Automated cardiovascular magnetic resonance image analysis with fully convolutional networks
Open accessAbstract
BACKGROUND: Cardiovascular resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction and myocardial mass, providing information for diagnosis and monitoring of CVDs. However, for years, clinicians have been relying on manual approaches for CMR image analysis, which is time consuming and prone to subjective errors. It is a major clinical challenge to automatically derive quantitative and clinically relevant information from CMR images. METHODS: Deep neural networks have shown a great potential in image pattern recognition and segmentation for a variety of tasks. Here we demonstrate an automated analysis method for CMR images, which is based on a fully convolutional network (FCN). The network is trained and evaluated on a large-scale dataset from the UK Biobank, consisting of 4,875 subjects with 93,500 pixelwise annotated images. The performance of the method has been evaluated using a number of technical metrics, including the Dice metric, mean contour distance and Hausdorff distance, as well as clinically relevant measures, including left ventricle (LV) end-diastolic volume (LVEDV) and end-systolic volume (LVESV), LV mass (LVM); right ventricle (RV) end-diastolic volume (RVEDV) and end-systolic volume (RVESV). RESULTS: By combining FCN with a large-scale annotated dataset, the proposed automated method achieves a high performance in segmenting the LV and RV on short-axis CMR images and the left atrium (LA) and right atrium (RA) on long-axis CMR images. On a short-axis image test set of 600 subjects, it achieves an average Dice metric of 0.94 for the LV cavity, 0.88 for the LV myocardium and 0.90 for the RV cavity. The mean absolute difference between automated measurement and manual measurement is 6.1 mL for LVEDV, 5.3 mL for LVESV, 6.9 gram for LVM, 8.5 mL for RVEDV and 7.2 mL for RVESV. On long-axis image test sets, the average Dice metric is 0.93 for the LA cavity (2-chamber view), 0.95 for the LA cavity (4-chamber view) and 0.96 for the RA cavity (4-chamber view). The performance is comparable to human inter-observer variability. CONCLUSIONS: We show that an automated method achieves a performance on par with human experts in analysing CMR images and deriving clinically relevant measures.
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AI has been applied in various domains of healthcare including cancer research, cardiology, diabetes, mental health, identification of prognosis, Alzheimer's disease, identification of difference in clinical groups, cardiovascular disease, and stroke-related studies.
“AI has been put in application in various domains of healthcare [83–87] which includes cancer research [88], cardiology [89], diabetes [90], mental health [91], identification of prognosis [92], identification of Alzheimer's disease [93], identification of difference in the clinical groups [94], identification of cardiovascular disease [39], stroke-related studies [95], etc.”
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- Authors
- Bai, W., Sinclair, M., Tarroni, G., Oktay, O., Rajchl, M., Vaillant, G., Lee, A. M., Aung, N., Lukaschuk, E., Sanghvi, M. M., Zemrak, F., Fung, K., Paiva, J. M., Carapella, V., Kim, Y. J., Suzuki, H., Kainz, B., Matthews, P. M., Petersen, S. E., Piechnik, S. K., Neubauer, S., Glocker, B., & Rueckert, D.
- Affiliations
- Imperial College London (GB)
- Queen Mary University of London (GB)
- University of Oxford (GB)
- Corresponding author
- Bai, Wenjia
- ORCID
- Bai, Wenjia
- Sinclair, Matthew
- Tarroni, Giacomo
- Oktay, Ozan
- Rajchl, Martin
- Vaillant, Ghislain
- Lee, Aaron M.
- Aung, Nay
- Lukaschuk, Elena
- Sanghvi, Mihir M.
- Zemrak, Filip
- Fung, Kenneth
- Carapella, Valentina
- Kim, Young Jin
- Suzuki, Hideaki
- Kainz, Bernhard
- Matthews, Paul M.
- Petersen, Steffen E.
- Piechnik, Stefan K.
- Neubauer, Stefan
- Glocker, Ben
- Rueckert, Daniel
- Venue
- Journal of Cardiovascular Magnetic Resonance
- Publisher
- Elsevier BV
- Published
- 2018-02
- Volume
- 20
- Issue
- 1
- Pages
- 65
- ISSN
- 1097-6647 (ISSN-L)
- PMC
- PMC6138894
- OpenAlex
- W2803176574
- Cited by
- 773
- References
- 7
- Citations by year
- 2026: 87 · 2025: 100 · 2024: 80 · 2023: 107 · 2022: 111 · 2021: 113 · 2020: 96 · 2019: 69 · 2018: 10
- FWCI
- 36.01
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Cardiovascular Magnetic Resonance (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- City Research Online (City University London) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion, cc-by)
- Spiral (Imperial College London) (submittedVersion, other-oa)
- Funders
- Engineering and Physical Sciences Research Council (EP/P001009/1)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- cardiovascular magnetic resonance, fully convolutional network, cardiac image segmentation, left ventricle, right ventricle, left atrium, right atrium, ejection fraction, myocardial mass, UK Biobank, automated image analysis, interobserver variability, myocardium segmentation, left ventricular end-diastolic volume
- MeSH terms
- Deep Learning, Aged, Automation, Female, Heart Diseases, Humans, Image Interpretation, Computer-Assisted, Male, Middle Aged, Myocardial Contraction, Predictive Value of Tests, Stroke Volume, Reproducibility of Results, Observer Variation, Databases, Factual, Ventricular Function, Left, Ventricular Function, Right, Neural Networks, Computer, Magnetic Resonance Imaging, Cine
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