Annotating 3D medical images demands expert knowledge and is time-consuming. As a result, semi-supervised learning (SSL) approaches have gained significant interest in 3D medical image segmentation. The significant size differences among various organs in the human body lead to imbalanced class distribution, which is a major challenge in the real-world application of these SSL approaches. To address this issue, we develop a novel Shape Transformation driven by Active Contour (STAC), that enlarges smaller organs to alleviate imbalanced class distribution across different organs. Inspired by curve evolution theory in active contour methods, STAC employs a signed distance function (SDF) as the level set function, to implicitly represent the shape of organs, and deforms voxels in the direction of the steepest descent of SDF (i.e., the normal vector). To ensure that the voxels far from expansion organs remain unchanged, we design an SDF-based weight function to control the degree of deformation for each voxel. We then use STAC as a data-augmentation process during the training stage. Experimental results on two benchmark datasets demonstrate that the proposed method significantly outperforms some state-of-the-art methods. Source code is publicly available at https://github.com/GuGuLL123/STAC.

翻译：三维医学图像的标注需要专业知识且耗时费力。因此，半监督学习方法在三维医学图像分割领域引起了广泛关注。人体内不同器官的尺寸差异显著，导致类别分布不平衡，这是这些半监督学习方法在实际应用中的主要挑战。为解决这一问题，我们提出了一种新颖的基于主动轮廓的形状变换方法，该方法通过扩大较小器官来缓解不同器官间的类别不平衡问题。受主动轮廓方法中曲线演化理论的启发，STAC采用符号距离函数作为水平集函数，以隐式表示器官形状，并沿SDF最速下降方向（即法向量）对体素进行形变。为确保远离扩张器官的体素保持不变，我们设计了一个基于SDF的权重函数来控制每个体素的形变程度。随后，我们在训练阶段将STAC作为数据增强流程使用。在两个基准数据集上的实验结果表明，所提方法显著优于多种先进方法。源代码已公开于 https://github.com/GuGuLL123/STAC。