Abstract: Retinal vessel segmentation is a challenging medical task owing to small size of dataset, micro blood vessels and low image contrast. To address these issues, we introduce a novel convolutional neural network in this paper, which takes the advantage of both adversarial learning and recurrent neural network. An iterative design of network with recurrent unit is performed to refine the segmentation results from input retinal image gradually. Recurrent unit preserves high-level semantic information for feature reuse, so as to output a sufficiently refined segmentation map instead of a coarse mask. Moreover, an adversarial loss is imposing the integrity and connectivity constraints on the segmented vessel regions, thus greatly reducing topology errors of segmentation. The experimental results on the DRIVE dataset show that our method achieves area under curve and sensitivity of 98.17% and 80.64%, respectively. Our method achieves superior performance in retinal vessel segmentation compared with other existing state-of-the-art methods.

Key words: medical image processing, retinal image segmentation, adversarial learning, iterative refinement

摘要： 由于数据量小、血管细小、图像对比度低等特点，视网膜血管分割是一项具有挑战性的医学任务。为了解决这些问题，文中引入了一种新的卷积神经网络，同时利用了对抗学习和循环神经网络的优势。采用递归单元迭代设计网络，逐步优化输入视网膜图像的分割结果。循环单元保留高级语义信息，用于特征重用，从而输出足够精细的分割图，而不是粗掩码。此外，对抗性损失对分割的血管区域施加了完整性和连通性约束，从而大大减少了分割的拓扑错误。在DRIVE数据集上的实验结果表明，该方法的AUC和灵敏度分别达到98.17%和80.64%。与其他现有的最先进方法相比，该方法在视网膜血管分割方面取得了更好的效果。

关键词: 医学图像处理，视网膜图像分割，对抗学习，迭代优化