Accurate retinal vessel segmentation is a crucial step in the quantitative
assessment of retinal vasculature, which is needed for the early detection of
retinal diseases and other conditions. Numerous studies have been conducted to
tackle the problem of segmenting vessels automatically using a pixel-wise
classification approach. The common practice of creating ground truth labels is
to categorize pixels as foreground and background. This approach is, however,
biased, and it ignores the uncertainty of a human annotator when it comes to
annotating e.g. thin vessels. In this work, we propose a simple and effective
method that casts the retinal image segmentation task as an image-level
regression. For this purpose, we first introduce a novel Segmentation
Annotation Uncertainty-Aware (SAUNA) transform, which adds pixel uncertainty to
the ground truth using the pixel's closeness to the annotation boundary and
vessel thickness. To train our model with soft labels, we generalize the
earlier proposed Jaccard metric loss to arbitrary hypercubes, which is a second
contribution of this work. The proposed SAUNA transform and the new theoretical
results allow us to directly train a standard U-Net-like architecture at the
image level, outperforming all recently published methods. We conduct thorough
experiments and compare our method to a diverse set of baselines across 5
retinal image datasets. Our implementation is available at
https://github.com/Oulu-IMEDS/SAUNA.

将视网膜图像分割任务视为图像级回归，通过引入 SAUNA 变换和广义的 Jaccard 度量损失训练 U-Net 模型，在 5 个视网膜图像数据集上优于最近发表的方法。