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Deep learning-based segmentation of left ventricular myocardium on dynamic contrast-enhanced MRI: a comprehensive evaluation across temporal frames

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Cardiac perfusion MRI is vital for disease diagnosis, treatment planning, and risk stratification, with anomalies serving as markers of underlying ischemic pathologies. AI-assisted methods and tools enable accurate and efficient left ventricular (LV) myocardium segmentation on all DCE-MRI timeframes, offering a solution to the challenges posed by the multidimensional nature of the data. This study aims to develop and assess an automated method for LV myocardial segmentation on DCE-MRI data of a local hospital.

Methods

The study consists of retrospective DCE-MRI data from 55 subjects acquired at the local hospital using a 1.5 T MRI scanner. The dataset included subjects with and without cardiac abnormalities. The timepoint for the reference frame (post-contrast LV myocardium) was identified using standard deviation across the temporal sequences. Iterative image registration of other temporal images with respect to this reference image was performed using Maxwell’s demons algorithm. The registered stack was fed to the model built using the U-Net framework for predicting the LV myocardium at all timeframes of DCE-MRI.

Results

The mean and standard deviation of the dice similarity coefficient (DSC) for myocardial segmentation using pre-trained network Net_cine is 0.78 ± 0.04, and for the fine-tuned network Net_dyn which predicts mask on all timeframes individually, it is 0.78 ± 0.03. The DSC for Net_dyn ranged from 0.71 to 0.93. The average DSC achieved for the reference frame is 0.82 ± 0.06.

Conclusion

The study proposed a fast and fully automated AI-assisted method to segment LV myocardium on all timeframes of DCE-MRI data. The method is robust, and its performance is independent of the intra-temporal sequence registration and can easily accommodate timeframes with potential registration errors.

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Acknowledgements

The authors would like to acknowledge the support received from MedImg Lab members (IIT, Delhi) in carrying out the research and proofreading the paper.

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Authors and Affiliations

  1. Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, 110016, India

    Raufiya Jafari & Anup Singh

  2. Department of Radiology, Fortis Memorial Research Institute, Gurugram, India

    Radhakrishan Verma & Rakesh Kumar Gupta

  3. Department of Cardiology, Fortis Memorial Research Institute, Gurugram, India

    Vinayak Aggarwal

  4. Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, Delhi, India

    Anup Singh

  5. Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi, New Delhi, Delhi, India

    Anup Singh

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  1. Raufiya Jafari
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  2. Radhakrishan Verma
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  4. Rakesh Kumar Gupta
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  5. Anup Singh
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Correspondence to Anup Singh.

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The authors declare that none of the work reported in this study could have been influenced by any known competing financial interests or personal relationships.

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This is a retrospective study, and Institutional Ethics Committee (IEC) approval was obtained (IEC code no.: 2022-005-EMP-38).

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Jafari, R., Verma, R., Aggarwal, V. et al. Deep learning-based segmentation of left ventricular myocardium on dynamic contrast-enhanced MRI: a comprehensive evaluation across temporal frames. Int J CARS 19, 2055–2062 (2024). https://doi.org/10.1007/s11548-024-03221-z

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  • DOI: https://doi.org/10.1007/s11548-024-03221-z

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