Most data-driven models for medical image analysis rely on universal augmentations to improve performance. Experimental evidence has confirmed their effectiveness, but the unclear mechanism underlying them poses a barrier to the widespread acceptance and trust in such methods within the medical community. We revisit and acknowledge the unique characteristics of medical images apart from traditional digital images, and consequently, proposed a medical-specific augmentation algorithm that is more elastic and aligns well with radiology scan procedure. The method performs piecewise affine with sinusoidal distorted ray according to radius on polar coordinates, thus simulating uncertain postures of human lying flat on the scanning table. Our method could generate human visceral distribution without affecting the fundamental relative position on axial plane. Two non-adaptive algorithms, namely Meta-based Scan Table Removal and Similarity-Guided Parameter Search, are introduced to bolster robustness of our augmentation method. Experiments show our method improves accuracy across multiple famous segmentation frameworks without requiring more data samples. Our preview code is available in: https://github.com/MGAMZ/PSBPD.

翻译：大多数用于医学图像分析的数据驱动模型依赖通用增强技术来提升性能。实验证据已证实其有效性，但背后机制的不明确性阻碍了此类方法在医学界的广泛接受与信任。我们重新审视并承认医学图像区别于传统数字图像的独特性，据此提出一种更具弹性且符合放射扫描流程的医学专用增强算法。该方法在极坐标系中根据半径对射线进行分段仿射与正弦畸变，从而模拟人体平卧于扫描床时的不确定姿态。我们的方法能够生成不影响轴向平面基本相对位置的人体内脏分布。为增强本方法的鲁棒性，我们引入了两种非自适应算法：基于元数据的扫描床移除算法与相似度引导参数搜索算法。实验表明，本方法能在无需额外数据样本的情况下，提升多种主流分割框架的精度。预览代码已发布于：https://github.com/MGAMZ/PSBPD。