We propose a client-server system which allows for the analysis of multi-centric medical images while preserving patient identity. In our approach, the client protects the patient identity by applying a pseudo-random non-linear deformation to the input image. This results into a proxy image which is sent to the server for processing. The server then returns back the deformed processed image which the client reverts to a canonical form. Our system has three components: 1) a flow-field generator which produces a pseudo-random deformation function, 2) a Siamese discriminator that learns the patient identity from the processed image, 3) a medical image processing network that analyzes the content of the proxy images. The system is trained end-to-end in an adversarial manner. By fooling the discriminator, the flow-field generator learns to produce a bi-directional non-linear deformation which allows to remove and recover the identity of the subject from both the input image and output result. After end-to-end training, the flow-field generator is deployed on the client side and the segmentation network is deployed on the server side. The proposed method is validated on the task of MRI brain segmentation using images from two different datasets. Results show that the segmentation accuracy of our method is similar to a system trained on non-encoded images, while considerably reducing the ability to recover subject identity.
翻译:我们提议一个客户服务器系统,用于分析多中心医疗图像,同时保存患者身份。 在我们的方法中, 客户通过对输入图像应用假随机非线性变形来保护患者身份。 其结果为发送到服务器处理的代理图像。 服务器随后又返回客户恢复为直线形式的已变形非线性变形图像。 我们的系统有三个组成部分:1) 流场生成器, 产生假随机变形功能;2) 一个从已处理图像中了解患者身份的暹罗色歧视器;3) 医疗图像处理网络, 分析代理图像的内容。 该系统经过对端到端的培训, 以对抗的方式被送至端。 通过愚弄导师, 流动场生成器学会生成双向方向的非线性变形变形图像。 允许从输入图像和输出结果中移除和恢复对象身份的双向型变形变形变形图像。 在用户端培训后, 流动场发电机将安装在客户端, 分路处理网络在服务器上安装分析代理图像内容的内容。 以对抗的方式, 将模拟图像的精度校验后, 显示为不相同的分路路路规则 。 。