论文标题
使用多层对抗学习从肾脏活检图像进行跨染色的分割
Cross-stained Segmentation from Renal Biopsy Images Using Multi-level Adversarial Learning
论文作者
论文摘要
肾脏病理图像的分割是自动分析肾脏组织学特征的关键步骤。但是，由于外观变化，模型的性能在不同类型的染色数据集中显着变化。在本文中，我们为跨染色的分割设计了强大而灵活的模型。这是一个新颖的多级深层对抗网络体系结构，由三个子网络组成：（i）分割网络； （ii）一对多级镜像歧视器，用于指导分割网络提取域不变特征； （iii）一种形状歧视器，用于进一步确定分割网络和地面真相的输出。从肾脏活检图像进行肾小球分割的实验结果表明，我们的网络能够改善染色图像的目标类型上的分割性能，并使用未标记的数据来实现与标记数据相似的准确性。另外，此方法可以轻松地应用于其他任务。
Segmentation from renal pathological images is a key step in automatic analyzing the renal histological characteristics. However, the performance of models varies significantly in different types of stained datasets due to the appearance variations. In this paper, we design a robust and flexible model for cross-stained segmentation. It is a novel multi-level deep adversarial network architecture that consists of three sub-networks: (i) a segmentation network; (ii) a pair of multi-level mirrored discriminators for guiding the segmentation network to extract domain-invariant features; (iii) a shape discriminator that is utilized to further identify the output of the segmentation network and the ground truth. Experimental results on glomeruli segmentation from renal biopsy images indicate that our network is able to improve segmentation performance on target type of stained images and use unlabeled data to achieve similar accuracy to labeled data. In addition, this method can be easily applied to other tasks.
