[A generative adversarial network-based unsupervised domain adaptation method for magnetic resonance image segmentation]

Intelligent medical image segmentation methods have been rapidly developed and applied, while a significant challenge is domain shift. That is, the segmentation performance degrades due to distribution differences between the source domain and the target domain. This paper proposed an unsupervised end-to-end domain adaptation medical image segmentation method based on the generative adversarial network (GAN). A network training and adjustment model was designed, including segmentation and discriminant networks. In the segmentation network, the residual module was used as the basic module to increase feature reusability and reduce model optimization difficulty. Further, it learned cross-domain features at the image feature level with the help of the discriminant network and a combination of segmentation loss with adversarial loss. The discriminant network took the convolutional neural network and used the labels from the source domain, to distinguish whether the segmentation result of the generated network is from the source domain or the target domain. The whole training process was unsupervised. The proposed method was tested with experiments on a public dataset of knee magnetic resonance (MR) images and the clinical dataset from our cooperative hospital. With our method, the mean Dice similarity coefficient (DSC) of segmentation results increased by 2.52% and 6.10% to the classical feature level and image level domain adaptive method. The proposed method effectively improves the domain adaptive ability of the segmentation method, significantly improves the segmentation accuracy of the tibia and femur, and can better solve the domain transfer problem in MR image segmentation.智能医学图像分割方法正在快速地发展和应用，但面临着域转移挑战，即由于源域和目标域数据分布不同导致算法性能下降。为此，本文提出了一种基于生成对抗网络（GAN）的无监督端到端域自适应医学图像分割方法。设计网络训练调整模型，由分割网络和鉴别网络组成。分割网络以残差模块为基本模块，增加对特征的复用能力，降低模型优化难度，并将分割损失与对抗损失相结合，在鉴别网络的作用下学习图像特征层面的跨域特征。鉴别网络采用卷积神经网络，并带入源域标签训练，用来区分生成网络的分割结果是来自源域或目标域，整个训练过程无监督。使用膝关节磁共振（MR）图像公开数据集和采集的临床数据集进行实验，与经典的特征级域自适应方法和图像级域自适应方法对比，所提方法的平均戴斯相似性系数（DSC）分别提高了2.52%与6.10%。本文方法有效提高了分割方法的域自适应能力，显著提高了对胫骨和股骨的分割精度，可以较好地解决磁共振图像分割中的域转移问题。.