Accurate prostate MR image segmentation is a necessary preprocessing stage for computer-assisted diagnostic algorithms. Convolutional neural network, as a research focus in recent years, has been proven to be powerful in computer vision field. Recently, the most effective prostate MRI segmentation technology mainly relies on full convolutional network which has been widely used in semantic segmentation task. However, it’s independent and identically distributed assumption neglect the structural regularity present in MR images and miss information between pixels. In this paper, we propose an MRI-conditional generative adversarial networks for prostate segmentation. Our adversarial training make it context aware and the use of adversarial loss functions learn high-level structural information. The network consist of a generator and a discriminator. The generator consists of a contraction channel and an expansion channel like U-Net. The method we proposed uses a multi-scale discriminator which consist of two discriminators with the same structure but different input sizes. The objective function has two parts: one is the adversarial loss, the other is feature matching loss which stabilizes the training and get better convergence. The experiment show that our network can accurately segment the prostate MRI and outperforms most existing methods.

准确的前列腺MR图像分割是计算机辅助诊断算法的必要预处理步骤。卷积神经网络作为近年来的研究重点，已被证明在计算机视觉领域具有强大的功能。最近，最有效的前列腺MRI分割技术主要依靠全卷积网络，该网络已广泛用于语义分割任务中。但是，它的独立且分布均匀的假设忽略了MR图像中存在的结构规律性，并忽略了像素之间的信息。在本文中，我们提出了一个用于前列腺分割的MRI条件生成对抗网络。我们的对抗训练使它可以根据上下文进行感知，并且对抗损失功能的使用可以学习高级结构信息。网络由生成器和鉴别器组成。生成器由一个收缩通道和一个扩展通道（如U-Net）组成。我们提出的方法使用多尺度鉴别器，该鉴别器由两个具有相同结构但输入大小不同的鉴别器组成。目标函数包括两部分：一个是对抗损失，另一个是特征匹配损失，它可以稳定训练并获得更好的收敛性。实验表明，我们的网络可以准确地分割前列腺MRI，并且胜过大多数现有方法。另一个是特征匹配损失，它可以稳定训练并获得更好的收敛性。实验表明，我们的网络可以准确地分割前列腺MRI，并且胜过大多数现有方法。另一个是特征匹配损失，它可以稳定训练并获得更好的收敛性。实验表明，我们的网络可以准确地分割前列腺MRI，并且胜过大多数现有方法。