Abstract
Background and Purpose: Functional assessment of the left ventricle using gated myocardial perfusion (MPS) single-photon emission computed tomography relies on the precise extraction of the left ventricular contours while simultaneously ensuring the security of patient data. Methods: In this paper, we introduce the integration of Federated Domain Adaptation with TimeSformer, named 'FedDA-TSformer' for left ventricle segmentation using MPS. FedDA-TSformer captures spatial and temporal features in gated MPS images, leveraging spatial attention, temporal attention, and federated learning for improved domain adaptation while ensuring patient data security. In detail, we employed Divide-Space-Time-Attention mechanism to extract spatio-temporal correlations from the multi-centered MPS datasets, ensuring that predictions are spatio-temporally consistent. To achieve domain adaptation, we align the model output on MPS from three different centers using local maximum mean discrepancy (LMMD) loss. This approach effectively addresses the dual requirements of federated learning and domain adaptation, enhancing the model's performance during training with multi-site datasets while ensuring the protection of data from different hospitals. Results: Our FedDA-TSformer was trained and evaluated using MPS datasets collected from three hospitals, comprising a total of 150 subjects. Each subject's cardiac cycle was divided into eight gates. The model achieved Dice Similarity Coefficients (DSC) of 0.842 and 0.907 for left ventricular (LV) endocardium and epicardium segmentation, respectively. Conclusion: Our proposed FedDA-TSformer model addresses the challenge of multi-center generalization, ensures patient data privacy protection, and demonstrates effectiveness in left ventricular (LV) segmentation.
Abstract (translated)
背景和目的：使用门控心肌灌注（MPS）单光子发射计算机断层扫描评估左心室功能，需要精确提取左心室轮廓并同时确保患者数据的安全性。 方法：本文介绍了将联邦领域适应与TimeSformer结合的方法，命名为“FedDA-TSformer”，用于基于MPS的左心室分割。 FedDA-TSformer能够捕捉门控MPS图像中的空间和时间特征，并利用空间注意力、时间注意力及联邦学习来提高域适应能力，同时确保患者数据的安全性。具体来说，我们采用了Divide-Space-Time-Attention机制从多中心的MPS数据集中提取时空关联，以确保预测在时间和空间上的一致性。为了实现领域适应，我们在三个不同中心的MPS图像上对模型输出进行局部最大平均差异（LMMD）损失对齐，从而有效地解决了联邦学习和领域适应的双重需求，在训练期间使用多站点数据集时提高了模型性能，并确保了来自不同医院的数据安全。 结果：我们的FedDA-TSformer使用从三个医院收集的MPS数据集进行了训练和评估，总共有150个受试者。每个受试者的整个心动周期被分为八个门控区间。该模型在左心室（LV）内膜和外膜分割上分别达到了Dice相似系数(DSC)为0.842 和 0.907。 结论：我们提出的FedDA-TSformer模型解决了多中心泛化挑战，确保了患者数据隐私保护，并且在左心室(LV)分割方面表现出有效性。
URL
https://arxiv.org/abs/2502.16709
