  Weakly-supervised learning (WSL) has been proposed to alleviate the conflict
between data annotation cost and model performance through employing
sparsely-grained (i.e., point-, box-, scribble-wise) supervision and has shown
promising performance, particularly in the image segmentation field. However,
it is still a very challenging task due to the limited supervision, especially
when only a small number of labeled samples are available. Additionally, almost
all existing WSL segmentation methods are designed for star-convex structures
which are very different from curvilinear structures such as vessels and
nerves. In this paper, we propose a novel sparsely annotated segmentation
framework for curvilinear structures, named YoloCurvSeg. A very essential
component of YoloCurvSeg is image synthesis. Specifically, a background
generator delivers image backgrounds that closely match the real distributions
through inpainting dilated skeletons. The extracted backgrounds are then
combined with randomly emulated curves generated by a Space Colonization
Algorithm-based foreground generator and through a multilayer patch-wise
contrastive learning synthesizer. In this way, a synthetic dataset with both
images and curve segmentation labels is obtained, at the cost of only one or a
few noisy skeleton annotations. Finally, a segmenter is trained with the
generated dataset and possibly an unlabeled dataset. The proposed YoloCurvSeg
is evaluated on four publicly available datasets (OCTA500, CORN, DRIVE and
CHASEDB1) and the results show that YoloCurvSeg outperforms state-of-the-art
WSL segmentation methods by large margins. With only one noisy skeleton
annotation (respectively 0.14\%, 0.03\%, 1.40\%, and 0.65\% of the full
annotation), YoloCurvSeg achieves more than 97\% of the fully-supervised
performance on each dataset. Code and datasets will be released at
https://github.com/llmir/YoloCurvSeg.
