Scientific Reports (May 2024)

Cardiac function in a large animal model of myocardial infarction at 7 T: deep learning based automatic segmentation increases reproducibility

  • Alena Kollmann,
  • David Lohr,
  • Markus J. Ankenbrand,
  • Maya Bille,
  • Maxim Terekhov,
  • Michael Hock,
  • Ibrahim Elabyad,
  • Steffen Baltes,
  • Theresa Reiter,
  • Florian Schnitter,
  • Wolfgang R. Bauer,
  • Ulrich Hofmann,
  • Laura M. Schreiber

DOI
https://doi.org/10.1038/s41598-024-61417-4
Journal volume & issue
Vol. 14, no. 1
pp. 1 – 15

Abstract

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Abstract Cardiac magnetic resonance (CMR) imaging allows precise non-invasive quantification of cardiac function. It requires reliable image segmentation for myocardial tissue. Clinically used software usually offers automatic approaches for this step. These are, however, designed for segmentation of human images obtained at clinical field strengths. They reach their limits when applied to preclinical data and ultrahigh field strength (such as CMR of pigs at 7 T). In our study, eleven animals (seven with myocardial infarction) underwent four CMR scans each. Short-axis cine stacks were acquired and used for functional cardiac analysis. End-systolic and end-diastolic images were labelled manually by two observers and inter- and intra-observer variability were assessed. Aiming to make the functional analysis faster and more reproducible, an established deep learning (DL) model for myocardial segmentation in humans was re-trained using our preclinical 7 T data (n = 772 images and labels). We then tested the model on n = 288 images. Excellent agreement in parameters of cardiac function was found between manual and DL segmentation: For ejection fraction (EF) we achieved a Pearson’s r of 0.95, an Intraclass correlation coefficient (ICC) of 0.97, and a Coefficient of variability (CoV) of 6.6%. Dice scores were 0.88 for the left ventricle and 0.84 for the myocardium.