thanos Athanasiou
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Jonathan W Weinsaft
- Department of Medicine (Cardiology), Weill Cornell College, New York, NY, USA
| | - Xiao Yun Xu
- Department of Chemical Engineering, Imperial College London, London, UK
| | - Selene Pirola
- Department of Chemical Engineering, Imperial College London, London, UK; Department of BioMechanical Engineering, 3mE Faculty, Delft University of Technology, Delft, Netherlands.
| | - Alberto Redaelli
- Department of Information, Electronics and Bioengineering, Politecnico di Milano, Milan, Italy
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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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A Combined Deep Learning System for Automatic Detection of “Bovine” Aortic Arch on Computed Tomography Scans. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12042056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The “bovine” aortic arch is an anatomic variant consisting in a common origin of the innominate and left carotid artery (CILCA), associated with a greater risk of thoracic aortic diseases (aneurysms and dissections), stroke, and complications after endovascular procedures. CILCA can be detected by visual assessment of computed tomography (CT) chest scans, but it is rarely reported. We developed a deep learning (DL) segmentation-plus-classification system to automatically detect CILCA based on 302 CT studies acquired at 2 centers. One model (3D U-Net) was trained from scratch (supervised by manual segmentation), validated, and tested for the automatic segmentation of the aortic arch and supra-aortic vessels. Three DL architectures (ResNet50, DenseNet-201, and SqueezeNet), pre-trained over millions of common images, were trained, validated, and tested for the automatic classification of CILCA versus non-CILCA, supervised by radiologist’s classification. The 3D U-Net-plus-DenseNet-201 was found to be the best system (Dice index 0.912); its classification performance obtained from internal, independent testing on 126 patients gave a receiver operating characteristic area under the curve of 87.0%, sensitivity 66.7%, specificity 90.5%, positive predictive value 87.5%, negative predictive value 73.1%, positive likelihood ratio 7.0, and negative likelihood ratio 0.4. In conclusion, a combined DL system applied to chest CT scans was developed and proven to be an effective tool to detect individuals with “bovine” aortic arch with a low rate of false-positive findings.
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