论文标题
编码器网络对于视网膜船分割的不合理效力
The Unreasonable Effectiveness of Encoder-Decoder Networks for Retinal Vessel Segmentation
论文作者
论文摘要
我们提出了一个编码器框架，用于在视网膜图像中对血管进行分割，该框架依赖于训练期间多个图像尺度的大规模斑块。三个底面图像数据集的实验表明，这种方法可实现最新的结果，并且可以使用简单有效的完全跨斜线网络实现，参数计数小于80万。此外，我们表明，该框架（称为Vlight）避免过度适应特定的训练图像，并在不同的数据集中很好地概括了它，这使其非常适合在需要鲁棒性，准确性以及需要高分辨率底面图像的推理时间的现实应用程序中。
We propose an encoder-decoder framework for the segmentation of blood vessels in retinal images that relies on the extraction of large-scale patches at multiple image-scales during training. Experiments on three fundus image datasets demonstrate that this approach achieves state-of-the-art results and can be implemented using a simple and efficient fully-convolutional network with a parameter count of less than 0.8M. Furthermore, we show that this framework - called VLight - avoids overfitting to specific training images and generalizes well across different datasets, which makes it highly suitable for real-world applications where robustness, accuracy as well as low inference time on high-resolution fundus images is required.
