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Conditional Generative Adversarial Networks with Multi-scale Discriminators for Prostate MRI Segmentation

机译：具有多尺度鉴别器的条件生成对抗网络，用于前列腺MRI分割

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摘要

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.
机译：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阶段。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有力。最近，最有效的前列腺MRI分割技术主要依赖于已广泛用于语义分割任务的完整卷积网络。然而，它是独立的，并且相同的分布式假设忽略了MR图像中存在的结构规律和像素之间的错误信息。在本文中，我们向前列腺细分提出了MRI条件生成的对抗网络。我们的对抗培训使其上下文意识到并使用对抗性损失函数学习高级结构信息。该网络由发电机和鉴别器组成。发电机由收缩频道和U-Net等扩展通道组成。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不同输入尺寸的鉴别器组成。目标函数有两部分：一个是对抗性损失，另一个是具有稳定训练的特征匹配损失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并优于大多数现有方法。

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