The accurate retinal vessel segmentation (RVS) is of great significance to assist doctors in the diagnosis of ophthalmology diseases and other systemic diseases. Manually designing a valid neural network architecture for retinal vessel segmentation requires high expertise and a large workload. In order to improve the performance of vessel segmentation and reduce the workload of manually designing neural network, we propose novel approach which applies neural architecture search (NAS) to optimize an encoder-decoder architecture for retinal vessel segmentation. A modified evolutionary algorithm is used to evolve the architectures of encoder-decoder framework with limited computing resources. The evolved model obtained by the proposed approach achieves top performance among all compared methods on the three datasets, namely DRIVE, STARE and CHASE_DB1, but with much fewer parameters. Moreover, the results of cross-training show that the evolved model is with considerable scalability, which indicates a great potential for clinical disease diagnosis.

中文翻译：

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视网膜血管分割的进化神经架构搜索

准确的视网膜血管分割（RVS）对于辅助医生诊断眼科疾病和其他全身性疾病具有重要意义。手动设计用于视网膜血管分割的有效神经网络架构需要高专业知识和大量工作量。为了提高血管分割的性能并减少手动设计神经网络的工作量，我们提出了应用神经架构搜索（NAS）来优化用于视网膜血管分割的编码器-解码器架构的新方法。改进的进化算法用于在有限的计算资源下进化编码器-解码器框架的体系结构。通过所提出的方法获得的进化模型在三个数据集上的所有比较方法中都取得了最佳性能，即 DRIVE、STARE 和 CHASE_DB1，但参数少得多。此外，交叉训练的结果表明，进化模型具有相当大的可扩展性，这表明在临床疾病诊断方面具有巨大潜力。