Zhang Rui, Wang Xu, Zhou Zijian, Ni Luyan, Jiang Meng, Hu Peng
School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, 393 M. Huaxia Rd., Pudong New District, Shanghai, 201210, China.
Department of Cardiology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, 160 Pu Jian Rd., Pudong New District, Shanghai, 200127, China.
MAGMA. 2025 Aug 23. doi: 10.1007/s10334-025-01288-6.
Epicardial and paracardial adipose tissues (EAT and PAT) are two types of fat depots around the heart and they have important roles in cardiac physiology. Manual quantification of EAT and PAT from cardiac MR (CMR) is time-consuming and prone to human bias. Leveraging the cardiac motion, we aimed to develop deep learning neural networks for automated segmentation and quantification of EAT and PAT in short-axis cine CMR.
A modified U-Net equipped with modules of multi-resolution convolution, motion information extraction, feature fusion, and dual attention mechanisms, was developed. Multiple steps of ablation studies were performed to verify the efficacy of each module. The performance of different networks was also compared.
The final network incorporating all modules achieved segmentation Dice indices of 77.72% ± 2.53% and 77.18% ± 3.54% for EAT and PAT, respectively, which were significantly higher than the baseline U-Net. It also achieved the highest performance compared to other networks. With our model, the determination coefficients of EAT and PAT volumes to the reference were 0.8550 and 0.8025, respectively.
Our proposed network can provide accurate and quick quantification of EAT and PAT on routine short-axis cine CMR, which can potentially aid cardiologists in clinical settings.
心外膜和心包旁脂肪组织（EAT和PAT）是心脏周围的两种脂肪库，它们在心脏生理学中具有重要作用。从心脏磁共振成像（CMR）手动量化EAT和PAT既耗时又容易出现人为偏差。利用心脏运动，我们旨在开发深度学习神经网络，用于在短轴电影CMR中自动分割和量化EAT和PAT。
开发了一种改进的U-Net，配备多分辨率卷积、运动信息提取、特征融合和双注意力机制模块。进行了多步消融研究以验证每个模块的有效性。还比较了不同网络的性能。
包含所有模块的最终网络对EAT和PAT的分割Dice指数分别为77.72%±2.53%和77.18%±3.54%，显著高于基线U-Net。与其他网络相比，它也具有最高的性能。使用我们的模型，EAT和PAT体积与参考值的决定系数分别为0.8550和0.8025。
我们提出的网络可以在常规短轴电影CMR上提供准确、快速的EAT和PAT量化，这可能有助于临床环境中的心脏病专家。
