用形状变形提升小器官分割，解决医学图像类别不平衡问题
Shape Transformation Driven by Active Contour for Class-Imbalanced Semi-Supervised Medical Image Segmentation
- 基于活动轮廓理论，用距离函数动态调整小器官形状
- 在两个基准数据集上显著优于现有先进方法
- 适合处理器官大小差异大的3D医学图像分割任务
标注3D医学图像需专业技能且耗时，因此半监督学习（SSL）在3D医学图像分割中备受关注。人体内各器官尺寸差异大，导致类别分布不均，成为实际应用中的主要挑战。为此，我们提出一种由活动轮廓驱动的形状变换方法（STAC），通过放大较小器官缓解器官间的类别不平衡。受活动轮廓中曲线演化理论启发，STAC采用符号距离函数（SDF）作为水平集函数，隐式表示器官形状，并沿SDF梯度下降方向（即法向量方向）变形体素。为确保远离扩张器官的体素不受影响，设计了基于SDF的权重函数控制每个体素的形变程度。随后将STAC作为训练阶段的数据增强手段。在两个基准数据集上的实验结果表明，该方法显著优于若干先进方法。源代码已公开于 https://github.com/GuGuLL123/STAC。
原文摘要 · Abstract (English)
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.
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