1
|
Aromiwura AA, Cavalcante JL, Kwong RY, Ghazipour A, Amini A, Bax J, Raman S, Pontone G, Kalra DK. The role of artificial intelligence in cardiovascular magnetic resonance imaging. Prog Cardiovasc Dis 2024:S0033-0620(24)00092-6. [PMID: 38925255 DOI: 10.1016/j.pcad.2024.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2024] [Accepted: 06/23/2024] [Indexed: 06/28/2024]
Abstract
Cardiovascular magnetic resonance (CMR) imaging is the gold standard test for myocardial tissue characterization and chamber volumetric and functional evaluation. However, manual CMR analysis can be time-consuming and is subject to intra- and inter-observer variability. Artificial intelligence (AI) is a field that permits automated task performance through the identification of high-level and complex data relationships. In this review, we review the rapidly growing role of AI in CMR, including image acquisition, sequence prescription, artifact detection, reconstruction, segmentation, and data reporting and analysis including quantification of volumes, function, myocardial infarction (MI) and scar detection, and prediction of outcomes. We conclude with a discussion of the emerging challenges to widespread adoption and solutions that will allow for successful, broader uptake of this powerful technology.
Collapse
Affiliation(s)
| | | | - Raymond Y Kwong
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Aryan Ghazipour
- Medical Imaging Laboratory, Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA
| | - Amir Amini
- Medical Imaging Laboratory, Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA
| | - Jeroen Bax
- Department of Cardiology, Leiden University, Leiden, the Netherlands
| | - Subha Raman
- Division of Cardiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Gianluca Pontone
- Department of Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, University of Milan, Milan, Italy
| | - Dinesh K Kalra
- Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA; Center for Artificial Intelligence in Radiological Sciences (CAIRS), Department of Radiology, University of Louisville, Louisville, KY, USA.
| |
Collapse
|
2
|
Zhu J, Bai J, Zhou Z, Liang Y, Chen Z, Chen X, Zhang X. RAS Dataset: A 3D Cardiac LGE-MRI Dataset for Segmentation of Right Atrial Cavity. Sci Data 2024; 11:401. [PMID: 38643183 PMCID: PMC11032400 DOI: 10.1038/s41597-024-03253-9] [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: 10/30/2023] [Accepted: 04/11/2024] [Indexed: 04/22/2024] Open
Abstract
The current challenge in effectively treating atrial fibrillation (AF) stems from a limited understanding of the intricate structure of the human atria. The objective and quantitative interpretation of the right atrium (RA) in late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) scans relies heavily on its precise segmentation. Leveraging the potential of artificial intelligence (AI) for RA segmentation presents a promising solution. However, the successful implementation of AI in this context necessitates access to a substantial volume of annotated LGE-MRI images for model training. In this paper, we present a comprehensive 3D cardiac dataset comprising 50 high-resolution LGE-MRI scans, each meticulously annotated at the pixel level. The annotation process underwent rigorous standardization through crowdsourcing among a panel of medical experts, ensuring the accuracy and consistency of the annotations. Our dataset represents a significant contribution to the field, providing a valuable resource for advancing RA segmentation methods.
Collapse
Affiliation(s)
- Jinwen Zhu
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Jieyun Bai
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, China.
- Auckland Bioengineering Institute, the University of Auckland, Auckland, New Zealand.
| | - Zihao Zhou
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Yaqi Liang
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Zhiting Chen
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Xiaoming Chen
- Department of Cardiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Xiaoshen Zhang
- Department of Cardiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| |
Collapse
|
3
|
Lin H, López-Tapia S, Schiffers F, Wu Y, Gunasekaran S, Hwang J, Bishara D, Kholmovski E, Elbaz M, Passman RS, Kim D, Katsaggelos AK. Usformer: A small network for left atrium segmentation of 3D LGE MRI. Heliyon 2024; 10:e28539. [PMID: 38596055 PMCID: PMC11002571 DOI: 10.1016/j.heliyon.2024.e28539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 03/20/2024] [Indexed: 04/11/2024] Open
Abstract
Left atrial (LA) fibrosis plays a vital role as a mediator in the progression of atrial fibrillation. 3D late gadolinium-enhancement (LGE) MRI has been proven effective in identifying LA fibrosis. Image analysis of 3D LA LGE involves manual segmentation of the LA wall, which is both lengthy and challenging. Automated segmentation poses challenges owing to the diverse intensities in data from various vendors, the limited contrast between LA and surrounding tissues, and the intricate anatomical structures of the LA. Current approaches relying on 3D networks are computationally intensive since 3D LGE MRIs and the networks are large. Regarding this issue, most researchers came up with two-stage methods: initially identifying the LA center using a scaled-down version of the MRIs and subsequently cropping the full-resolution MRIs around the LA center for final segmentation. We propose a lightweight transformer-based 3D architecture, Usformer, designed to precisely segment LA volume in a single stage, eliminating error propagation associated with suboptimal two-stage training. The transposed attention facilitates capturing the global context in large 3D volumes without significant computation requirements. Usformer outperforms the state-of-the-art supervised learning methods in terms of accuracy and speed. First, with the smallest Hausdorff Distance (HD) and Average Symmetric Surface Distance (ASSD), it achieved a dice score of 93.1% and 92.0% in the 2018 Atrial Segmentation Challenge and our local institutional dataset, respectively. Second, the number of parameters and computation complexity are largely reduced by 2.8x and 3.8x, respectively. Moreover, Usformer does not require a large dataset. When only 16 labeled MRI scans are used for training, Usformer achieves a 92.1% dice score in the challenge dataset. The proposed Usformer delineates the boundaries of the LA wall relatively accurately, which may assist in the clinical translation of LA LGE for planning catheter ablation of atrial fibrillation.
Collapse
Affiliation(s)
- Hui Lin
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA
| | - Santiago López-Tapia
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA
| | - Florian Schiffers
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA
| | - Yunan Wu
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA
| | | | - Julia Hwang
- Department of Radiology, Northwestern University, Chicago, IL, USA
| | - Dima Bishara
- Department of Radiology, Northwestern University, Chicago, IL, USA
| | - Eugene Kholmovski
- Department of Biomedical Engineering, Johns Hopkins University, Maryland, USA
| | - Mohammed Elbaz
- Department of Radiology, Northwestern University, Chicago, IL, USA
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA
| | - Rod S. Passman
- Department of Medicine, Northwestern University, Chicago, IL, USA
| | - Daniel Kim
- Department of Radiology, Northwestern University, Chicago, IL, USA
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA
| | - Aggelos K. Katsaggelos
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA
| |
Collapse
|
4
|
Vernikouskaya I, Bertsche D, Metze P, Schneider LM, Rasche V. Multi-network approach for image segmentation in non-contrast enhanced cardiac 3D MRI of arrhythmic patients. Comput Med Imaging Graph 2024; 113:102340. [PMID: 38277768 DOI: 10.1016/j.compmedimag.2024.102340] [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: 09/06/2023] [Revised: 01/16/2024] [Accepted: 01/16/2024] [Indexed: 01/28/2024]
Abstract
Left atrial appendage (LAA) is the source of thrombi formation in more than 90% of strokes in patients with nonvalvular atrial fibrillation. Catheter-based LAA occlusion is being increasingly applied as a treatment strategy to prevent stroke. Anatomical complexity of LAA makes percutaneous occlusion commonly performed under transesophageal echocardiography (TEE) and X-ray (XR) guidance especially challenging. Image fusion techniques integrating 3D anatomical models derived from pre-procedural imaging into the live XR fluoroscopy can be applied to guide each step of the LAA closure. Cardiac magnetic resonance (CMR) imaging gains in importance for radiation-free evaluation of cardiac morphology as alternative to gold-standard TEE or computed tomography angiography (CTA). Manual delineation of cardiac structures from non-contrast enhanced CMR is, however, labor-intensive, tedious, and challenging due to the rather low contrast. Additionally, arrhythmia often impairs the image quality in ECG synchronized acquisitions causing blurring and motion artifacts. Thus, for cardiac segmentation in arrhythmic patients, there is a strong need for an automated image segmentation method. Deep learning-based methods have shown great promise in medical image analysis achieving superior performance in various imaging modalities and different clinical applications. Fully-convolutional neural networks (CNNs), especially U-Net, have become the method of choice for cardiac segmentation. In this paper, we propose an approach for automatic segmentation of cardiac structures from non-contrast enhanced CMR images of arrhythmic patients based on CNNs implemented in a multi-stage pipeline. Two-stage implementation allows subdividing the task into localization of the relevant cardiac structures and segmentation of these structures from the cropped sub-regions obtained from previous step leading to efficient and effective way of automated cardiac segmentation.
Collapse
Affiliation(s)
- Ina Vernikouskaya
- Department of Internal Medicine II, Ulm University Medical Center, Ulm, Germany.
| | - Dagmar Bertsche
- Department of Internal Medicine II, Ulm University Medical Center, Ulm, Germany.
| | - Patrick Metze
- Department of Internal Medicine II, Ulm University Medical Center, Ulm, Germany.
| | - Leonhard M Schneider
- Department of Internal Medicine II, Ulm University Medical Center, Ulm, Germany.
| | - Volker Rasche
- Department of Internal Medicine II, Ulm University Medical Center, Ulm, Germany.
| |
Collapse
|
5
|
Bizimana PC, Zhang Z, Asim M, El-Latif AAA, Hammad M. Learning-based techniques for heart disease prediction: a survey of models and performance metrics. MULTIMEDIA TOOLS AND APPLICATIONS 2023; 83:39867-39921. [DOI: 10.1007/s11042-023-17051-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 07/14/2023] [Accepted: 09/11/2023] [Indexed: 07/23/2024]
|
6
|
Lyu Y, Bennamoun M, Sharif N, Lip GYH, Dwivedi G. Artificial Intelligence in the Image-Guided Care of Atrial Fibrillation. Life (Basel) 2023; 13:1870. [PMID: 37763273 PMCID: PMC10532509 DOI: 10.3390/life13091870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 08/19/2023] [Accepted: 08/30/2023] [Indexed: 09/29/2023] Open
Abstract
Atrial fibrillation arises mainly due to abnormalities in the cardiac conduction system and is associated with anatomical remodeling of the atria and the pulmonary veins. Cardiovascular imaging techniques, such as echocardiography, computed tomography, and magnetic resonance imaging, are crucial in the management of atrial fibrillation, as they not only provide anatomical context to evaluate structural alterations but also help in determining treatment strategies. However, interpreting these images requires significant human expertise. The potential of artificial intelligence in analyzing these images has been repeatedly suggested due to its ability to automate the process with precision comparable to human experts. This review summarizes the benefits of artificial intelligence in enhancing the clinical care of patients with atrial fibrillation through cardiovascular image analysis. It provides a detailed overview of the two most critical steps in image-guided AF management, namely, segmentation and classification. For segmentation, the state-of-the-art artificial intelligence methodologies and the factors influencing the segmentation performance are discussed. For classification, the applications of artificial intelligence in the diagnosis and prognosis of atrial fibrillation are provided. Finally, this review also scrutinizes the current challenges hindering the clinical applicability of these methods, with the aim of guiding future research toward more effective integration into clinical practice.
Collapse
Affiliation(s)
- Yiheng Lyu
- Department of Computer Science and Software Engineering, School of Physics, Mathematics and Computing, The University of Western Australia, Perth, WA 6009, Australia; (Y.L.); (M.B.)
- Harry Perkins Institute of Medical Research, The University of Western Australia, Perth, WA 6009, Australia
| | - Mohammed Bennamoun
- Department of Computer Science and Software Engineering, School of Physics, Mathematics and Computing, The University of Western Australia, Perth, WA 6009, Australia; (Y.L.); (M.B.)
| | - Naeha Sharif
- Department of Computer Science and Software Engineering, School of Physics, Mathematics and Computing, The University of Western Australia, Perth, WA 6009, Australia; (Y.L.); (M.B.)
| | - Gregory Y. H. Lip
- Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool L69 3BX, UK
- Liverpool John Moores University, Liverpool L3 5UX, UK
- Liverpool Heart and Chest Hospital, Liverpool L14 3PE, UK
- Danish Center for Health Services Research, Department of Clinical Medicine, Aalborg University, 9220 Aalborg, Denmark
| | - Girish Dwivedi
- Harry Perkins Institute of Medical Research, The University of Western Australia, Perth, WA 6009, Australia
- Department of Cardiology, Fiona Stanley Hospital, Perth, WA 6150, Australia
- Medical School, The University of Western Australia, Perth, WA 6009, Australia
| |
Collapse
|
7
|
Jafari M, Shoeibi A, Khodatars M, Ghassemi N, Moridian P, Alizadehsani R, Khosravi A, Ling SH, Delfan N, Zhang YD, Wang SH, Gorriz JM, Alinejad-Rokny H, Acharya UR. Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review. Comput Biol Med 2023; 160:106998. [PMID: 37182422 DOI: 10.1016/j.compbiomed.2023.106998] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 03/01/2023] [Accepted: 04/28/2023] [Indexed: 05/16/2023]
Abstract
In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. At early stages, CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMRI) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians as each scan has many slices of data, and the contrast of it might be low. To address these issues, deep learning (DL) techniques have been employed in the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. The following presents research to detect CVDs using CMR images and the most significant DL methods. Another section discussed the challenges in diagnosing CVDs from CMRI data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. Finally, the most important findings of this study are presented in the conclusion section.
Collapse
Affiliation(s)
- Mahboobeh Jafari
- Internship in BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia
| | - Afshin Shoeibi
- Internship in BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia; Data Science and Computational Intelligence Institute, University of Granada, Spain.
| | - Marjane Khodatars
- Data Science and Computational Intelligence Institute, University of Granada, Spain
| | - Navid Ghassemi
- Internship in BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia
| | - Parisa Moridian
- Data Science and Computational Intelligence Institute, University of Granada, Spain
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, Australia
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, Australia
| | - Sai Ho Ling
- Faculty of Engineering and IT, University of Technology Sydney (UTS), Australia
| | - Niloufar Delfan
- Faculty of Computer Engineering, Dept. of Artificial Intelligence Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Yu-Dong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, UK
| | - Shui-Hua Wang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, UK
| | - Juan M Gorriz
- Data Science and Computational Intelligence Institute, University of Granada, Spain; Department of Psychiatry, University of Cambridge, UK
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia; UNSW Data Science Hub, The University of New South Wales, Sydney, NSW, 2052, Australia; Health Data Analytics Program, Centre for Applied Artificial Intelligence, Macquarie University, Sydney, 2109, Australia
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia; Dept. of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
| |
Collapse
|
8
|
Kazi A, Betko S, Salvi A, Menon PG. Automatic Segmentation of the Left Atrium from Computed Tomography Angiography Images. Ann Biomed Eng 2023:10.1007/s10439-023-03170-9. [PMID: 36890303 DOI: 10.1007/s10439-023-03170-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 02/15/2023] [Indexed: 03/10/2023]
Abstract
The left atrial appendage (LAA) causes 91% of thrombi in atrial fibrillation patients, a potential harbinger of stroke. Leveraging computed tomography angiography (CTA) images, radiologists interpret the left atrium (LA) and LAA geometries to stratify stroke risk. Nevertheless, accurate LA segmentation remains a time-consuming task with high inter-observer variability. Binary masks of the LA and their corresponding CTA images were used to train and test a 3D U-Net to automate LA segmentation. One model was trained using the entire unified-image-volume while a second model was trained on regional patch-volumes which were run for inference and then assimilated back into the full volume. The unified-image-volume U-Net achieved median DSCs of 0.92 and 0.88 for the train and test sets, respectively; the patch-volume U-Net achieved median DSCs of 0.90 and 0.89 for the train and test sets, respectively. This indicates that the unified-image-volume and patch-volume U-Net models captured up to 88 and 89% of the LA/LAA boundary's regional complexity, respectively. Additionally, the results indicate that the LA/LAA were fully captured in most of the predicted segmentations. By automating the segmentation process, our deep learning model can expedite LA/LAA shape, informing stratification of stroke risk.
Collapse
Affiliation(s)
- Amaan Kazi
- University of Pittsburgh, 302 Benedum Hall, 3700 O'Hara Street, Pittsburgh, PA, 15213, USA.,ImageRx, Pittsburgh, PA, USA
| | - Sage Betko
- Statistics & Machine Learning, Carnegie Mellon University, Pittsburgh, PA, USA.,ImageRx, Pittsburgh, PA, USA
| | - Anish Salvi
- University of Pittsburgh, 302 Benedum Hall, 3700 O'Hara Street, Pittsburgh, PA, 15213, USA.,ImageRx, Pittsburgh, PA, USA
| | - Prahlad G Menon
- University of Pittsburgh, 302 Benedum Hall, 3700 O'Hara Street, Pittsburgh, PA, 15213, USA. .,ImageRx, Pittsburgh, PA, USA.
| |
Collapse
|
9
|
Comprehensive information integration network for left atrium segmentation on LGE CMR images. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
|
10
|
Bhan A, Mangipudi PS, Goyal A. Left atrium MRI image segmentation using efficient Xception stochastic depth based generative adversarial network. INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT 2023. [DOI: 10.1080/20479700.2023.2166206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Affiliation(s)
- Anupama Bhan
- Department of Electronics and Communication Engineering, Amity School of Engineering and Technology, Amity University, Noida, India
| | - Partha Sarathi Mangipudi
- Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University, Noida, India
| | - Ayush Goyal
- Department of Electrical Engineering and Computer Science, Frank H. Dotterweich College of Engineering, Texas A&M University – Kingsville, Kingsville, TX, USA
| |
Collapse
|
11
|
Qi Y, Hu C, Zuo L, Yang B, Lv Y. Cardiac Magnetic Resonance Image Segmentation Method Based on Multi-Scale Feature Fusion and Sequence Relationship Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:690. [PMID: 36679487 PMCID: PMC9865693 DOI: 10.3390/s23020690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 12/27/2022] [Accepted: 12/30/2022] [Indexed: 06/17/2023]
Abstract
Accurate segmentation of the left atrial structure using magnetic resonance images provides an important basis for the diagnosis of atrial fibrillation (AF) and its treatment using robotic surgery. In this study, an image segmentation method based on sequence relationship learning and multi-scale feature fusion is proposed for 3D to 2D sequence conversion in cardiac magnetic resonance images and the varying scales of left atrial structures within different slices. Firstly, a convolutional neural network layer with an attention module was designed to extract and fuse contextual information at different scales in the image, to strengthen the target features using the correlation between features in different regions within the image, and to improve the network's ability to distinguish the left atrial structure. Secondly, a recurrent neural network layer oriented to two-dimensional images was designed to capture the correlation of left atrial structures in adjacent slices by simulating the continuous relationship between sequential image slices. Finally, a combined loss function was constructed to reduce the effect of positive and negative sample imbalance and improve model stability. The Dice, IoU, and Hausdorff distance values reached 90.73%, 89.37%, and 4.803 mm, respectively, based on the LASC2013 (left atrial segmentation challenge in 2013) dataset; the corresponding values reached 92.05%, 89.41% and 9.056 mm, respectively, based on the ASC2018 (atrial segmentation challenge at 2018) dataset.
Collapse
Affiliation(s)
- Yushi Qi
- College of Mechanical Engineering, Donghua University, Shanghai 201620, China
| | - Chunhu Hu
- College of Mechanical Engineering, Donghua University, Shanghai 201620, China
| | - Liling Zuo
- College of Mechanical Engineering, Donghua University, Shanghai 201620, China
| | - Bo Yang
- College of Mechanical Engineering, Donghua University, Shanghai 201620, China
| | - Youlong Lv
- Institute of Artificial Intelligence, Donghua University, Shanghai 201620, China
| |
Collapse
|
12
|
Wong KKL, Zhang A, Yang K, Wu S, Ghista DN. GCW-UNet segmentation of cardiac magnetic resonance images for evaluation of left atrial enlargement. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106915. [PMID: 35653942 DOI: 10.1016/j.cmpb.2022.106915] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 05/15/2022] [Accepted: 05/24/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Left atrial enlargement (LAE) is an anatomical variation of the left atrium and the result of the long-term increase of left atrial pressure. Most of the increase in stress or volume is due to potential cardiovascular disease. Studies have shown that LAE can independently predict the development of clinically significant cardiovascular disease and heart failure. If the left atrial volume is accurately measured, it will be an essential indicator of human health and an essential means for doctors to find patients' potential diseases. We can analyze the dynamic changes in the left atrial structure and analyze left atrial dilation. However, manual segmentation was inefficient and error-prone before the 3D reconstruction of the left atrium. In order to solve this problem, a convolution neural network (CNN) method based on cardiac magnetic resonance image (MRI) is proposed to automatically segment the left atrial region. METHODOLOGY In this paper, we have proposed and developed a novel U-Net with Gaussian blur and channel weight neural network (GCW-UNet) to automatically segment the left atrial region in the MRI of a patient with LAE. After Gaussian blur, different resolutions of the MRI are obtained. High-resolution MRI clearly shows the detailed features of the left atrium, while low-resolution MRI clearly shows the overall outline of the left atrium, which can solve the problem of more minor MRI features. Adaptive channel weights can enhance the atrial segmentation capability of the network. RESULTS Compared with the state-of-the-art left atrial segmentation methods, our CNN-based technique results in the segmentation of the left atrium being closer to the manual segmentation by an experienced radiologist. On the test datasets, the mean Dice similarity coefficient reaches 93.57%. CONCLUSION Firstly, MRI has a small number of imaging artifacts, which results in low segmentation accuracy. Our method successfully solves the problem. Secondly, due to the high similarity between the background (the area outside the left atrium) and the foreground (the left atrium) in MRI, traditional neural networks misclassify the background as the foreground. Our GCW-Unit can address the imbalanced number of pixels between the foreground and background. Finally, after segmenting the left atrium in the MRI by GCW-Unit, we reconstructed the left atrium to model a three-dimensional heart of a patient suffering from LAE. Based on the different time frames of one heartbeat, we could present the dynamics of the left atrial structure during a cardiac cycle. This can better assist in the evaluation of LAE in heart patients.
Collapse
Affiliation(s)
- Kelvin K L Wong
- School of Computer Science and Engineering, Central South University, Changsha 410083, China.
| | - An Zhang
- Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
| | - Ke Yang
- Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
| | - Shiqian Wu
- Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
| | | |
Collapse
|
13
|
Argentiero A, Muscogiuri G, Rabbat MG, Martini C, Soldato N, Basile P, Baggiano A, Mushtaq S, Fusini L, Mancini ME, Gaibazzi N, Santobuono VE, Sironi S, Pontone G, Guaricci AI. The Applications of Artificial Intelligence in Cardiovascular Magnetic Resonance-A Comprehensive Review. J Clin Med 2022; 11:jcm11102866. [PMID: 35628992 PMCID: PMC9147423 DOI: 10.3390/jcm11102866] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/13/2022] [Accepted: 05/16/2022] [Indexed: 12/11/2022] Open
Abstract
Cardiovascular disease remains an integral field on which new research in both the biomedical and technological fields is based, as it remains the leading cause of mortality and morbidity worldwide. However, despite the progress of cardiac imaging techniques, the heart remains a challenging organ to study. Artificial intelligence (AI) has emerged as one of the major innovations in the field of diagnostic imaging, with a dramatic impact on cardiovascular magnetic resonance imaging (CMR). AI will be increasingly present in the medical world, with strong potential for greater diagnostic efficiency and accuracy. Regarding the use of AI in image acquisition and reconstruction, the main role was to reduce the time of image acquisition and analysis, one of the biggest challenges concerning magnetic resonance; moreover, it has been seen to play a role in the automatic correction of artifacts. The use of these techniques in image segmentation has allowed automatic and accurate quantification of the volumes and masses of the left and right ventricles, with occasional need for manual correction. Furthermore, AI can be a useful tool to directly help the clinician in the diagnosis and derivation of prognostic information of cardiovascular diseases. This review addresses the applications and future prospects of AI in CMR imaging, from image acquisition and reconstruction to image segmentation, tissue characterization, diagnostic evaluation, and prognostication.
Collapse
Affiliation(s)
- Adriana Argentiero
- University Cardiology Unit, Cardio-Thoracic Department, Policlinic University Hospital, 70121 Bari, Italy; (A.A.); (N.S.); (P.B.); (V.E.S.)
| | - Giuseppe Muscogiuri
- School of Medicine and Surgery, University of Milano-Bicocca, 20126 Milan, Italy; (G.M.); (S.S.)
- Department of Radiology, IRCCS Istituto Auxologico Italiano, San Luca Hospital, 20149 Milan, Italy
| | - Mark G. Rabbat
- Division of Cardiology, Loyola University of Chicago, Chicago, IL 60660, USA;
| | - Chiara Martini
- Radiologic Sciences, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy;
| | - Nicolò Soldato
- University Cardiology Unit, Cardio-Thoracic Department, Policlinic University Hospital, 70121 Bari, Italy; (A.A.); (N.S.); (P.B.); (V.E.S.)
| | - Paolo Basile
- University Cardiology Unit, Cardio-Thoracic Department, Policlinic University Hospital, 70121 Bari, Italy; (A.A.); (N.S.); (P.B.); (V.E.S.)
| | - Andrea Baggiano
- Perioperative and Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (A.B.); (S.M.); (L.F.); (M.E.M.); (G.P.)
| | - Saima Mushtaq
- Perioperative and Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (A.B.); (S.M.); (L.F.); (M.E.M.); (G.P.)
| | - Laura Fusini
- Perioperative and Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (A.B.); (S.M.); (L.F.); (M.E.M.); (G.P.)
| | - Maria Elisabetta Mancini
- Perioperative and Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (A.B.); (S.M.); (L.F.); (M.E.M.); (G.P.)
| | - Nicola Gaibazzi
- Department of Cardiology, Azienda Ospedaliero-Universitaria, 43126 Parma, Italy;
| | - Vincenzo Ezio Santobuono
- University Cardiology Unit, Cardio-Thoracic Department, Policlinic University Hospital, 70121 Bari, Italy; (A.A.); (N.S.); (P.B.); (V.E.S.)
| | - Sandro Sironi
- School of Medicine and Surgery, University of Milano-Bicocca, 20126 Milan, Italy; (G.M.); (S.S.)
- Department of Radiology, ASST Papa Giovanni XXIII Hospital, 24127 Bergamo, Italy
| | - Gianluca Pontone
- Perioperative and Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, 20138 Milan, Italy; (A.B.); (S.M.); (L.F.); (M.E.M.); (G.P.)
| | - Andrea Igoren Guaricci
- University Cardiology Unit, Cardio-Thoracic Department, Policlinic University Hospital, 70121 Bari, Italy; (A.A.); (N.S.); (P.B.); (V.E.S.)
- Department of Emergency and Organ Transplantation, University of Bari, 70121 Bari, Italy
- Correspondence:
| |
Collapse
|
14
|
Xiong Z, Stiles MK, Yao Y, Shi R, Nalar A, Hawson J, Lee G, Zhao J. Automatic 3D Surface Reconstruction of the Left Atrium From Clinically Mapped Point Clouds Using Convolutional Neural Networks. Front Physiol 2022; 13:880260. [PMID: 35574484 PMCID: PMC9092219 DOI: 10.3389/fphys.2022.880260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 03/31/2022] [Indexed: 11/17/2022] Open
Abstract
Point clouds are a widely used format for storing information in a memory-efficient and easily manipulatable representation. However, research in the application of point cloud mapping and subsequent organ reconstruction with deep learning, is limited. In particular, current methods for left atrium (LA) visualization using point clouds recorded from clinical mapping during cardiac ablation are proprietary and remain difficult to validate. Many clinics rely on additional imaging such as MRIs/CTs to improve the accuracy of LA mapping. In this study, for the first time, we proposed a novel deep learning framework for the automatic 3D surface reconstruction of the LA directly from point clouds acquired via widely used clinical mapping systems. The backbone of our framework consists of a 30-layer 3D fully convolutional neural network (CNN). The architecture contains skip connections that perform multi-resolution processing to maximize information extraction from the point clouds and ensure a high-resolution prediction by combining features at different receptive levels. We used large kernels with increased receptive fields to address the sparsity of the point clouds. Residual blocks and activation normalization were further implemented to improve the feature learning on sparse inputs. By utilizing a light-weight design with low-depth layers, our CNN took approximately 10 s per patient. Independent testing on two cross-modality clinical datasets showed excellent dice scores of 93% and surface-to-surface distances below 1 pixel. Overall, our study may provide a more efficient, cost-effective 3D LA reconstruction approach during ablation procedures, and potentially lead to improved treatment of cardiac diseases.
Collapse
Affiliation(s)
- Zhaohan Xiong
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Martin K. Stiles
- Waikato Clinical School, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Yan Yao
- Fuwai Hospital, Beijing, China
| | - Rui Shi
- Fuwai Hospital, Beijing, China
| | - Aaqel Nalar
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Josh Hawson
- Royal Melbourne Hospital, Melbourne, VIC, Australia
| | - Geoffrey Lee
- Royal Melbourne Hospital, Melbourne, VIC, Australia
| | - Jichao Zhao
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| |
Collapse
|
15
|
Li L, Zimmer VA, Schnabel JA, Zhuang X. Medical image analysis on left atrial LGE MRI for atrial fibrillation studies: A review. Med Image Anal 2022; 77:102360. [PMID: 35124370 PMCID: PMC7614005 DOI: 10.1016/j.media.2022.102360] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 11/04/2021] [Accepted: 01/10/2022] [Indexed: 02/08/2023]
Abstract
Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is commonly used to visualize and quantify left atrial (LA) scars. The position and extent of LA scars provide important information on the pathophysiology and progression of atrial fibrillation (AF). Hence, LA LGE MRI computing and analysis are essential for computer-assisted diagnosis and treatment stratification of AF patients. Since manual delineations can be time-consuming and subject to intra- and inter-expert variability, automating this computing is highly desired, which nevertheless is still challenging and under-researched. This paper aims to provide a systematic review on computing methods for LA cavity, wall, scar, and ablation gap segmentation and quantification from LGE MRI, and the related literature for AF studies. Specifically, we first summarize AF-related imaging techniques, particularly LGE MRI. Then, we review the methodologies of the four computing tasks in detail and summarize the validation strategies applied in each task as well as state-of-the-art results on public datasets. Finally, the possible future developments are outlined, with a brief survey on the potential clinical applications of the aforementioned methods. The review indicates that the research into this topic is still in the early stages. Although several methods have been proposed, especially for the LA cavity segmentation, there is still a large scope for further algorithmic developments due to performance issues related to the high variability of enhancement appearance and differences in image acquisition.
Collapse
Affiliation(s)
- Lei Li
- School of Data Science, Fudan University, Shanghai, China; School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China; School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Veronika A Zimmer
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Department of Informatics, Technical University of Munich, Germany
| | - Julia A Schnabel
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Department of Informatics, Technical University of Munich, Germany; Helmholtz Center Munich, Germany
| | - Xiahai Zhuang
- School of Data Science, Fudan University, Shanghai, China.
| |
Collapse
|
16
|
Uslu F, Varela M, Boniface G, Mahenthran T, Chubb H, Bharath AA. LA-Net: A Multi-Task Deep Network for the Segmentation of the Left Atrium. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:456-464. [PMID: 34606450 DOI: 10.1109/tmi.2021.3117495] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Although atrial fibrillation (AF) is the most common sustained atrial arrhythmia, treatment success for this condition remains suboptimal. Information from magnetic resonance imaging (MRI) has the potential to improve treatment efficacy, but there are currently few automatic tools for the segmentation of the atria in MR images. In the study, we propose a LA-Net, a multi-task network optimised to simultaneously generate left atrial segmentation and edge masks from MRI. LA-Net includes cross attention modules (CAMs) and enhanced decoder modules (EDMs) to purposefully select the most meaningful edge information for segmentation and smoothly incorporate it into segmentation masks at multiple-scales. We evaluate the performance of LA-Net on two MR sequences: late gadolinium enhanced (LGE) atrial MRI and atrial short axis balanced steady state free precession (bSSFP) MRI. LA-Net gives Hausdorff distances of 12.43 mm and Dice scores of 0.92 on the LGE (STACOM 2018) dataset and Hausdorff distances of 17.41 mm and Dice scores of 0.90 on the bSSFP (in-house) dataset without any post-processing, surpassing previously proposed segmentation networks, including U-Net and SEGANet. Our method allows automatic extraction of information about the LA from MR images, which can play an important role in the management of AF patients.
Collapse
|
17
|
Chen J, Zhang H, Mohiaddin R, Wong T, Firmin D, Keegan J, Yang G. Adaptive Hierarchical Dual Consistency for Semi-Supervised Left Atrium Segmentation on Cross-Domain Data. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:420-433. [PMID: 34534077 DOI: 10.1109/tmi.2021.3113678] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Semi-supervised learning provides great significance in left atrium (LA) segmentation model learning with insufficient labelled data. Generalising semi-supervised learning to cross-domain data is of high importance to further improve model robustness. However, the widely existing distribution difference and sample mismatch between different data domains hinder the generalisation of semi-supervised learning. In this study, we alleviate these problems by proposing an Adaptive Hierarchical Dual Consistency (AHDC) for the semi-supervised LA segmentation on cross-domain data. The AHDC mainly consists of a Bidirectional Adversarial Inference module (BAI) and a Hierarchical Dual Consistency learning module (HDC). The BAI overcomes the difference of distributions and the sample mismatch between two different domains. It mainly learns two mapping networks adversarially to obtain two matched domains through mutual adaptation. The HDC investigates a hierarchical dual learning paradigm for cross-domain semi-supervised segmentation based on the obtained matched domains. It mainly builds two dual-modelling networks for mining the complementary information in both intra-domain and inter-domain. For the intra-domain learning, a consistency constraint is applied to the dual-modelling targets to exploit the complementary modelling information. For the inter-domain learning, a consistency constraint is applied to the LAs modelled by two dual-modelling networks to exploit the complementary knowledge among different data domains. We demonstrated the performance of our proposed AHDC on four 3D late gadolinium enhancement cardiac MR (LGE-CMR) datasets from different centres and a 3D CT dataset. Compared to other state-of-the-art methods, our proposed AHDC achieved higher segmentation accuracy, which indicated its capability in the cross-domain semi-supervised LA segmentation.
Collapse
|
18
|
Li L, Zimmer VA, Schnabel JA, Zhuang X. AtrialJSQnet: A New framework for joint segmentation and quantification of left atrium and scars incorporating spatial and shape information. Med Image Anal 2022; 76:102303. [PMID: 34875581 DOI: 10.1016/j.media.2021.102303] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 10/08/2021] [Accepted: 11/08/2021] [Indexed: 10/19/2022]
Abstract
Left atrial (LA) and atrial scar segmentation from late gadolinium enhanced magnetic resonance imaging (LGE MRI) is an important task in clinical practice. The automatic segmentation is however still challenging due to the poor image quality, the various LA shapes, the thin wall, and the surrounding enhanced regions. Previous methods normally solved the two tasks independently and ignored the intrinsic spatial relationship between LA and scars. In this work, we develop a new framework, namely AtrialJSQnet, where LA segmentation, scar projection onto the LA surface, and scar quantification are performed simultaneously in an end-to-end style. We propose a mechanism of shape attention (SA) via an implicit surface projection to utilize the inherent correlation between LA cavity and scars. In specific, the SA scheme is embedded into a multi-task architecture to perform joint LA segmentation and scar quantification. Besides, a spatial encoding (SE) loss is introduced to incorporate continuous spatial information of the target in order to reduce noisy patches in the predicted segmentation. We evaluated the proposed framework on 60 post-ablation LGE MRIs from the MICCAI2018 Atrial Segmentation Challenge. Moreover, we explored the domain generalization ability of the proposed AtrialJSQnet on 40 pre-ablation LGE MRIs from this challenge and 30 post-ablation multi-center LGE MRIs from another challenge (ISBI2012 Left Atrium Fibrosis and Scar Segmentation Challenge). Extensive experiments on public datasets demonstrated the effect of the proposed AtrialJSQnet, which achieved competitive performance over the state-of-the-art. The relatedness between LA segmentation and scar quantification was explicitly explored and has shown significant performance improvements for both tasks. The code has been released via https://zmiclab.github.io/projects.html.
Collapse
Affiliation(s)
- Lei Li
- School of Data Science, Fudan University, Shanghai, China; School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China; School of Biomedical Engineering and Imaging Sciences, Kings College London, London, UK
| | - Veronika A Zimmer
- School of Biomedical Engineering and Imaging Sciences, Kings College London, London, UK; Technical University Munich, Munich, Germany
| | - Julia A Schnabel
- School of Biomedical Engineering and Imaging Sciences, Kings College London, London, UK; Technical University Munich, Munich, Germany; Helmholtz Center Munich, Germany
| | - Xiahai Zhuang
- School of Data Science, Fudan University, Shanghai, China.
| |
Collapse
|
19
|
Chen J, Yang N, Zhou M, Zhang Z, Yang X. A configurable deep learning framework for medical image analysis. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06873-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
20
|
Chen J, Yang G, Khan H, Zhang H, Zhang Y, Zhao S, Mohiaddin R, Wong T, Firmin D, Keegan J. JAS-GAN: Generative Adversarial Network Based Joint Atrium and Scar Segmentations on Unbalanced Atrial Targets. IEEE J Biomed Health Inform 2022; 26:103-114. [PMID: 33945491 DOI: 10.1109/jbhi.2021.3077469] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Automated and accurate segmentations of left atrium (LA) and atrial scars from late gadolinium-enhanced cardiac magnetic resonance (LGE CMR) images are in high demand for quantifying atrial scars. The previous quantification of atrial scars relies on a two-phase segmentation for LA and atrial scars due to their large volume difference (unbalanced atrial targets). In this paper, we propose an inter-cascade generative adversarial network, namely JAS-GAN, to segment the unbalanced atrial targets from LGE CMR images automatically and accurately in an end-to-end way. Firstly, JAS-GAN investigates an adaptive attention cascade to automatically correlate the segmentation tasks of the unbalanced atrial targets. The adaptive attention cascade mainly models the inclusion relationship of the two unbalanced atrial targets, where the estimated LA acts as the attention map to adaptively focus on the small atrial scars roughly. Then, an adversarial regularization is applied to the segmentation tasks of the unbalanced atrial targets for making a consistent optimization. It mainly forces the estimated joint distribution of LA and atrial scars to match the real ones. We evaluated the performance of our JAS-GAN on a 3D LGE CMR dataset with 192 scans. Compared with the state-of-the-art methods, our proposed approach yielded better segmentation performance (Average Dice Similarity Coefficient (DSC) values of 0.946 and 0.821 for LA and atrial scars, respectively), which indicated the effectiveness of our proposed approach for segmenting unbalanced atrial targets.
Collapse
|
21
|
Malik YS, Tamoor M, Naseer A, Wali A, Khan A. Applying an adaptive Otsu-based initialization algorithm to optimize active contour models for skin lesion segmentation. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2022; 30:1169-1184. [PMID: 36093674 DOI: 10.3233/xst-221245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
BACKGROUND Medical image processing has gained much attention in developing computer-aided diagnosis (CAD) of diseases. CAD systems require deep understanding of X-rays, MRIs, CT scans and other medical images. The segmentation of the region of interest (ROI) from those images is one of the most crucial tasks. OBJECTIVE Although active contour model (ACM) is a popular method to segment ROIs in medical images, the final segmentation results highly depend on the initial placement of the contour. In order to overcome this challenge, the objective of this study is to investigate feasibility of developing a fully automated initialization process that can be optimally used in ACM to more effectively segment ROIs. METHODS In this study, a fully automated initialization algorithm namely, an adaptive Otsu-based initialization (AOI) method is proposed. Using this proposed method, an initial contour is produced and further refined by the ACM to produce accurate segmentation. For evaluation of the proposed algorithm, the ISIC-2017 Skin Lesion dataset is used due to its challenging complexities. RESULTS Four different supervised performance evaluation metrics are employed to measure the accuracy and robustness of the proposed algorithm. Using this AOI algorithm, the ACM significantly (p≤0.05) outperforms Otsu thresholding method with 0.88 Dice Score Coefficients (DSC) and 0.79 Jaccard Index (JI) and computational complexity of 0(mn). CONCLUSIONS After comparing proposed method with other state-of-the-art methods, our study demonstrates that the proposed methods is superior to other skin lesion segmentation methods, and it requires no training time, which also makes the new method more efficient than other deep learning and machine learning methods.
Collapse
Affiliation(s)
- Yushaa Shafqat Malik
- Department of Computer Science, Forman Christian College University, Lahore, Pakistan
| | - Maria Tamoor
- Department of Computer Science, Forman Christian College University, Lahore, Pakistan
| | - Asma Naseer
- Department of Computer Science, National University of Computer and Emerging Science, Lahore, Pakistan
| | - Aamir Wali
- Department of Computer Science, National University of Computer and Emerging Science, Lahore, Pakistan
| | - Ayesha Khan
- Department of Computer Science, Forman Christian College University, Lahore, Pakistan
| |
Collapse
|
22
|
Peters DC, Lamy J, Sinusas AJ, Baldassarre LA. Left atrial evaluation by cardiovascular magnetic resonance: sensitive and unique biomarkers. Eur Heart J Cardiovasc Imaging 2021; 23:14-30. [PMID: 34718484 DOI: 10.1093/ehjci/jeab221] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 10/12/2021] [Indexed: 12/12/2022] Open
Abstract
Left atrial (LA) imaging is still not routinely used for diagnosis and risk stratification, although recent studies have emphasized its importance as an imaging biomarker. Cardiovascular magnetic resonance is able to evaluate LA structure and function, metrics that serve as early indicators of disease, and provide prognostic information, e.g. regarding diastolic dysfunction, and atrial fibrillation (AF). MR angiography defines atrial anatomy, useful for planning ablation procedures, and also for characterizing atrial shapes and sizes that might predict cardiovascular events, e.g. stroke. Long-axis cine images can be evaluated to define minimum, maximum, and pre-atrial contraction LA volumes, and ejection fractions (EFs). More modern feature tracking of these cine images provides longitudinal LA strain through the cardiac cycle, and strain rates. Strain may be a more sensitive marker than EF and can predict post-operative AF, AF recurrence after ablation, outcomes in hypertrophic cardiomyopathy, stratification of diastolic dysfunction, and strain correlates with atrial fibrosis. Using high-resolution late gadolinium enhancement (LGE), the extent of fibrosis in the LA can be estimated and post-ablation scar can be evaluated. The LA LGE method is widely available, its reproducibility is good, and validations with voltage-mapping exist, although further scan-rescan studies are needed, and consensus regarding atrial segmentation is lacking. Using LGE, scar patterns after ablation in AF subjects can be reproducibly defined. Evaluation of 'pre-existent' atrial fibrosis may have roles in predicting AF recurrence after ablation, predicting new-onset AF and diastolic dysfunction in patients without AF. LA imaging biomarkers are ready to enter into diagnostic clinical practice.
Collapse
Affiliation(s)
- Dana C Peters
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Jérôme Lamy
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Albert J Sinusas
- Department of Cardiology, Yale School of Medicine, New Haven, CT, USA
| | | |
Collapse
|
23
|
Decuyper M, Maebe J, Van Holen R, Vandenberghe S. Artificial intelligence with deep learning in nuclear medicine and radiology. EJNMMI Phys 2021; 8:81. [PMID: 34897550 PMCID: PMC8665861 DOI: 10.1186/s40658-021-00426-y] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 11/19/2021] [Indexed: 12/19/2022] Open
Abstract
The use of deep learning in medical imaging has increased rapidly over the past few years, finding applications throughout the entire radiology pipeline, from improved scanner performance to automatic disease detection and diagnosis. These advancements have resulted in a wide variety of deep learning approaches being developed, solving unique challenges for various imaging modalities. This paper provides a review on these developments from a technical point of view, categorizing the different methodologies and summarizing their implementation. We provide an introduction to the design of neural networks and their training procedure, after which we take an extended look at their uses in medical imaging. We cover the different sections of the radiology pipeline, highlighting some influential works and discussing the merits and limitations of deep learning approaches compared to other traditional methods. As such, this review is intended to provide a broad yet concise overview for the interested reader, facilitating adoption and interdisciplinary research of deep learning in the field of medical imaging.
Collapse
Affiliation(s)
- Milan Decuyper
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Jens Maebe
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Roel Van Holen
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Stefaan Vandenberghe
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| |
Collapse
|
24
|
Convolutional Neural Network-Processed MRI Images in the Diagnosis of Plastic Bronchitis in Children. CONTRAST MEDIA & MOLECULAR IMAGING 2021; 2021:2748830. [PMID: 34621144 PMCID: PMC8457940 DOI: 10.1155/2021/2748830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 08/27/2021] [Accepted: 08/30/2021] [Indexed: 11/24/2022]
Abstract
Objective The study focused on the features of the convolutional neural networks- (CNN-) processed magnetic resonance imaging (MRI) images for plastic bronchitis (PB) in children. Methods 30 PB children were selected as subjects, including 19 boys and 11 girls. They all received the MRI examination for the chest. Then, a CNN-based algorithm was constructed and compared with Active Appearance Model (AAM) algorithm for segmentation effects of MRI images in 30 PB children, factoring into occurring simultaneously than (OST), Dice, and Jaccard coefficient. Results The maximum Dice coefficient of CNN algorithm reached 0.946, while that of active AAM was 0.843, and the Jaccard coefficient of CNN algorithm was also higher (0.894 vs. 0.758, P < 0.05). The MRI images showed pulmonary inflammation in all subjects. Of 30 patients, 14 (46.66%) had complicated pulmonary atelectasis, 9 (30%) had the complicated pleural effusion, 3 (10%) had pneumothorax, 2 (6.67%) had complicated mediastinal emphysema, and 2 (6.67%) had complicated pneumopericardium. Also, of 30 patients, 19 (63.33%) had lung consolidation and atelectasis in a single lung lobe and 11 (36.67%) in both two lung lobes. Conclusion The algorithm based on CNN can significantly improve the segmentation accuracy of MRI images for plastic bronchitis in children. The pleural effusion was a dangerous factor for the occurrence and development of PB.
Collapse
|
25
|
Wu Y, Tang Z, Li B, Firmin D, Yang G. Recent Advances in Fibrosis and Scar Segmentation From Cardiac MRI: A State-of-the-Art Review and Future Perspectives. Front Physiol 2021; 12:709230. [PMID: 34413789 PMCID: PMC8369509 DOI: 10.3389/fphys.2021.709230] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 06/28/2021] [Indexed: 12/03/2022] Open
Abstract
Segmentation of cardiac fibrosis and scars is essential for clinical diagnosis and can provide invaluable guidance for the treatment of cardiac diseases. Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) has been successful in guiding the clinical diagnosis and treatment reliably. For LGE CMR, many methods have demonstrated success in accurately segmenting scarring regions. Co-registration with other non-contrast-agent (non-CA) modalities [e.g., balanced steady-state free precession (bSSFP) cine magnetic resonance imaging (MRI)] can further enhance the efficacy of automated segmentation of cardiac anatomies. Many conventional methods have been proposed to provide automated or semi-automated segmentation of scars. With the development of deep learning in recent years, we can also see more advanced methods that are more efficient in providing more accurate segmentations. This paper conducts a state-of-the-art review of conventional and current state-of-the-art approaches utilizing different modalities for accurate cardiac fibrosis and scar segmentation.
Collapse
Affiliation(s)
- Yinzhe Wu
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom.,Department of Bioengineering, Faculty of Engineering, Imperial College London, London, United Kingdom
| | - Zeyu Tang
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom.,Department of Bioengineering, Faculty of Engineering, Imperial College London, London, United Kingdom
| | - Binghuan Li
- Department of Bioengineering, Faculty of Engineering, Imperial College London, London, United Kingdom
| | - David Firmin
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom.,Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, United Kingdom
| | - Guang Yang
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom.,Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, United Kingdom
| |
Collapse
|
26
|
Bai J, Lu Y, Zhu Y, Wang H, Yin D, Zhang H, Franco D, Zhao J. Understanding PITX2-Dependent Atrial Fibrillation Mechanisms through Computational Models. Int J Mol Sci 2021; 22:7681. [PMID: 34299303 PMCID: PMC8307824 DOI: 10.3390/ijms22147681] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 07/14/2021] [Accepted: 07/16/2021] [Indexed: 01/11/2023] Open
Abstract
Atrial fibrillation (AF) is a common arrhythmia. Better prevention and treatment of AF are needed to reduce AF-associated morbidity and mortality. Several major mechanisms cause AF in patients, including genetic predispositions to AF development. Genome-wide association studies have identified a number of genetic variants in association with AF populations, with the strongest hits clustering on chromosome 4q25, close to the gene for the homeobox transcription PITX2. Because of the inherent complexity of the human heart, experimental and basic research is insufficient for understanding the functional impacts of PITX2 variants on AF. Linking PITX2 properties to ion channels, cells, tissues, atriums and the whole heart, computational models provide a supplementary tool for achieving a quantitative understanding of the functional role of PITX2 in remodelling atrial structure and function to predispose to AF. It is hoped that computational approaches incorporating all we know about PITX2-related structural and electrical remodelling would provide better understanding into its proarrhythmic effects leading to development of improved anti-AF therapies. In the present review, we discuss advances in atrial modelling and focus on the mechanistic links between PITX2 and AF. Challenges in applying models for improving patient health are described, as well as a summary of future perspectives.
Collapse
Affiliation(s)
- Jieyun Bai
- College of Information Science and Technology, Jinan University, Guangzhou 510632, China; (Y.L.); (Y.Z.)
- Auckland Bioengineering Institute, University of Auckland, Auckland 1010, New Zealand
| | - Yaosheng Lu
- College of Information Science and Technology, Jinan University, Guangzhou 510632, China; (Y.L.); (Y.Z.)
| | - Yijie Zhu
- College of Information Science and Technology, Jinan University, Guangzhou 510632, China; (Y.L.); (Y.Z.)
| | - Huijin Wang
- College of Information Science and Technology, Jinan University, Guangzhou 510632, China; (Y.L.); (Y.Z.)
| | - Dechun Yin
- Department of Cardiology, First Affiliated Hospital of Harbin Medical University, Harbin 150000, China;
| | - Henggui Zhang
- Biological Physics Group, School of Physics & Astronomy, The University of Manchester, Manchester M13 9PL, UK;
| | - Diego Franco
- Department of Experimental Biology, University of Jaen, 23071 Jaen, Spain;
| | - Jichao Zhao
- Auckland Bioengineering Institute, University of Auckland, Auckland 1010, New Zealand
| |
Collapse
|
27
|
Craft J, Li Y, Bhatti S, Cao JJ. How to do left atrial late gadolinium enhancement: a review. Radiol Med 2021; 126:1159-1169. [PMID: 34132927 DOI: 10.1007/s11547-021-01383-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 06/04/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND Quantification of left atrial late gadolinium enhancement is a powerful clinical and research tool. Fibrosis burden has been shown to predict the success of pulmonary vein isolation, post-ablation reoccurrence, and major adverse cardiovascular events such as stroke. OVERVIEW The standardized cardiovascular magnetic resonance imaging protocols 2020 update describes the key components of the examination. This review is a more in-depth guide, geared toward building left atrial late gadolinium enhancement imaging from the ground up. The standard protocol consists of the following: localization, pulmonary vein magnetic resonance angiography, cardiac cines, left ventricular, and atrial late gadolinium enhancement. We also review typical segmentation and post-processing techniques, as well as discuss pitfalls, limitations, and potential future innovations in this area. CONCLUSIONS With sufficient experience and optimized protocols, left atrial late gadolinium enhancement imaging is a strong addition to the cardiac magnetic resonance imaging repertoire.
Collapse
Affiliation(s)
- Jason Craft
- St Francis Hospital, Dematteis Research Center, 101 Northern Blvd, Greenvale, NY, 11548, USA.
| | - Yulee Li
- St Francis Hospital, Dematteis Research Center, 101 Northern Blvd, Greenvale, NY, 11548, USA
| | - Salman Bhatti
- The Ohio State University Wexner Medical Center, 410 W 10th Ave, Columbus, OH, 43210, USA
| | - Jie Jane Cao
- St Francis Hospital, Dematteis Research Center, 101 Northern Blvd, Greenvale, NY, 11548, USA
| |
Collapse
|
28
|
Liu X, Gao K, Liu B, Pan C, Liang K, Yan L, Ma J, He F, Zhang S, Pan S, Yu Y. Advances in Deep Learning-Based Medical Image Analysis. HEALTH DATA SCIENCE 2021; 2021:8786793. [PMID: 38487506 PMCID: PMC10880179 DOI: 10.34133/2021/8786793] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 03/04/2021] [Indexed: 03/17/2024]
Abstract
Importance. With the booming growth of artificial intelligence (AI), especially the recent advancements of deep learning, utilizing advanced deep learning-based methods for medical image analysis has become an active research area both in medical industry and academia. This paper reviewed the recent progress of deep learning research in medical image analysis and clinical applications. It also discussed the existing problems in the field and provided possible solutions and future directions.Highlights. This paper reviewed the advancement of convolutional neural network-based techniques in clinical applications. More specifically, state-of-the-art clinical applications include four major human body systems: the nervous system, the cardiovascular system, the digestive system, and the skeletal system. Overall, according to the best available evidence, deep learning models performed well in medical image analysis, but what cannot be ignored are the algorithms derived from small-scale medical datasets impeding the clinical applicability. Future direction could include federated learning, benchmark dataset collection, and utilizing domain subject knowledge as priors.Conclusion. Recent advanced deep learning technologies have achieved great success in medical image analysis with high accuracy, efficiency, stability, and scalability. Technological advancements that can alleviate the high demands on high-quality large-scale datasets could be one of the future developments in this area.
Collapse
Affiliation(s)
| | | | - Bo Liu
- DeepWise AI Lab, BeijingChina
| | | | | | | | | | | | | | - Siyuan Pan
- Shanghai Jiaotong University, Shanghai, China
| | - Yizhou Yu
- DeepWise AI Lab, BeijingChina
- The University of Hong Kong, Hong Kong
| |
Collapse
|
29
|
Olier I, Ortega-Martorell S, Pieroni M, Lip GYH. How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management. Cardiovasc Res 2021; 117:1700-1717. [PMID: 33982064 PMCID: PMC8477792 DOI: 10.1093/cvr/cvab169] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 05/11/2021] [Indexed: 02/01/2023] Open
Abstract
There has been an exponential growth of artificial intelligence (AI) and machine learning (ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has been mainly driven by the confluence of two factors: the advances in deep neural networks (DeepNNs) and the availability of large, open access databases. It is observed that most of the attention has centred on applying ML for dvsetecting AF, particularly using electrocardiograms (ECGs) as the main data modality. Nearly a third of them used DeepNNs to minimize or eliminate the need for transforming the ECGs to extract features prior to ML modelling; however, we did not observe a significant advantage in following this approach. We also found a fraction of studies using other data modalities, and others centred in aims, such as risk prediction, AF management, and others. From the clinical perspective, AI/ML can help expand the utility of AF detection and risk prediction, especially for patients with additional comorbidities. The use of AI/ML for detection and risk prediction into applications and smart mobile health (mHealth) technology would enable ‘real time’ dynamic assessments. AI/ML could also adapt to treatment changes over time, as well as incident risk factors. Incorporation of a dynamic AI/ML model into mHealth technology would facilitate ‘real time’ assessment of stroke risk, facilitating mitigation of modifiable risk factors (e.g. blood pressure control). Overall, this would lead to an improvement in clinical care for patients with AF.
Collapse
Affiliation(s)
- Ivan Olier
- School of Computer Science and Mathematics, Liverpool John Moores University, 3 Byrom Street, Liverpool L3 3AF, UK.,Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK
| | - Sandra Ortega-Martorell
- School of Computer Science and Mathematics, Liverpool John Moores University, 3 Byrom Street, Liverpool L3 3AF, UK.,Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK
| | - Mark Pieroni
- School of Computer Science and Mathematics, Liverpool John Moores University, 3 Byrom Street, Liverpool L3 3AF, UK.,Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK
| | - Gregory Y H Lip
- Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK.,Liverpool Heart and Chest Hospital, Liverpool, UK
| |
Collapse
|
30
|
Statistical shape analysis of the left atrial appendage predicts stroke in atrial fibrillation. Int J Cardiovasc Imaging 2021; 37:2521-2527. [PMID: 33956285 DOI: 10.1007/s10554-021-02262-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 04/27/2021] [Indexed: 10/21/2022]
Abstract
The shape of the left atrium (LA) and left atrial appendage (LAA) have been shown to predict stroke in patients with atrial fibrillation (AF). Prior studies rely on qualitative assessment of shape, which limits reproducibility and clinical utility. Statistical shape analysis (SSA) allows for quantitative assessment of shape. We use this method to assess the shape of the LA and LAA and predict stroke in patients with AF. From a database of AF patients who had previously undergone MRI of the LA, we identified 43 patients with AF who subsequently had an ischemic stroke. We also identified a cohort of 201 controls with AF who did not have a stroke after the MRI. We performed SSA of the LA and LAA shape to quantify the shape of these structures. We found three of the candidate LAA shape parameters to be predictive of stroke, while none of the LA shape parameters predicted stroke. When the three predictive LAA shape parameters were added to a logistic regression model that included the CHA2DS2-VASc score, the area under the ROC curve increased from 0.640 to 0.778 (p = .003). The shape of the LA and LAA can be assessed quantitatively using SSA. LAA shape predicts stroke in AF patients, while LA shape does not. Additionally, LAA shape predicts stroke independent of CHA2DS2-VASc score. SSA for assessment of LAA shape may improve stroke risk stratification and clinical decision making for AF patients.
Collapse
|
31
|
Zhou SK, Greenspan H, Davatzikos C, Duncan JS, van Ginneken B, Madabhushi A, Prince JL, Rueckert D, Summers RM. A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2021; 109:820-838. [PMID: 37786449 PMCID: PMC10544772 DOI: 10.1109/jproc.2021.3054390] [Citation(s) in RCA: 176] [Impact Index Per Article: 58.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propelling us into the so-called artificial intelligence (AI) era. It is known that the success of AI is mostly attributed to the availability of big data with annotations for a single task and the advances in high performance computing. However, medical imaging presents unique challenges that confront deep learning approaches. In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues. We cover the topics of network architecture, sparse and noisy labels, federating learning, interpretability, uncertainty quantification, etc. Then, we present several case studies that are commonly found in clinical practice, including digital pathology and chest, brain, cardiovascular, and abdominal imaging. Rather than presenting an exhaustive literature survey, we instead describe some prominent research highlights related to these case study applications. We conclude with a discussion and presentation of promising future directions.
Collapse
Affiliation(s)
- S Kevin Zhou
- School of Biomedical Engineering, University of Science and Technology of China and Institute of Computing Technology, Chinese Academy of Sciences
| | - Hayit Greenspan
- Biomedical Engineering Department, Tel-Aviv University, Israel
| | - Christos Davatzikos
- Radiology Department and Electrical and Systems Engineering Department, University of Pennsylvania, USA
| | - James S Duncan
- Departments of Biomedical Engineering and Radiology & Biomedical Imaging, Yale University
| | | | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University and Louis Stokes Cleveland Veterans Administration Medical Center, USA
| | - Jerry L Prince
- Electrical and Computer Engineering Department, Johns Hopkins University, USA
| | - Daniel Rueckert
- Klinikum rechts der Isar, TU Munich, Germany and Department of Computing, Imperial College, UK
| | | |
Collapse
|
32
|
Müller A, Mertens SM, Göstemeyer G, Krois J, Schwendicke F. Barriers and Enablers for Artificial Intelligence in Dental Diagnostics: A Qualitative Study. J Clin Med 2021; 10:1612. [PMID: 33920189 PMCID: PMC8069285 DOI: 10.3390/jcm10081612] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 04/06/2021] [Accepted: 04/08/2021] [Indexed: 12/22/2022] Open
Abstract
The present study aimed to identify barriers and enablers for the implementation of artificial intelligence (AI) in dental, specifically radiographic, diagnostics. Semi-structured phone interviews with dentists and patients were conducted between the end of May and the end of June 2020 (convenience/snowball sampling). A questionnaire developed along the Theoretical Domains Framework (TDF) and the Capabilities, Opportunities and Motivations influencing Behaviors model (COM-B) was used to guide interviews. Mayring's content analysis was employed to point out barriers and enablers. We identified 36 barriers, conflicting themes or enablers, covering nine of the fourteen domains of the TDF and all three determinants of behavior (COM). Both stakeholders emphasized chances and hopes for AI. A range of enablers for implementing AI in dental diagnostics were identified (e.g., the chance for higher diagnostic accuracy, a reduced workload, more comprehensive reporting and better patient-provider communication). Barriers related to reliance on AI and responsibility for medical decisions, as well as the explainability of AI and the related option to de-bug AI applications, emerged. Decision-makers and industry may want to consider these aspects to foster implementation of AI in dentistry.
Collapse
Affiliation(s)
- Anne Müller
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, Germany; (A.M.); (J.K.)
| | - Sarah Marie Mertens
- Department of Operative and Preventive Dentistry, Charité-Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, Germany; (S.M.M.); (G.G.)
| | - Gerd Göstemeyer
- Department of Operative and Preventive Dentistry, Charité-Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, Germany; (S.M.M.); (G.G.)
| | - Joachim Krois
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, Germany; (A.M.); (J.K.)
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, Germany; (A.M.); (J.K.)
| |
Collapse
|
33
|
You Y, Viktorovich LA, Qiu J, Nikolaevich KA, Vladimirovich BY. Cardiac magnetic resonance image diagnosis of hypertrophic obstructive cardiomyopathy based on a double-branch neural network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 200:105889. [PMID: 33293182 DOI: 10.1016/j.cmpb.2020.105889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 11/24/2020] [Indexed: 06/12/2023]
Abstract
OBJECTIVE Cardiac magnetic resonance (CMR) imaging is a well-established technique for diagnosis of hypertrophic obstructive cardiomyopathy (HOCM) and evaluation of cardiac function, but the process is complicated and time consuming. Therefore, this paper proposes a cardiomyopathy recognition algorithm using a multi-task learning mechanism and a double-branch deep learning neural network. METHOD We implemented a double-branch neural network CMR-based HOCM recognition algorithm. Compared with the traditional classification algorithms such as the ResNet, DenseNet network, contrast the accuracy of network classification of cardiomyopathy is higher by 10.11%. RESULT The loss curve of the algorithm basically converges in 100 rounds, and the convergence speed of the algorithm is twice that of the traditional algorithm. The accuracy of this algorithm to classify cardiomyopathy is 96.79%, and the sensitivity is 95.24%, which is 10.11% higher than the conventional algorithm. CONCLUSION The CMR imaging automatic recognition algorithm for HOCM capture static morphological and motion characteristics of the heart, and comprehensively enhances recognition accuracy when the sample size is limited.
Collapse
Affiliation(s)
- Yuanbing You
- Department of Cardiovascular Surgery, B.V.Petrovsky Russian Research Center of Surgery, I.M.Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Lysenko Andrey Viktorovich
- Department of Cardiovascular Surgery, B.V.Petrovsky Russian Research Center of Surgery, I.M.Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Jiawei Qiu
- Department of Cardiovascular Surgery, Fuwai Hospital, Chinese Academy of Medical Sciences (CAMS), Beijing,100000, China
| | - Kosenkov Alexander Nikolaevich
- Department of Cardiovascular Surgery and Invasive Cardiology, B.V.Petrovsky Russian Research Center of Surgery, I.M.Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Belov Yuri Vladimirovich
- Department of Cardiovascular Surgery, B.V.Petrovsky Russian Research Center of Surgery, I.M.Sechenov First Moscow State Medical University, Moscow, 119991, Russia.
| |
Collapse
|
34
|
Xiong Z, Xia Q, Hu Z, Huang N, Bian C, Zheng Y, Vesal S, Ravikumar N, Maier A, Yang X, Heng PA, Ni D, Li C, Tong Q, Si W, Puybareau E, Khoudli Y, Géraud T, Chen C, Bai W, Rueckert D, Xu L, Zhuang X, Luo X, Jia S, Sermesant M, Liu Y, Wang K, Borra D, Masci A, Corsi C, de Vente C, Veta M, Karim R, Preetha CJ, Engelhardt S, Qiao M, Wang Y, Tao Q, Nuñez-Garcia M, Camara O, Savioli N, Lamata P, Zhao J. A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging. Med Image Anal 2021; 67:101832. [PMID: 33166776 DOI: 10.1016/j.media.2020.101832] [Citation(s) in RCA: 80] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 09/21/2020] [Accepted: 09/23/2020] [Indexed: 12/29/2022]
Abstract
Segmentation of medical images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) used for visualizing diseased atrial structures, is a crucial first step for ablation treatment of atrial fibrillation. However, direct segmentation of LGE-MRIs is challenging due to the varying intensities caused by contrast agents. Since most clinical studies have relied on manual, labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning approaches. To address this, we organized the 2018 Left Atrium Segmentation Challenge using 154 3D LGE-MRIs, currently the world's largest atrial LGE-MRI dataset, and associated labels of the left atrium segmented by three medical experts, ultimately attracting the participation of 27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis, offering an overall picture of the major design choices of convolutional neural networks (CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show that the top method achieved a Dice score of 93.2% and a mean surface to surface distance of 0.7 mm, significantly outperforming prior state-of-the-art. Particularly, our analysis demonstrated that double sequentially used CNNs, in which a first CNN is used for automatic region-of-interest localization and a subsequent CNN is used for refined regional segmentation, achieved superior results than traditional methods and machine learning approaches containing single CNNs. This large-scale benchmarking study makes a significant step towards much-improved segmentation methods for atrial LGE-MRIs, and will serve as an important benchmark for evaluating and comparing the future works in the field. Furthermore, the findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging community.
Collapse
Affiliation(s)
- Zhaohan Xiong
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Qing Xia
- State Key Lab of Virtual Reality Technology and Systems, Beihang University, Beijing, China
| | - Zhiqiang Hu
- School of Electronics Engineering and Computer Science, Peking University, Beijing, China
| | | | - Cheng Bian
- Tencent Jarvis Laboratory, Shenzhen, China
| | | | - Sulaiman Vesal
- Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Nishant Ravikumar
- Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Andreas Maier
- Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Xin Yang
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong
| | - Pheng-Ann Heng
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong
| | - Dong Ni
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Caizi Li
- School of Computer Science, Wuhan University, Wuhan, China
| | - Qianqian Tong
- School of Computer Science, Wuhan University, Wuhan, China
| | - Weixin Si
- Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | | | - Younes Khoudli
- EPITA Research and Development Laboratory, Paris, France
| | - Thierry Géraud
- EPITA Research and Development Laboratory, Paris, France
| | - Chen Chen
- Department of Computing, Imperial College London, London, United Kingdom
| | - Wenjia Bai
- Department of Computing, Imperial College London, London, United Kingdom
| | - Daniel Rueckert
- Department of Computing, Imperial College London, London, United Kingdom
| | - Lingchao Xu
- School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Xiahai Zhuang
- School of Data Science, Fudan University, Shanghai, China
| | - Xinzhe Luo
- School of Data Science, Fudan University, Shanghai, China
| | - Shuman Jia
- Inria, Université Côte d'Azur, Epione team, Sophia Antipolis, France
| | - Maxime Sermesant
- Inria, Université Côte d'Azur, Epione team, Sophia Antipolis, France
| | - Yashu Liu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Kuanquan Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Davide Borra
- Department of Electric, Electronic and Information Engineering, University of Bologna, Cesena, Italy
| | - Alessandro Masci
- Department of Electric, Electronic and Information Engineering, University of Bologna, Cesena, Italy
| | - Cristiana Corsi
- Department of Electric, Electronic and Information Engineering, University of Bologna, Cesena, Italy
| | - Coen de Vente
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Mitko Veta
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Rashed Karim
- School of Biomedical Engineering & Imaging Sciences, Kings College London, London, United Kingdom
| | | | - Sandy Engelhardt
- Department of Internal Medicine III, Heidelberg University Hospital, Heidelberg, Germany
| | - Menyun Qiao
- Biomedical Engineering Center, Fudan University, Shanghai, China
| | - Yuanyuan Wang
- Biomedical Engineering Center, Fudan University, Shanghai, China
| | - Qian Tao
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Marta Nuñez-Garcia
- Physense, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Oscar Camara
- Physense, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Nicolo Savioli
- Department of Bioengineering, Kings College London, London, United Kingdom
| | - Pablo Lamata
- Department of Bioengineering, Kings College London, London, United Kingdom
| | - Jichao Zhao
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.
| |
Collapse
|
35
|
Razeghi O, Sim I, Roney CH, Karim R, Chubb H, Whitaker J, O’Neill L, Mukherjee R, Wright M, O’Neill M, Williams SE, Niederer S. Fully Automatic Atrial Fibrosis Assessment Using a Multilabel Convolutional Neural Network. Circ Cardiovasc Imaging 2020; 13:e011512. [PMID: 33317334 PMCID: PMC7771635 DOI: 10.1161/circimaging.120.011512] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Supplemental Digital Content is available in the text. Background: Pathological atrial fibrosis is a major contributor to sustained atrial fibrillation. Currently, late gadolinium enhancement (LGE) scans provide the only noninvasive estimate of atrial fibrosis. However, widespread adoption of atrial LGE has been hindered partly by nonstandardized image processing techniques, which can be operator and algorithm dependent. Minimal validation and limited access to transparent software platforms have also exacerbated the problem. This study aims to estimate atrial fibrosis from cardiac magnetic resonance scans using a reproducible operator-independent fully automatic open-source end-to-end pipeline. Methods: A multilabel convolutional neural network was designed to accurately delineate atrial structures including the blood pool, pulmonary veins, and mitral valve. The output from the network removed the operator dependent steps in a reproducible pipeline and allowed for automated estimation of atrial fibrosis from LGE-cardiac magnetic resonance scans. The pipeline results were compared against manual fibrosis burdens, calculated using published thresholds: image intensity ratio 0.97, image intensity ratio 1.61, and mean blood pool signal +3.3 SD. Results: We validated our methods on a large 3-dimensional LGE-cardiac magnetic resonance data set from 207 labeled scans. Automatic atrial segmentation achieved a 91% Dice score, compared with the mutual agreement of 85% in Dice seen in the interobserver analysis of operators. Intraclass correlation coefficients of the automatic pipeline with manually generated results were excellent and better than or equal to interobserver correlations for all 3 thresholds: 0.94 versus 0.88, 0.99 versus 0.99, 0.99 versus 0.96 for image intensity ratio 0.97, image intensity ratio 1.61, and +3.3 SD thresholds, respectively. Automatic analysis required 3 minutes per case on a standard workstation. The network and the analysis software are publicly available. Conclusions: Our pipeline provides a fully automatic estimation of fibrosis burden from LGE-cardiac magnetic resonance scans that is comparable to manual analysis. This removes one key source of variability in the measurement of atrial fibrosis.
Collapse
Affiliation(s)
- Orod Razeghi
- Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (O.R., I.S., C.H.R., R.K., H.C., J.W., L.O., R.M., M.O., S.E.W., S.N.)
| | - Iain Sim
- Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (O.R., I.S., C.H.R., R.K., H.C., J.W., L.O., R.M., M.O., S.E.W., S.N.)
| | - Caroline H. Roney
- Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (O.R., I.S., C.H.R., R.K., H.C., J.W., L.O., R.M., M.O., S.E.W., S.N.)
| | - Rashed Karim
- Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (O.R., I.S., C.H.R., R.K., H.C., J.W., L.O., R.M., M.O., S.E.W., S.N.)
| | - Henry Chubb
- Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (O.R., I.S., C.H.R., R.K., H.C., J.W., L.O., R.M., M.O., S.E.W., S.N.)
| | - John Whitaker
- Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (O.R., I.S., C.H.R., R.K., H.C., J.W., L.O., R.M., M.O., S.E.W., S.N.)
| | - Louisa O’Neill
- Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (O.R., I.S., C.H.R., R.K., H.C., J.W., L.O., R.M., M.O., S.E.W., S.N.)
| | - Rahul Mukherjee
- Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (O.R., I.S., C.H.R., R.K., H.C., J.W., L.O., R.M., M.O., S.E.W., S.N.)
| | - Matthew Wright
- Cardiology Department, St. Thomas’ Hospital, London, United Kingdom (M.W., M.O.)
| | - Mark O’Neill
- Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (O.R., I.S., C.H.R., R.K., H.C., J.W., L.O., R.M., M.O., S.E.W., S.N.)
- Cardiology Department, St. Thomas’ Hospital, London, United Kingdom (M.W., M.O.)
| | - Steven E. Williams
- Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (O.R., I.S., C.H.R., R.K., H.C., J.W., L.O., R.M., M.O., S.E.W., S.N.)
| | - Steven Niederer
- Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (O.R., I.S., C.H.R., R.K., H.C., J.W., L.O., R.M., M.O., S.E.W., S.N.)
| |
Collapse
|
36
|
Bifulco SF, Akoum N, Boyle PM. Translational applications of computational modelling for patients with cardiac arrhythmias. Heart 2020; 107:heartjnl-2020-316854. [PMID: 33303478 PMCID: PMC10896425 DOI: 10.1136/heartjnl-2020-316854] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 11/13/2020] [Accepted: 11/19/2020] [Indexed: 11/04/2022] Open
Abstract
Cardiac arrhythmia is associated with high morbidity, and its underlying mechanisms are poorly understood. Computational modelling and simulation approaches have the potential to improve standard-of-care therapy for these disorders, offering deeper understanding of complex disease processes and sophisticated translational tools for planning clinical procedures. This review provides a clinician-friendly summary of recent advancements in computational cardiology. Organ-scale models automatically generated from clinical-grade imaging data are used to custom tailor our understanding of arrhythmia drivers, estimate future arrhythmogenic risk and personalise treatment plans. Recent mechanistic insights derived from atrial and ventricular arrhythmia simulations are highlighted, and the potential avenues to patient care (eg, by revealing new antiarrhythmic drug targets) are covered. Computational approaches geared towards improving outcomes in resynchronisation therapy have used simulations to elucidate optimal patient selection and lead location. Technology to personalise catheter ablation procedures are also covered, specifically preliminary outcomes form early-stage or pilot clinical studies. To conclude, future developments in computational cardiology are discussed, including improving the representation of patient-specific fibre orientations and fibrotic remodelling characterisation and how these might improve understanding of arrhythmia mechanisms and provide transformative tools for patient-specific therapy.
Collapse
Affiliation(s)
- Savannah F Bifulco
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
| | - Nazem Akoum
- Department of Cardiology, University of Washington, Seattle, Washington, USA
| | - Patrick M Boyle
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
- Center for Cardiovascular Biology, University of Washington, Seattle, WA, USA
- Institute for Stem Cell & Regenerative Medicine, University of Washington, Seattle, WA, USA
| |
Collapse
|
37
|
Habijan M, Babin D, Galić I, Leventić H, Romić K, Velicki L, Pižurica A. Overview of the Whole Heart and Heart Chamber Segmentation Methods. Cardiovasc Eng Technol 2020; 11:725-747. [DOI: 10.1007/s13239-020-00494-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 10/06/2020] [Indexed: 12/13/2022]
|
38
|
Uslu F, Varela M, Bharath AA. A Semi-Automatic Method To Segment The Left Atrium in MR Volumes With Varying Slice Numbers. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:1198-1202. [PMID: 33018202 DOI: 10.1109/embc44109.2020.9175749] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Atrial fibrillation (AF) is the most common sustained arrhythmia and is associated with dramatic increases in mortality and morbidity. Atrial cine MR images are increasingly used in the management of this condition, but there are few specific tools to aid in the segmentation of such data. Some characteristics of atrial cine MR (thick slices, variable number of slices in a volume) preclude the direct use of traditional segmentation tools. When combined with scarcity of labelled data and similarity of the intensity and texture of the left atrium (LA) to other cardiac structures, the segmentation of the LA in CINE MRI becomes a difficult task. To deal with these challenges, we propose a semi-automatic method to segment the left atrium (LA) in MR images, which requires an initial user click per volume. The manually given location information is used to generate a chamber location map to roughly locate the LA, which is then used as an input to a deep network with slightly over 0.5 million parameters. A tracking method is introduced to pass the location information across a volume and to remove unwanted structures in segmentation maps. According to the results of our experiments conducted in an in-house MRI dataset, the proposed method outperforms the U-Net [1] with a margin of 20 mm on Hausdorff distance and 0.17 on Dice score, with limited manual interaction.
Collapse
|
39
|
Borra D, Andalò A, Paci M, Fabbri C, Corsi C. A fully automated left atrium segmentation approach from late gadolinium enhanced magnetic resonance imaging based on a convolutional neural network. Quant Imaging Med Surg 2020; 10:1894-1907. [PMID: 33014723 DOI: 10.21037/qims-20-168] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Background Several studies suggest that the evaluation of left atrial (LA) fibrosis is a relevant information for the assessment of the appropriate strategy in catheter ablation in atrial fibrillation (AF). Late gadolinium enhanced (LGE) cardiac magnetic resonance imaging (MRI) is a non-invasive technique, which might be employed for the non-invasive quantification of LA myocardial fibrotic tissue in patients with AF. Nowadays, the analysis of LGE MRI relies on manual tracing of LA boundaries and this procedure is time-consuming and prone to high inter-observer variability given the different degrees of observers' experience, LA wall thickness and data resolution. Therefore, an automated segmentation approach of the atrial cavity for the quantification of scar tissue would be highly desirable. Methods This study focuses on the design of a fully automated LGE MRI segmentation pipeline which includes a convolutional neural network (CNN) based on the successful architecture U-Net. The CNN was trained, validated and tested end-to-end with the data available from the Statistical Atlases and Computational Modelling of the Heart 2018 Atrial Segmentation Challenge (100 cardiac data). Two different approaches were tested: using both stacks of 2-D axial slices and using 3-D data (with the appropriate changes in the baseline architecture). In the latter approach, thanks to the 3-D convolution operator, all the information underlying 3-D data can be exploited. Once the training was completed using 80 cardiac data, a post-processing step was applied on 20 predicted segmentations belonging to the test set. Results By applying the 2-D and 3-D approaches, average Dice coefficient and mean Hausdorff distances were 0.896, 0.914, and 8.98 mm, 8.34 mm, respectively. Volumes of the anatomical LA meshes from the automated analysis were highly correlated with the volumes from ground truth [2-D: r=0.978, y=0.94x+0.07, bias=3.5 ml (5.6%), SD=5.3 mL (8.5%); 3-D: r=0.982, y=0.92x+2.9, bias=2.1 mL (3.5%), SD=5.2 mL (8.4%)]. Conclusions These results suggest the proposed approach is feasible and provides accurate results. Despite the increase of the number of trainable parameters, the proposed 3-D CNN learns better features leading to higher performance, feasible for a real clinical application.
Collapse
Affiliation(s)
- Davide Borra
- Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi" (DEI), University of Bologna, Bologna, Italy
| | - Alice Andalò
- Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi" (DEI), University of Bologna, Bologna, Italy
| | - Michelangelo Paci
- BioMediTech, Faculty of Medicine and Health Technology, Tampere University, FI-33520 Tampere, Finland
| | - Claudio Fabbri
- Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi" (DEI), University of Bologna, Bologna, Italy
| | - Cristiana Corsi
- Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi" (DEI), University of Bologna, Bologna, Italy
| |
Collapse
|
40
|
Feeny AK, Chung MK, Madabhushi A, Attia ZI, Cikes M, Firouznia M, Friedman PA, Kalscheur MM, Kapa S, Narayan SM, Noseworthy PA, Passman RS, Perez MV, Peters NS, Piccini JP, Tarakji KG, Thomas SA, Trayanova NA, Turakhia MP, Wang PJ. Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology. Circ Arrhythm Electrophysiol 2020; 13:e007952. [PMID: 32628863 PMCID: PMC7808396 DOI: 10.1161/circep.119.007952] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Artificial intelligence (AI) and machine learning (ML) in medicine are currently areas of intense exploration, showing potential to automate human tasks and even perform tasks beyond human capabilities. Literacy and understanding of AI/ML methods are becoming increasingly important to researchers and clinicians. The first objective of this review is to provide the novice reader with literacy of AI/ML methods and provide a foundation for how one might conduct an ML study. We provide a technical overview of some of the most commonly used terms, techniques, and challenges in AI/ML studies, with reference to recent studies in cardiac electrophysiology to illustrate key points. The second objective of this review is to use examples from recent literature to discuss how AI and ML are changing clinical practice and research in cardiac electrophysiology, with emphasis on disease detection and diagnosis, prediction of patient outcomes, and novel characterization of disease. The final objective is to highlight important considerations and challenges for appropriate validation, adoption, and deployment of AI technologies into clinical practice.
Collapse
Affiliation(s)
- Albert K Feeny
- Cleveland Clinic Lerner College of Medicine (A.K.F., M.K.C.), Case Western Reserve University, OH
| | - Mina K Chung
- Cleveland Clinic Lerner College of Medicine (A.K.F., M.K.C.), Case Western Reserve University, OH
- Department of Cardiovascular Medicine, Cleveland Clinic, OH (M.K.C., K.G.T., S.A.T.)
| | - Anant Madabhushi
- Department of Biomedical Engineering (A.M., M.F.), Case Western Reserve University, OH
- Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, OH (A.M.)
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN (Z.I.A., P.A.F., S.K., P.A.N., )
| | - Maja Cikes
- Department of Cardiovascular Diseases, University of Zagreb School of Medicine & University Hospital Center Zagreb, Croatia (M.C.)
| | - Marjan Firouznia
- Department of Biomedical Engineering (A.M., M.F.), Case Western Reserve University, OH
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN (Z.I.A., P.A.F., S.K., P.A.N., )
| | - Matthew M Kalscheur
- Division of Cardiovascular Medicine, Department of Medicine, School of Medicine & Public Health, University of Wisconsin (M.M.K.)
- William S. Middleton Veterans Hospital, Madison, WI (M.M.K.)
| | - Suraj Kapa
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN (Z.I.A., P.A.F., S.K., P.A.N., )
| | - Sanjiv M Narayan
- Division of Cardiovascular Medicine, Stanford University, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
- Veterans Affairs Palo Alto Health Care System, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
| | - Peter A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN (Z.I.A., P.A.F., S.K., P.A.N., )
| | - Rod S Passman
- Division of Cardiology, Northwestern University, Feinberg School of Medicine, Chicago, IL (R.S.P.)
| | - Marco V Perez
- Division of Cardiovascular Medicine, Stanford University, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
- Veterans Affairs Palo Alto Health Care System, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
| | - Nicholas S Peters
- National Heart Lung Institute & Centre for Cardiac Engineering, Imperial College London, United Kingdom (N.S.P.)
| | - Jonathan P Piccini
- Duke Clinical Research Institute, Duke University Medical Center, Durham, NC (J.P.P.)
| | - Khaldoun G Tarakji
- Department of Cardiovascular Medicine, Cleveland Clinic, OH (M.K.C., K.G.T., S.A.T.)
| | - Suma A Thomas
- Department of Cardiovascular Medicine, Cleveland Clinic, OH (M.K.C., K.G.T., S.A.T.)
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD (N.A.T.)
| | - Mintu P Turakhia
- Division of Cardiovascular Medicine, Stanford University, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
- Veterans Affairs Palo Alto Health Care System, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
- Center for Digital Health, Stanford University School of Medicine, CA (M.P.T.)
| | - Paul J Wang
- Division of Cardiovascular Medicine, Stanford University, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
- Veterans Affairs Palo Alto Health Care System, CA (S.M.N., M.V.P., M.P.T., P.J.W.)
| |
Collapse
|
41
|
Fully Automated 3D Cardiac MRI Localisation and Segmentation Using Deep Neural Networks. J Imaging 2020; 6:jimaging6070065. [PMID: 34460658 PMCID: PMC8321054 DOI: 10.3390/jimaging6070065] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 06/30/2020] [Accepted: 07/01/2020] [Indexed: 12/24/2022] Open
Abstract
Cardiac magnetic resonance (CMR) imaging is used widely for morphological assessment and diagnosis of various cardiovascular diseases. Deep learning approaches based on 3D fully convolutional networks (FCNs), have improved state-of-the-art segmentation performance in CMR images. However, previous methods have employed several pre-processing steps and have focused primarily on segmenting low-resolutions images. A crucial step in any automatic segmentation approach is to first localize the cardiac structure of interest within the MRI volume, to reduce false positives and computational complexity. In this paper, we propose two strategies for localizing and segmenting the heart ventricles and myocardium, termed multi-stage and end-to-end, using a 3D convolutional neural network. Our method consists of an encoder–decoder network that is first trained to predict a coarse localized density map of the target structure at a low resolution. Subsequently, a second similar network employs this coarse density map to crop the image at a higher resolution, and consequently, segment the target structure. For the latter, the same two-stage architecture is trained end-to-end. The 3D U-Net with some architectural changes (referred to as 3D DR-UNet) was used as the base architecture in this framework for both the multi-stage and end-to-end strategies. Moreover, we investigate whether the incorporation of coarse features improves the segmentation. We evaluate the two proposed segmentation strategies on two cardiac MRI datasets, namely, the Automatic Cardiac Segmentation Challenge (ACDC) STACOM 2017, and Left Atrium Segmentation Challenge (LASC) STACOM 2018. Extensive experiments and comparisons with other state-of-the-art methods indicate that the proposed multi-stage framework consistently outperforms the rest in terms of several segmentation metrics. The experimental results highlight the robustness of the proposed approach, and its ability to generate accurate high-resolution segmentations, despite the presence of varying degrees of pathology-induced changes to cardiac morphology and image appearance, low contrast, and noise in the CMR volumes.
Collapse
|
42
|
Zhang L, Wang X, Yang D, Sanford T, Harmon S, Turkbey B, Wood BJ, Roth H, Myronenko A, Xu D, Xu Z. Generalizing Deep Learning for Medical Image Segmentation to Unseen Domains via Deep Stacked Transformation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:2531-2540. [PMID: 32070947 PMCID: PMC7393676 DOI: 10.1109/tmi.2020.2973595] [Citation(s) in RCA: 116] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Recent advances in deep learning for medical image segmentation demonstrate expert-level accuracy. However, application of these models in clinically realistic environments can result in poor generalization and decreased accuracy, mainly due to the domain shift across different hospitals, scanner vendors, imaging protocols, and patient populations etc. Common transfer learning and domain adaptation techniques are proposed to address this bottleneck. However, these solutions require data (and annotations) from the target domain to retrain the model, and is therefore restrictive in practice for widespread model deployment. Ideally, we wish to have a trained (locked) model that can work uniformly well across unseen domains without further training. In this paper, we propose a deep stacked transformation approach for domain generalization. Specifically, a series of n stacked transformations are applied to each image during network training. The underlying assumption is that the "expected" domain shift for a specific medical imaging modality could be simulated by applying extensive data augmentation on a single source domain, and consequently, a deep model trained on the augmented "big" data (BigAug) could generalize well on unseen domains. We exploit four surprisingly effective, but previously understudied, image-based characteristics for data augmentation to overcome the domain generalization problem. We train and evaluate the BigAug model (with n=9 transformations) on three different 3D segmentation tasks (prostate gland, left atrial, left ventricle) covering two medical imaging modalities (MRI and ultrasound) involving eight publicly available challenge datasets. The results show that when training on relatively small dataset (n = 10~32 volumes, depending on the size of the available datasets) from a single source domain: (i) BigAug models degrade an average of 11%(Dice score change) from source to unseen domain, substantially better than conventional augmentation (degrading 39%) and CycleGAN-based domain adaptation method (degrading 25%), (ii) BigAug is better than "shallower" stacked transforms (i.e. those with fewer transforms) on unseen domains and demonstrates modest improvement to conventional augmentation on the source domain, (iii) after training with BigAug on one source domain, performance on an unseen domain is similar to training a model from scratch on that domain when using the same number of training samples. When training on large datasets (n = 465 volumes) with BigAug, (iv) application to unseen domains reaches the performance of state-of-the-art fully supervised models that are trained and tested on their source domains. These findings establish a strong benchmark for the study of domain generalization in medical imaging, and can be generalized to the design of highly robust deep segmentation models for clinical deployment.
Collapse
|
43
|
Yang G, Chen J, Gao Z, Li S, Ni H, Angelini E, Wong T, Mohiaddin R, Nyktari E, Wage R, Xu L, Zhang Y, Du X, Zhang H, Firmin D, Keegan J. Simultaneous left atrium anatomy and scar segmentations via deep learning in multiview information with attention. FUTURE GENERATIONS COMPUTER SYSTEMS : FGCS 2020; 107:215-228. [PMID: 32494091 PMCID: PMC7134530 DOI: 10.1016/j.future.2020.02.005] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Revised: 01/03/2020] [Accepted: 02/02/2020] [Indexed: 05/20/2023]
Abstract
Three-dimensional late gadolinium enhanced (LGE) cardiac MR (CMR) of left atrial scar in patients with atrial fibrillation (AF) has recently emerged as a promising technique to stratify patients, to guide ablation therapy and to predict treatment success. This requires a segmentation of the high intensity scar tissue and also a segmentation of the left atrium (LA) anatomy, the latter usually being derived from a separate bright-blood acquisition. Performing both segmentations automatically from a single 3D LGE CMR acquisition would eliminate the need for an additional acquisition and avoid subsequent registration issues. In this paper, we propose a joint segmentation method based on multiview two-task (MVTT) recursive attention model working directly on 3D LGE CMR images to segment the LA (and proximal pulmonary veins) and to delineate the scar on the same dataset. Using our MVTT recursive attention model, both the LA anatomy and scar can be segmented accurately (mean Dice score of 93% for the LA anatomy and 87% for the scar segmentations) and efficiently ( ∼ 0.27 s to simultaneously segment the LA anatomy and scars directly from the 3D LGE CMR dataset with 60-68 2D slices). Compared to conventional unsupervised learning and other state-of-the-art deep learning based methods, the proposed MVTT model achieved excellent results, leading to an automatic generation of a patient-specific anatomical model combined with scar segmentation for patients in AF.
Collapse
Affiliation(s)
- Guang Yang
- Cardiovascular Research Centre, Royal Brompton Hospital, SW3 6NP, London, UK
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Corresponding author at: Cardiovascular Research Centre, Royal Brompton Hospital, SW3 6NP, London, UK.
| | - Jun Chen
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, 510006, China
| | - Zhifan Gao
- Department of Medical Imaging, Western University, London, ON, N6A 3K7, Canada
| | - Shuo Li
- Department of Medical Imaging, Western University, London, ON, N6A 3K7, Canada
| | - Hao Ni
- Department of Mathematics, University College London, London, WC1E 6BT, UK
- Alan Turing Institute, London, NW1 2DB, UK
| | - Elsa Angelini
- NIHR Imperial Biomedical Research Centre, ITMAT Data Science Group, Imperial College London, London, SW7 2AZ, UK
| | - Tom Wong
- Cardiovascular Research Centre, Royal Brompton Hospital, SW3 6NP, London, UK
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
| | - Raad Mohiaddin
- Cardiovascular Research Centre, Royal Brompton Hospital, SW3 6NP, London, UK
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
| | - Eva Nyktari
- Cardiovascular Research Centre, Royal Brompton Hospital, SW3 6NP, London, UK
| | - Ricardo Wage
- Cardiovascular Research Centre, Royal Brompton Hospital, SW3 6NP, London, UK
| | - Lei Xu
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | | | | | - Heye Zhang
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, 510006, China
- Corresponding author.
| | - David Firmin
- Cardiovascular Research Centre, Royal Brompton Hospital, SW3 6NP, London, UK
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
| | - Jennifer Keegan
- Cardiovascular Research Centre, Royal Brompton Hospital, SW3 6NP, London, UK
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
| |
Collapse
|
44
|
Jamart K, Xiong Z, Maso Talou GD, Stiles MK, Zhao J. Mini Review: Deep Learning for Atrial Segmentation From Late Gadolinium-Enhanced MRIs. Front Cardiovasc Med 2020; 7:86. [PMID: 32528977 PMCID: PMC7266934 DOI: 10.3389/fcvm.2020.00086] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Accepted: 04/21/2020] [Indexed: 12/12/2022] Open
Abstract
Segmentation and 3D reconstruction of the human atria is of crucial importance for precise diagnosis and treatment of atrial fibrillation, the most common cardiac arrhythmia. However, the current manual segmentation of the atria from medical images is a time-consuming, labor-intensive, and error-prone process. The recent emergence of artificial intelligence, particularly deep learning, provides an alternative solution to the traditional methods that fail to accurately segment atrial structures from clinical images. This has been illustrated during the recent 2018 Atrial Segmentation Challenge for which most of the challengers developed deep learning approaches for atrial segmentation, reaching high accuracy (>90% Dice score). However, as significant discrepancies exist between the approaches developed, many important questions remain unanswered, such as which deep learning architectures and methods to ensure reliability while achieving the best performance. In this paper, we conduct an in-depth review of the current state-of-the-art of deep learning approaches for atrial segmentation from late gadolinium-enhanced MRIs, and provide critical insights for overcoming the main hindrances faced in this task.
Collapse
Affiliation(s)
- Kevin Jamart
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Zhaohan Xiong
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Gonzalo D. Maso Talou
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Martin K. Stiles
- Waikato Clinical School, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Jichao Zhao
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| |
Collapse
|
45
|
Corrado C, Razeghi O, Roney C, Coveney S, Williams S, Sim I, O'Neill M, Wilkinson R, Oakley J, Clayton RH, Niederer S. Quantifying atrial anatomy uncertainty from clinical data and its impact on electro-physiology simulation predictions. Med Image Anal 2020; 61:101626. [PMID: 32000114 DOI: 10.1016/j.media.2019.101626] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 11/05/2019] [Accepted: 12/05/2019] [Indexed: 12/11/2022]
Abstract
Patient-specific computational models of structure and function are increasingly being used to diagnose disease and predict how a patient will respond to therapy. Models of anatomy are often derived after segmentation of clinical images or from mapping systems which are affected by image artefacts, resolution and contrast. Quantifying the impact of uncertain anatomy on model predictions is important, as models are increasingly used in clinical practice where decisions need to be made regardless of image quality. We use a Bayesian probabilistic approach to estimate the anatomy and to quantify the uncertainty about the shape of the left atrium derived from Cardiac Magnetic Resonance images. We show that we can quantify uncertain shape, encode uncertainty about the left atrial shape due to imaging artefacts, and quantify the effect of uncertain shape on simulations of left atrial activation times.
Collapse
Affiliation(s)
- Cesare Corrado
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom.
| | - Orod Razeghi
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Caroline Roney
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Sam Coveney
- Insigneo Institute for in-silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Steven Williams
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Iain Sim
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Mark O'Neill
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Richard Wilkinson
- School of Mathematics and Statistics, University of Sheffield, Sheffield, United Kingdom
| | - Jeremy Oakley
- School of Mathematics and Statistics, University of Sheffield, Sheffield, United Kingdom
| | - Richard H Clayton
- Insigneo Institute for in-silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Steven Niederer
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| |
Collapse
|
46
|
Chen C, Qin C, Qiu H, Tarroni G, Duan J, Bai W, Rueckert D. Deep Learning for Cardiac Image Segmentation: A Review. Front Cardiovasc Med 2020; 7:25. [PMID: 32195270 PMCID: PMC7066212 DOI: 10.3389/fcvm.2020.00025] [Citation(s) in RCA: 308] [Impact Index Per Article: 77.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 02/17/2020] [Indexed: 12/15/2022] Open
Abstract
Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound and major anatomical structures of interest (ventricles, atria, and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research.
Collapse
Affiliation(s)
- Chen Chen
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Chen Qin
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Huaqi Qiu
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Giacomo Tarroni
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
- CitAI Research Centre, Department of Computer Science, City University of London, London, United Kingdom
| | - Jinming Duan
- School of Computer Science, University of Birmingham, Birmingham, United Kingdom
| | - Wenjia Bai
- Data Science Institute, Imperial College London, London, United Kingdom
- Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| |
Collapse
|
47
|
Xiong Z, Nalar A, Jamart K, Stiles MK, Fedorov VV, Zhao J. Fully Automatic 3D Bi-Atria Segmentation from Late Gadolinium-Enhanced MRIs Using Double Convolutional Neural Networks. STATISTICAL ATLASES AND COMPUTATIONAL MODELS OF THE HEART. MULTI-SEQUENCE CMR SEGMENTATION, CRT-EPIGGY AND LV FULL QUANTIFICATION CHALLENGES 2020. [DOI: 10.1007/978-3-030-39074-7_7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
|
48
|
Li L, Wu F, Yang G, Xu L, Wong T, Mohiaddin R, Firmin D, Keegan J, Zhuang X. Atrial scar quantification via multi-scale CNN in the graph-cuts framework. Med Image Anal 2019; 60:101595. [PMID: 31811981 PMCID: PMC6988106 DOI: 10.1016/j.media.2019.101595] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2018] [Revised: 06/05/2019] [Accepted: 10/26/2019] [Indexed: 11/06/2022]
Abstract
Propose a fully automatic method for left atrial scar quantification, with promising performance. Formulate a new framework of scar quantification based on surface projection and graph-cuts framework. Propose the multi-scale learning CNN, combined with the random shift training strategy, to learn and predict the graph potentials, which significantly improves the performance of the proposed method, and enables the full automation of the framework. Provide thorough validation and parameter studies for the proposed techniques using fifty-eight clinical images.
Late gadolinium enhancement magnetic resonance imaging (LGE MRI) appears to be a promising alternative for scar assessment in patients with atrial fibrillation (AF). Automating the quantification and analysis of atrial scars can be challenging due to the low image quality. In this work, we propose a fully automated method based on the graph-cuts framework, where the potentials of the graph are learned on a surface mesh of the left atrium (LA) using a multi-scale convolutional neural network (MS-CNN). For validation, we have included fifty-eight images with manual delineations. MS-CNN, which can efficiently incorporate both the local and global texture information of the images, has been shown to evidently improve the segmentation accuracy of the proposed graph-cuts based method. The segmentation could be further improved when the contribution between the t-link and n-link weights of the graph is balanced. The proposed method achieves a mean accuracy of 0.856 ± 0.033 and mean Dice score of 0.702 ± 0.071 for LA scar quantification. Compared to the conventional methods, which are based on the manual delineation of LA for initialization, our method is fully automatic and has demonstrated significantly better Dice score and accuracy (p < 0.01). The method is promising and can be potentially useful in diagnosis and prognosis of AF.
Collapse
Affiliation(s)
- Lei Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China; School of Data Science, Fudan University, Shanghai, China
| | - Fuping Wu
- School of Data Science, Fudan University, Shanghai, China; Dept of Statistics, School of Management, Fudan University, Shanghai, China
| | - Guang Yang
- National Heart and Lung Institute, Imperial College London, London, UK; Cardiovascular Research Center, Royal Brompton Hospital, London, UK
| | - Lingchao Xu
- School of NAOCE, Shanghai Jiao Tong University, Shanghai, China
| | - Tom Wong
- Cardiovascular Research Center, Royal Brompton Hospital, London, UK
| | - Raad Mohiaddin
- National Heart and Lung Institute, Imperial College London, London, UK; Cardiovascular Research Center, Royal Brompton Hospital, London, UK
| | - David Firmin
- National Heart and Lung Institute, Imperial College London, London, UK; Cardiovascular Research Center, Royal Brompton Hospital, London, UK
| | - Jennifer Keegan
- National Heart and Lung Institute, Imperial College London, London, UK; Cardiovascular Research Center, Royal Brompton Hospital, London, UK
| | - Xiahai Zhuang
- School of Data Science, Fudan University, Shanghai, China; Fudan-Xinzailing Joint Research Center for Big Data, Fudan University, Shanghai, China.
| |
Collapse
|
49
|
Wang Y, Xiong Z, Nalar A, Hansen BJ, Kharche S, Seemann G, Loewe A, Fedorov VV, Zhao J. A robust computational framework for estimating 3D Bi-Atrial chamber wall thickness. Comput Biol Med 2019; 114:103444. [PMID: 31542646 PMCID: PMC6817405 DOI: 10.1016/j.compbiomed.2019.103444] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 08/23/2019] [Accepted: 09/10/2019] [Indexed: 12/14/2022]
Abstract
Atrial fibrillation (AF) is the most prevalent form of cardiac arrhythmia. The atrial wall thickness (AWT) can potentially improve our understanding of the mechanism underlying atrial structure that drives AF and provides important clinical information. However, most existing studies for estimating AWT rely on ruler-based measurements performed on only a few selected locations in 2D or 3D using digital calipers. Only a few studies have developed automatic approaches to estimate the AWT in the left atrium, and there are currently no methods to robustly estimate the AWT of both atrial chambers. Therefore, we have developed a computational pipeline to automatically calculate the 3D AWT across bi-atrial chambers and extensively validated our pipeline on both ex vivo and in vivo human atria data. The atrial geometry was first obtained by segmenting the atrial wall from the MRIs using a novel machine learning approach. The epicardial and endocardial surfaces were then separated using a multi-planar convex hull approach to define boundary conditions, from which, a Laplace equation was solved numerically to automatically separate bi-atrial chambers. To robustly estimate the AWT in each atrial chamber, coupled partial differential equations by coupling the Laplace solution with two surface trajectory functions were formulated and solved. Our pipeline enabled the reconstruction and visualization of the 3D AWT for bi-atrial chambers with a relative error of 8% and outperformed existing algorithms by >7%. Our approach can potentially lead to improved clinical diagnosis, patient stratification, and clinical guidance during ablation treatment for patients with AF.
Collapse
Affiliation(s)
- Yufeng Wang
- Auckland Bioengineering Institute, The University of Auckland, Auckland, 1142, New Zealand
| | - Zhaohan Xiong
- Auckland Bioengineering Institute, The University of Auckland, Auckland, 1142, New Zealand
| | - Aaqel Nalar
- Auckland Bioengineering Institute, The University of Auckland, Auckland, 1142, New Zealand
| | - Brian J Hansen
- Department of Physiology and Cell Biology, The Ohio State University Wexner Medical Center, Columbus, USA
| | - Sanjay Kharche
- Department of Medical Biophysics, Western University, Canada
| | - Gunnar Seemann
- The Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg, Bad Krozingen, Faculty of Medicine, Albert-Ludwigs University, Freiburg, Germany
| | - Axel Loewe
- The Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Vadim V Fedorov
- Department of Physiology and Cell Biology, The Ohio State University Wexner Medical Center, Columbus, USA
| | - Jichao Zhao
- Auckland Bioengineering Institute, The University of Auckland, Auckland, 1142, New Zealand.
| |
Collapse
|
50
|
Abstract
OBJECTIVE. The recent advancement of deep learning techniques has profoundly impacted research on quantitative cardiac MRI analysis. The purpose of this article is to introduce the concept of deep learning, review its current applications on quantitative cardiac MRI, and discuss its limitations and challenges. CONCLUSION. Deep learning has shown state-of-the-art performance on quantitative analysis of multiple cardiac MRI sequences and holds great promise for future use in clinical practice and scientific research.
Collapse
|