Purpose

With the recent development of deep learning technologies, various neural networks have been proposed for fundus retinal vessel segmentation. Among them, the U-Net is regarded as one of the most successful architectures. In this work, we start with simplification of the U-Net, and explore the performance of few-parameter networks on this task.

Methods

We firstly modify the model with popular functional blocks and additional resolution levels, then we switch to exploring the limits for compression of the network architecture. Experiments are designed to simplify the network structure, decrease the number of trainable parameters, and reduce the amount of training data. Performance evaluation is carried out on four public databases, namely DRIVE, STARE, HRF and CHASE_DB1. In addition, the generalization ability of the few-parameter networks are compared against the state-of-the-art segmentation network.

Results

We demonstrate that the additive variants do not significantly improve the segmentation performance. The performance of the models are not severely harmed unless they are harshly degenerated: one level, or one filter in the input convolutional layer, or trained with one image. We also demonstrate that few-parameter networks have strong generalization ability.

Conclusion

It is counter-intuitive that the U-Net produces reasonably good segmentation predictions until reaching the mentioned limits. Our work has two main contributions. On the one hand, the importance of different elements of the U-Net is evaluated, and the minimal U-Net which is capable of the task is presented. On the other hand, our work demonstrates that retinal vessel segmentation can be tackled by surprisingly simple configurations of U-Net reaching almost state-of-the-art performance. We also show that the simple configurations have better generalization ability than state-of-the-art models with high model complexity. These observations seem to be in contradiction to the current trend of continued increase in model complexity and capacity for the task under consideration.

中文翻译：

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“保持简单，学者”：眼底成像中视网膜血管的多参数分割网络的实验分析

目的

随着深度学习技术的最新发展，已经提出了各种神经网络用于眼底视网膜血管分割。其中，U-Net被认为是最成功的体系结构之一。在这项工作中，我们从简化U-Net开始，并探索在此任务上少数参数网络的性能。

方法

我们首先用流行的功能块和附加的分辨率级别修改模型，然后切换到探索网络体系结构压缩的极限。实验旨在简化网络结构，减少可训练参数的数量并减少训练数据的数量。性能评估是在四个公共数据库（即DRIVE，STARE，HRF和CHASE_DB1）上进行的。此外，将少数参数网络的泛化能力与最新的分段网络进行了比较。

结果

我们证明了添加剂变体不会显着提高细分性能。除非严重退化模型的性能，否则它们的性能不会受到严重损害：输入卷积层中的一个级别或一个滤波器，或者使用一张图像对其进行训练。我们还证明了很少参数的网络具有很强的泛化能力。

结论

在达到上述限制之前，U-Net会产生合理的分段预测，这是违反直觉的。我们的工作有两个主要贡献。一方面，评估了U-Net不同元素的重要性，并提出了能够完成此任务的最小U-Net。另一方面，我们的工作表明，通过令人惊奇的简单配置的U-Net即可达到几乎最先进的性能，从而可以解决视网膜血管的分割问题。我们还显示，与具有高模型复杂性的最新模型相比，简单的配置具有更好的泛化能力。这些观察似乎与当前不断增加的模型复杂性和正在考虑的任务的能力的趋势相矛盾。