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Abouei E, Pan S, Hu M, Kesarwala AH, Qiu RLJ, Zhou J, Roper J, Yang X. Cardiac MRI segmentation using shifted-window multilayer perceptron mixer networks. Phys Med Biol 2024; 69:115048. [PMID: 38744300 DOI: 10.1088/1361-6560/ad4b91] [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: 11/30/2023] [Accepted: 05/14/2024] [Indexed: 05/16/2024]
Abstract
Objectives. In this work, we proposed a deep-learning segmentation algorithm for cardiac magnetic resonance imaging to aid in contouring of the left ventricle, right ventricle, and Myocardium (Myo).Approach.We proposed a shifted window multilayer perceptron (Swin-MLP) mixer network which is built upon a 3D U-shaped symmetric encoder-decoder structure. We evaluated our proposed network using public data from 100 individuals. The network performance was quantitatively evaluated using 3D volume similarity between the ground truth contours and the predictions using Dice score coefficient, sensitivity, and precision as well as 2D surface similarity using Hausdorff distance (HD), mean surface distance (MSD) and residual mean square distance (RMSD). We benchmarked the performance against two other current leading edge networks known as Dynamic UNet and Swin-UNetr on the same public dataset.Results.The proposed network achieved the following volume similarity metrics when averaged over three cardiac segments: Dice = 0.952 ± 0.017, precision = 0.948 ± 0.016, sensitivity = 0.956 ± 0.022. The average surface similarities were HD = 1.521 ± 0.121 mm, MSD = 0.266 ± 0.075 mm, and RMSD = 0.668 ± 0.288 mm. The network shows statistically significant improvement in comparison to the Dynamic UNet and Swin-UNetr algorithms for most volumetric and surface metrics withp-value less than 0.05. Overall, the proposed Swin-MLP mixer network demonstrates better or comparable performance than competing methods.Significance.The proposed Swin-MLP mixer network demonstrates more accurate segmentation performance compared to current leading edge methods. This robust method demonstrates the potential to streamline clinical workflows for multiple applications.
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Affiliation(s)
- Elham Abouei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
| | - Shaoyan Pan
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
- Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, United States of America
| | - Mingzhe Hu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
- Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, United States of America
| | - Aparna H Kesarwala
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
| | - Richard L J Qiu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
| | - Jun Zhou
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
| | - Justin Roper
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
- Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, United States of America
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Hoh T, Margolis I, Weine J, Joyce T, Manka R, Weisskopf M, Cesarovic N, Fuetterer M, Kozerke S. Impact of late gadolinium enhancement image acquisition resolution on neural network based automatic scar segmentation. J Cardiovasc Magn Reson 2024; 26:101031. [PMID: 38431078 PMCID: PMC10981112 DOI: 10.1016/j.jocmr.2024.101031] [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: 02/23/2024] [Accepted: 02/23/2024] [Indexed: 03/05/2024] Open
Abstract
BACKGROUND Automatic myocardial scar segmentation from late gadolinium enhancement (LGE) images using neural networks promises an alternative to time-consuming and observer-dependent semi-automatic approaches. However, alterations in data acquisition, reconstruction as well as post-processing may compromise network performance. The objective of the present work was to systematically assess network performance degradation due to a mismatch of point-spread function between training and testing data. METHODS Thirty-six high-resolution (0.7×0.7×2.0 mm3) LGE k-space datasets were acquired post-mortem in porcine models of myocardial infarction. The in-plane point-spread function and hence in-plane resolution Δx was retrospectively degraded using k-space lowpass filtering, while field-of-view and matrix size were kept constant. Manual segmentation of the left ventricle (LV) and healthy remote myocardium was performed to quantify location and area (% of myocardium) of scar by thresholding (≥ SD5 above remote). Three standard U-Nets were trained on training resolutions Δxtrain = 0.7, 1.2 and 1.7 mm to predict endo- and epicardial borders of LV myocardium and scar. The scar prediction of the three networks for varying test resolutions (Δxtest = 0.7 to 1.7 mm) was compared against the reference SD5 thresholding at 0.7 mm. Finally, a fourth network trained on a combination of resolutions (Δxtrain = 0.7 to 1.7 mm) was tested. RESULTS The prediction of relative scar areas showed the highest precision when the resolution of the test data was identical to or close to the resolution used during training. The median fractional scar errors and precisions (IQR) from networks trained and tested on the same resolution were 0.0 percentage points (p.p.) (1.24 - 1.45), and - 0.5 - 0.0 p.p. (2.00 - 3.25) for networks trained and tested on the most differing resolutions, respectively. Deploying the network trained on multiple resolutions resulted in reduced resolution dependency with median scar errors and IQRs of 0.0 p.p. (1.24 - 1.69) for all investigated test resolutions. CONCLUSION A mismatch of the imaging point-spread function between training and test data can lead to degradation of scar segmentation when using current U-Net architectures as demonstrated on LGE porcine myocardial infarction data. Training networks on multi-resolution data can alleviate the resolution dependency.
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Affiliation(s)
- Tobias Hoh
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland.
| | - Isabel Margolis
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland.
| | - Jonathan Weine
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Thomas Joyce
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland.
| | - Robert Manka
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland; Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Department of Cardiology, University Heart Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
| | - Miriam Weisskopf
- Center of Surgical Research, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
| | - Nikola Cesarovic
- Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland; Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany.
| | - Maximilian Fuetterer
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland.
| | - Sebastian Kozerke
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland.
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Milosevic M, Jin Q, Singh A, Amal S. Applications of AI in multi-modal imaging for cardiovascular disease. FRONTIERS IN RADIOLOGY 2024; 3:1294068. [PMID: 38283302 PMCID: PMC10811170 DOI: 10.3389/fradi.2023.1294068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 12/22/2023] [Indexed: 01/30/2024]
Abstract
Data for healthcare is diverse and includes many different modalities. Traditional approaches to Artificial Intelligence for cardiovascular disease were typically limited to single modalities. With the proliferation of diverse datasets and new methods in AI, we are now able to integrate different modalities, such as magnetic resonance scans, computerized tomography scans, echocardiography, x-rays, and electronic health records. In this paper, we review research from the last 5 years in applications of AI to multi-modal imaging. There have been many promising results in registration, segmentation, and fusion of different magnetic resonance imaging modalities with each other and computer tomography scans, but there are still many challenges that need to be addressed. Only a few papers have addressed modalities such as x-ray, echocardiography, or non-imaging modalities. As for prediction or classification tasks, there have only been a couple of papers that use multiple modalities in the cardiovascular domain. Furthermore, no models have been implemented or tested in real world cardiovascular clinical settings.
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Affiliation(s)
- Marko Milosevic
- Roux Institute, Northeastern University, Portland, ME, United States
| | - Qingchu Jin
- Roux Institute, Northeastern University, Portland, ME, United States
| | - Akarsh Singh
- College of Engineering, Northeastern University, Boston, MA, United States
| | - Saeed Amal
- Roux Institute, Northeastern University, Portland, ME, United States
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Li L, Ding W, Huang L, Zhuang X, Grau V. Multi-modality cardiac image computing: A survey. Med Image Anal 2023; 88:102869. [PMID: 37384950 DOI: 10.1016/j.media.2023.102869] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 05/01/2023] [Accepted: 06/12/2023] [Indexed: 07/01/2023]
Abstract
Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases. It allows a combination of complementary anatomical, morphological and functional information, increases diagnosis accuracy, and improves the efficacy of cardiovascular interventions and clinical outcomes. Fully-automated processing and quantitative analysis of multi-modality cardiac images could have a direct impact on clinical research and evidence-based patient management. However, these require overcoming significant challenges including inter-modality misalignment and finding optimal methods to integrate information from different modalities. This paper aims to provide a comprehensive review of multi-modality imaging in cardiology, the computing methods, the validation strategies, the related clinical workflows and future perspectives. For the computing methodologies, we have a favored focus on the three tasks, i.e., registration, fusion and segmentation, which generally involve multi-modality imaging data, either combining information from different modalities or transferring information across modalities. The review highlights that multi-modality cardiac imaging data has the potential of wide applicability in the clinic, such as trans-aortic valve implantation guidance, myocardial viability assessment, and catheter ablation therapy and its patient selection. Nevertheless, many challenges remain unsolved, such as missing modality, modality selection, combination of imaging and non-imaging data, and uniform analysis and representation of different modalities. There is also work to do in defining how the well-developed techniques fit in clinical workflows and how much additional and relevant information they introduce. These problems are likely to continue to be an active field of research and the questions to be answered in the future.
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Affiliation(s)
- Lei Li
- Department of Engineering Science, University of Oxford, Oxford, UK.
| | - Wangbin Ding
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
| | - Liqin Huang
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
| | - Xiahai Zhuang
- School of Data Science, Fudan University, Shanghai, China
| | - Vicente Grau
- Department of Engineering Science, University of Oxford, Oxford, UK
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Beetz M, Yang Y, Banerjee A, Li L, Grau V. 3D Shape-Based Myocardial Infarction Prediction Using Point Cloud Classification Networks. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082756 DOI: 10.1109/embc40787.2023.10340878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases with associated clinical decision-making typically based on single-valued imaging biomarkers. However, such metrics only approximate the complex 3D structure and physiology of the heart and hence hinder a better understanding and prediction of MI outcomes. In this work, we investigate the utility of complete 3D cardiac shapes in the form of point clouds for an improved detection of MI events. To this end, we propose a fully automatic multi-step pipeline consisting of a 3D cardiac surface reconstruction step followed by a point cloud classification network. Our method utilizes recent advances in geometric deep learning on point clouds to enable direct and efficient multi-scale learning on high-resolution surface models of the cardiac anatomy. We evaluate our approach on 1068 UK Biobank subjects for the tasks of prevalent MI detection and incident MI prediction and find improvements of ∼13% and ∼5% respectively over clinical benchmarks. Furthermore, we analyze the role of each ventricle and cardiac phase for 3D shape-based MI detection and conduct a visual analysis of the morphological and physiological patterns typically associated with MI outcomes.Clinical relevance- The presented approach enables the fast and fully automatic pathology-specific analysis of full 3D cardiac shapes. It can thus be employed as a real-time diagnostic tool in clinical practice to discover and visualize more intricate biomarkers than currently used single-valued metrics and improve predictive accuracy of myocardial infarction.
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