Since segmentation labeling is usually time-consuming and annotating medical images requires professional expertise, it is laborious to obtain a large-scale, high-quality annotated segmentation dataset. We propose a novel weakly- and semi-supervised framework named SOUSA (Segmentation Only Uses Sparse Annotations), aiming at learning from a small set of sparse annotated data and a large amount of unlabeled data. The proposed framework contains a teacher model and a student model. The student model is weakly supervised by scribbles and a Geodesic distance map derived from scribbles. Meanwhile, a large amount of unlabeled data with various perturbations are fed to student and teacher models. The consistency of their output predictions is imposed by Mean Square Error (MSE) loss and a carefully designed Multi-angle Projection Reconstruction (MPR) loss. Extensive experiments are conducted to demonstrate the robustness and generalization ability of our proposed method. Results show that our method outperforms weakly- and semi-supervised state-of-the-art methods on multiple datasets. Furthermore, our method achieves a competitive performance with some fully supervised methods with dense annotation when the size of the dataset is limited.

由于分割标注通常很耗时，并且注释医学图像需要专业知识，因此获取大规模、高质量的注释分割数据集非常费力。我们提出了一种名为 SOUSA（Segmentation Only Uses Sparse Annotations）的新型弱监督和半监督框架，旨在从一小部分稀疏注释数据和大量未标记数据中学习。所提出的框架包含一个教师模型和一个学生模型。学生模型受到涂鸦和从涂鸦导出的测地距离图的弱监督。同时，将大量具有各种扰动的未标记数据提供给学生和教师模型。它们的输出预测的一致性是由均方误差 (MSE) 损失和精心设计的多角度投影重建 (MPR) 损失决定的。进行了广泛的实验以证明我们提出的方法的鲁棒性和泛化能力。结果表明，我们的方法在多个数据集上优于弱监督和半监督的最先进方法。此外，当数据集的大小有限时，我们的方法与一些具有密集注释的完全监督方法取得了竞争性能。