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

Journal: International journal of computer assisted radiology and surgery
PMID: 38965165

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.

Authors

  • Raufiya Jafari
    Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, 110016, India.
  • Radhakrishan Verma
    Department of Radiology, Fortis Memorial Research Institute, Gurugram, India.
  • Vinayak Aggarwal
    Department of Cardiology, Fortis Memorial Research Institute, Gurugram, India.
  • Rakesh Kumar Gupta
    Department of Radiology, Fortis Memorial Research Institute, Gurgaon, India.
  • Anup Singh
    Centre for Biomedical Engineering, IIT Delhi, New Delhi, India; Department of Biomedical Engineering, AIIMS Delhi, New Delhi, India. Electronic address: anupsm@iitd.ac.in.

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