CineMyoPS: Segmenting Myocardial Pathologies from Cine Cardiac MR
Journal:
arXiv
Published Date:
Jul 3, 2025
Abstract
Myocardial infarction (MI) is a leading cause of death worldwide. Late
gadolinium enhancement (LGE) and T2-weighted cardiac magnetic resonance (CMR)
imaging can respectively identify scarring and edema areas, both of which are
essential for MI risk stratification and prognosis assessment. Although
combining complementary information from multi-sequence CMR is useful,
acquiring these sequences can be time-consuming and prohibitive, e.g., due to
the administration of contrast agents. Cine CMR is a rapid and contrast-free
imaging technique that can visualize both motion and structural abnormalities
of the myocardium induced by acute MI. Therefore, we present a new end-to-end
deep neural network, referred to as CineMyoPS, to segment myocardial
pathologies, \ie scars and edema, solely from cine CMR images. Specifically,
CineMyoPS extracts both motion and anatomy features associated with MI. Given
the interdependence between these features, we design a consistency loss
(resembling the co-training strategy) to facilitate their joint learning.
Furthermore, we propose a time-series aggregation strategy to integrate
MI-related features across the cardiac cycle, thereby enhancing segmentation
accuracy for myocardial pathologies. Experimental results on a multi-center
dataset demonstrate that CineMyoPS achieves promising performance in myocardial
pathology segmentation, motion estimation, and anatomy segmentation.
