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.

翻译：弱监督学习（WSL）通过采用稀疏粒度（如点级、框级、涂鸦级）监督来缓解数据标注成本与模型性能之间的矛盾，并在图像分割领域展现出良好前景。然而，由于监督信息极为有限，尤其在标注样本数量稀少时，该任务仍极具挑战性。此外，现有大多数弱监督分割方法专为星凸结构设计，与血管、神经等曲线结构差异显著。本文提出一种新颖的曲线结构稀疏标注分割框架YoloCurvSeg，其核心组件为图像合成技术。具体而言，背景生成器通过修复膨胀后的骨架生成与真实分布高度匹配的图像背景，随后将提取的背景与基于空间殖民算法的前景生成器随机模拟的曲线相结合，经多层块级对比学习合成器处理，仅需一个或少量噪声骨架标注即可获得包含图像与曲线分割标签的合成数据集。最终利用生成数据集及可能的无标签数据集训练分割器。在四个公开数据集（OCTA500、CORN、DRIVE和CHASEDB1）上的评估结果表明，YoloCurvSeg以极大优势超越现有最优弱监督分割方法。仅需一个噪声骨架标注（分别占完整标注的0.14%、0.03%、1.40%和0.65%），YoloCurvSeg即可在全部数据集上实现超过全监督方法97%的性能。代码与数据集将于https://github.com/llmir/YoloCurvSeg 开源。