CineMyoPS: Segmenting Myocardial Pathologies from Cine Cardiac MR

TL;DR

CineMyoPS is a novel deep learning model that accurately segments myocardial scars and edema from cine CMR images, enabling rapid, contrast-free cardiac pathology assessment by leveraging motion and anatomy features across the cardiac cycle.

Contribution

The paper introduces CineMyoPS, an end-to-end neural network that jointly learns motion and anatomy features from cine CMR for myocardial pathology segmentation, with a novel consistency loss and time-series aggregation.

Findings

Achieves high accuracy in myocardial pathology segmentation.

Effectively estimates motion and anatomy from cine CMR.

Demonstrates robustness across multi-center datasets.

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.