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Updated: Aug 12, 2025
3D Whole-heart Myocardial Tissue Analysis
Published on: April 12, 2017
Automated Inline Myocardial Segmentation of Joint T1 and T2 Mapping Using Deep Learning
James P Howard1, Kelvin Chow1, Liza Chacko1
1National Heart and Lung Institute, Imperial College London, Du Cane Rd, B Block, 2nd Floor, Hammersmith Hospital, London W12 0HS, England (J.P.H., G.D.C.); National Amyloidosis Centre, Division of Medicine, University College London, London, England (L.C., M.F.); Cardiovascular MR R&D, Siemens Medical Solutions USA, Chicago, Ill (K.C., L.C., M.F.); and Medical Signal and Image Processing Program, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Md (P.K., H.X.).
Purpose:
To develop an artificial intelligence (AI) solution for automated segmentation and analysis of joint cardiac MRI short-axis T1 and T2 mapping.
Materials And Methods:
In this retrospective study, a joint T1 and T2 mapping sequence was used to acquire 4240 maps from 807 patients across two hospitals between March and November 2020. Five hundred nine maps from 94 consecutive patients were assigned to a holdout testing set. A convolutional neural network was trained to segment the endocardial and epicardial contours with use of an edge probability estimation approach. Training labels were segmented by an expert cardiologist. Predicted contours were processed to yield mapping values for each of the 16 American Heart Association segments. Network segmentation performance and segment-wise measurements on the testing set were compared with those of two experts on the holdout testing set. The AI model was fully integrated using open-source software to run on MRI scanners.
Results:
A total of 3899 maps (92%) were deemed artifact-free and suitable for human segmentation. AI segmentation closely matched that of each expert (mean Dice coefficient, 0.82 ± 0.07 [SD] vs expert 1 and 0.86 ± 0.06 vs expert 2) and compared favorably with interexpert agreement (Dice coefficient, 0.84 ± 0.06 for expert 1 vs expert 2). AI-derived segment-wise values for native T1, postcontrast T1, and T2 mapping correlated with expert-derived values (R 2 = 0.96, 0.98, and 0.87, respectively, vs expert 1, and 0.97, 0.99, and 0.92 vs expert 2) and fell within the range of interexpert reproducibility (R 2 = 0.97, 0.99, and 0.90, respectively). The AI model has since been deployed at two hospitals, enabling automated inline analysis.
Conclusion:
Automated inline analysis of joint T1 and T2 mapping allows accurate segment-wise tissue characterization, with performance equivalent to that of human experts.Keywords: MRI, Neural Networks, Cardiac, Heart Supplemental material is available for this article. © RSNA, 2022.
