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Zhu Y, Chen L, Lu W, Gong Y, Wang X. The application of the nnU-Net-based automatic segmentation model in assisting carotid artery stenosis and carotid atherosclerotic plaque evaluation. Front Physiol 2022; 13:1057800. [PMID: 36561211 PMCID: PMC9763590 DOI: 10.3389/fphys.2022.1057800] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 11/11/2022] [Indexed: 12/12/2022] Open
Abstract
Objective: No new U-net (nnU-Net) is a newly-developed deep learning neural network, whose advantages in medical image segmentation have been noticed recently. This study aimed to investigate the value of the nnU-Net-based model for computed tomography angiography (CTA) imaging in assisting the evaluation of carotid artery stenosis (CAS) and atherosclerotic plaque. Methods: This study retrospectively enrolled 93 CAS-suspected patients who underwent head and neck CTA examination, then randomly divided them into the training set (N = 70) and the validation set (N = 23) in a 3:1 ratio. The radiologist-marked images in the training set were used for the development of the nnU-Net model, which was subsequently tested in the validation set. Results: In the training set, the nnU-Net had already displayed a good performance for CAS diagnosis and atherosclerotic plaque segmentation. Then, its utility was further confirmed in the validation set: the Dice similarity coefficient value of the nnU-Net model in segmenting background, blood vessels, calcification plaques, and dark spots reached 0.975, 0.974 0.795, and 0.498, accordingly. Besides, the nnU-Net model displayed a good consistency with physicians in assessing CAS (Kappa = 0.893), stenosis degree (Kappa = 0.930), the number of calcification plaque (Kappa = 0.922), non-calcification (Kappa = 0.768) and mixed plaque (Kappa = 0.793), as well as the max thickness of calcification plaque (intraclass correlation coefficient = 0.972). Additionally, the evaluation time of the nnU-Net model was shortened compared with the physicians (27.3 ± 4.4 s vs. 296.8 ± 81.1 s, p < 0.001). Conclusion: The automatic segmentation model based on nnU-Net shows good accuracy, reliability, and efficiency in assisting CTA to evaluate CAS and carotid atherosclerotic plaques.
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Affiliation(s)
- Ying Zhu
- First Clinical Medical College, Soochow University, Suzhou, China
| | - Liwei Chen
- Department of Radiology, School of Medicine, Tongren Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Wenjie Lu
- Department of Radiology, School of Medicine, Tongren Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Yongjun Gong
- Department of Radiology, School of Medicine, Tongren Hospital, Shanghai Jiao Tong University, Shanghai, China,*Correspondence: Yongjun Gong, ; Ximing Wang,
| | - Ximing Wang
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China,*Correspondence: Yongjun Gong, ; Ximing Wang,
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Cau R, Flanders A, Mannelli L, Politi C, Faa G, Suri JS, Saba L. Artificial intelligence in computed tomography plaque characterization: A review. Eur J Radiol 2021; 140:109767. [PMID: 34000598 DOI: 10.1016/j.ejrad.2021.109767] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 04/23/2021] [Accepted: 04/29/2021] [Indexed: 12/12/2022]
Abstract
Cardiovascular disease (CVD) is associated with high mortality around the world. Prevention and early diagnosis are key targets in reducing the socio-economic burden of CVD. Artificial intelligence (AI) has experienced a steady growth due to technological innovations that have to lead to constant development. Several AI algorithms have been applied to various aspects of CVD in order to improve the quality of image acquisition and reconstruction and, at the same time adding information derived from the images to create strong predictive models. In computed tomography angiography (CTA), AI can offer solutions for several parts of plaque analysis, including an automatic assessment of the degree of stenosis and characterization of plaque morphology. A growing body of evidence demonstrates a correlation between some type of plaques, so-called high-risk plaque or vulnerable plaque, and cardiovascular events, independent of the degree of stenosis. The radiologist must apprehend and participate actively in developing and implementing AI in current clinical practice. In this current overview on the existing AI literature, we describe the strengths, limitations, recent applications, and promising developments of employing AI to plaque characterization with CT.
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Affiliation(s)
- Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato, s.s. 554 Monserrato (Cagliari), 09045, Italy
| | - Adam Flanders
- Thomas Jefferson University, 1020 Walnut Street, Philadelphia, PA, United States
| | | | - Carola Politi
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato, s.s. 554 Monserrato (Cagliari), 09045, Italy
| | - Gavino Faa
- Department of Pathology, Azienda Ospedaliero Universitaria (AOU) di Cagliari, University Hospital San Giovanni di Dio, Cagliari, Italy; Proteomic Laboratory - European Center for Brain Research, IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Jasjit S Suri
- Stroke Diagnosis and Monitoring Division ATHEROPOINT LLC, Roseville, CA USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato, s.s. 554 Monserrato (Cagliari), 09045, Italy.
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Caetano Dos Santos FL, Laforest T, Künzi M, Kowalczuk L, Behar-Cohen F, Moser C. Fully automated detection, segmentation, and analysis of in vivo RPE single cells. Eye (Lond) 2020; 35:1473-1481. [PMID: 32555522 DOI: 10.1038/s41433-020-1036-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Accepted: 06/09/2020] [Indexed: 11/09/2022] Open
Abstract
OBJECTIVE To develop a fully automated method of retinal pigmented epithelium (RPE) cells detection, segmentation and analysis based on in vivo cellular resolution images obtained with the transscleral optical phase imaging method (TOPI). METHODS Fourteen TOPI-RPE images from 11 healthy individuals were analysed. The developed image processing method encompassed image filtering and normalisation, detection and removal of blood vessels, cell detection and cell membrane segmentation. The produced measures were cellular density of RPE layer, cell area, number of neighbouring cells, eccentricity, circularity and solidity. In addition, we proposed coefficient of variation (CV) of RPE cellular membrane (CMDCV) and the solidity of the RPE cell membrane-shape as new metrics for the assessment of RPE single cells. RESULTS The observed median cellular density of the RPE layer was 3743 cells/µm2 (interquartile rate (IQR) 1687), with a median observed RPE cell area of 193 µm2 (IQR 141). The mean number of neighbouring cells was 5.22 (standard deviation (SD) 0.05) per RPE cell. The mean RPE cell eccentricity was 0.67 (SD 0.02), median circularity 0.83 (IQR 0.01), and median solidity 0.92 (IQR 0.00). The median CMDCV was 0.19 (IQR 0.02). The method is characterised by a median image processing and analysis time of 48 sec (IQR 12) per image. CONCLUSIONS The present study provides the first fully automated quantitative assessment of human RPE single cells in vivo. The method provides a baseline for future research in the field of clinical ophthalmology, enabling characterisation and diagnostics of retinal diseases at the single-cell level.
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Affiliation(s)
| | - Timothé Laforest
- Laboratory of Applied Photonic Devices (LAPD), School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | | | - Laura Kowalczuk
- Laboratory of Applied Photonic Devices (LAPD), School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Department of Ophthalmology, Jules-Gonin Eye Hospital, Fondation Asile des aveugles, University of Lausanne, Lausanne, Switzerland
| | - Francine Behar-Cohen
- INSERM UMR_S 1138, Team 17, Centre de Recherche des Cordeliers, University of Pierre et Marie Curie, Paris Descartes University, Sorbonne Paris Cité, Paris, France.,Department of Ophthalmology, Ophthalmopole, Cochin Hospital, Assistance Publique, Hôpitaux de Paris, Paris, France
| | - Christophe Moser
- Laboratory of Applied Photonic Devices (LAPD), School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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Saxena A, Ng EYK, Lim ST. Imaging modalities to diagnose carotid artery stenosis: progress and prospect. Biomed Eng Online 2019; 18:66. [PMID: 31138235 PMCID: PMC6537161 DOI: 10.1186/s12938-019-0685-7] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Accepted: 05/17/2019] [Indexed: 12/25/2022] Open
Abstract
In the past few decades, imaging has been developed to a high level of sophistication. Improvements from one-dimension (1D) to 2D images, and from 2D images to 3D models, have revolutionized the field of imaging. This not only helps in diagnosing various critical and fatal diseases in the early stages but also contributes to making informed clinical decisions on the follow-up treatment profile. Carotid artery stenosis (CAS) may potentially cause debilitating stroke, and its accurate early detection is therefore important. In this paper, the technical development of various CAS diagnosis imaging modalities and its impact on the clinical efficacy is thoroughly reviewed. These imaging modalities include duplex ultrasound (DUS), computed tomography angiography (CTA) and magnetic resonance angiography (MRA). For each of the imaging modalities considered, imaging methodology (principle), critical imaging parameters, and the extent of imaging the vulnerable plaque are discussed. DUS is usually the initial recommended CAS diagnostic examination. However, for the therapeutic intervention, either MRA or CTA is recommended for confirmation, and for added information on intracranial cerebral circulation and aortic arch condition for procedural planning. Over the past few decades, the focus of CAS diagnosis has also shifted from pure stenosis quantification to plaque characterization. This has led to further advancement in the existing imaging tools and development of other potential imaging tools like Optical coherence tomography (OCT), photoacoustic tomography (PAT), and infrared (IR) thermography.
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Affiliation(s)
- Ashish Saxena
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Ave, Block N3, Singapore, 639798, Singapore
| | - Eddie Yin Kwee Ng
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Ave, Block N3, Singapore, 639798, Singapore.
| | - Soo Teik Lim
- Department of Cardiology, National Heart Center Singapore, 5 Hospital Dr, Singapore, 169609, Singapore
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VASIM: an automated tool for the quantification of carotid atherosclerosis by computed tomography angiography. Int J Cardiovasc Imaging 2019; 35:1149-1159. [DOI: 10.1007/s10554-019-01549-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Accepted: 01/28/2019] [Indexed: 10/27/2022]
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Reiber JHC, De Sutter J, Schoenhagen P, Stillman AE, Vande Veire NRL. Cardiovascular imaging 2016 in the International Journal of Cardiovascular Imaging. Int J Cardiovasc Imaging 2017; 33:761-770. [PMID: 28315986 PMCID: PMC5406479 DOI: 10.1007/s10554-017-1111-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Johan H C Reiber
- Department of Radiology, Division of Image Processing, Leiden University Medical Center, Leiden, The Netherlands.
| | - Johan De Sutter
- Department of Cardiology, AZ Maria Middelares Gent and University Gent, Ghent, Belgium
| | - Paul Schoenhagen
- Department of Radiology, The Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Arthur E Stillman
- Department of Radiology, Emory University Hospital, Atlanta, GA, USA
| | - Nico R L Vande Veire
- Department of Cardiology, AZ Maria Middelares Gent and Free University Brussels, Brussels, Belgium
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