In this paper, we present the Multi-Center Privacy-Preserving Network (MP-Net), a novel framework designed for secure medical image segmentation in multi-center collaborations. Our methodology offers a new approach to multi-center collaborative learning, capable of reducing the volume of data transmission and enhancing data privacy protection. Unlike federated learning, which requires the transmission of model data between the central server and local servers in each round, our method only necessitates a single transfer of encrypted data. The proposed MP-Net comprises a three-layer model, consisting of encryption, segmentation, and decryption networks. We encrypt the image data into ciphertext using an encryption network and introduce an improved U-Net for image ciphertext segmentation. Finally, the segmentation mask is obtained through a decryption network. This architecture enables ciphertext-based image segmentation through computable image encryption. We evaluate the effectiveness of our approach on three datasets, including two cardiac MRI datasets and a CTPA dataset. Our results demonstrate that the MP-Net can securely utilize data from multiple centers to establish a more robust and information-rich segmentation model.

中文翻译：

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MP-Net：用于医学图像分割的多中心隐私保护网络


在本文中，我们提出了多中心隐私保护网络（MP-Net），这是一种专为多中心协作中安全医学图像分割而设计的新颖框架。我们的方法论提供了一种新的多中心协作学习方法，能够减少数据传输量并增强数据隐私保护。与联邦学习每轮都需要在中央服务器和本地服务器之间传输模型数据不同，我们的方法只需要单次传输加密数据。所提出的 MP-Net 包括一个三层模型，由加密、分段和解密网络组成。我们使用加密网络将图像数据加密为密文，并引入改进的 U-Net 进行图像密文分割。最后通过解密网络得到分割掩码。该架构通过可计算图像加密实现基于密文的图像分割。我们评估了我们的方法在三个数据集上的有效性，包括两个心脏 MRI 数据集和一个 CTPA 数据集。我们的结果表明，MP-Net 可以安全地利用来自多个中心的数据来建立更强大且信息丰富的分割模型。