Optimizing Deep Learning for Cardiac MRI Segmentation: The Impact of Automated Slice Range Classification.

Journal: Academic radiology
Published Date: Feb 1, 2024

Abstract

RATIONALE AND OBJECTIVES: Cardiac magnetic resonance imaging is crucial for diagnosing cardiovascular diseases, but lengthy postprocessing and manual segmentation can lead to observer bias. Deep learning (DL) has been proposed for automated cardiac segmentation; however, its effectiveness is limited by the slice range selection from base to apex.

Authors

  • Sarv Priya
    The University of Iowa Iowa City, Iowa, USA.
  • Durjoy D Dhruba
    Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa (D.D.D., M.J.).
  • Sarah S Perry
    Department of Biostatistics, University of Iowa, Iowa City, Iowa (S.S.P.).
  • Pritish Y Aher
    Department of Radiology, University of Miami, Miller School of Medicine, Miami, Florida (P.Y.A.).
  • Amit Gupta
    Department of Cardiology, SKIMS, Srinagar, India. Electronic address: amitcardio12@gmail.com.
  • Prashant Nagpal
    Department of Radiology, University of Iowa Hospitals and Clinics, Iowa City, IA, USA.
  • Mathews Jacob

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