Simultaneous Deep Learning of Myocardium Segmentation and T2 Quantification for Acute Myocardial Infarction MRI

TL;DR

This paper introduces SQNet, a dual-task deep learning model that simultaneously segments myocardium and quantifies T2 in cardiac MRI, improving accuracy and clinical utility for diagnosing myocardial infarction.

Contribution

SQNet integrates Transformer and CNN components with a fusion decoder and coupling module, enabling effective joint myocardium segmentation and T2 quantification in cardiac MRI.

Findings

Achieves higher segmentation dice scores (89.3/89.2) than state-of-the-art (87.7/87.9)

Demonstrates strong linear correlation (Pearson 0.84/0.93) for T2 quantification

Radiologist evaluations favor SQNet's image quality over existing methods

Abstract

In cardiac Magnetic Resonance Imaging (MRI) analysis, simultaneous myocardial segmentation and T2 quantification are crucial for assessing myocardial pathologies. Existing methods often address these tasks separately, limiting their synergistic potential. To address this, we propose SQNet, a dual-task network integrating Transformer and Convolutional Neural Network (CNN) components. SQNet features a T2-refine fusion decoder for quantitative analysis, leveraging global features from the Transformer, and a segmentation decoder with multiple local region supervision for enhanced accuracy. A tight coupling module aligns and fuses CNN and Transformer branch features, enabling SQNet to focus on myocardium regions. Evaluation on healthy controls (HC) and acute myocardial infarction patients (AMI) demonstrates superior segmentation dice scores (89.3/89.2) compared to state-of-the-art methods (87.7/87.9). T2 quantification yields strong linear correlations (Pearson coefficients: 0.84/0.93) with label values for HC/AMI, indicating accurate mapping. Radiologist evaluations confirm SQNet's superior image quality scores (4.60/4.58 for segmentation, 4.32/4.42 for T2 quantification) over state-of-the-art methods (4.50/4.44 for segmentation, 3.59/4.37 for T2 quantification). SQNet thus offers accurate simultaneous segmentation and quantification, enhancing cardiac disease diagnosis, such as AMI.