直觉式轴向增强：基于极坐标正弦分段畸变的医学切片分割方法
Intuitive Axial Augmentation Using Polar-Sine-Based Piecewise Distortion for Medical Slice-Wise Segmentation
AI总结：
针对医学图像分割，提出一种基于极坐标正弦分段畸变的轴向增强方法，模拟人体躺卧姿态，提升多种分割框架在两种模态下的准确性，且设计直观易用。
摘要：
大多数用于医学图像分析的数据驱动模型依赖通用增强方法来提高准确性。实验证据已证实其有效性，但其背后的机制尚不明确，这阻碍了医学界对此类方法的广泛接受和信任。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特征，据此提出了一种更具弹性且与放射学扫描流程高度契合的医学专用增强算法。该方法在极坐标下根据半径执行分段仿射变换，并叠加正弦畸变射线，从而模拟人体平躺于扫描台上时的不确定姿态。我们的方法能够生成人体内脏分布，同时不影响轴位平面上的基本相对位置。为增强增强方法的鲁棒性，我们引入了两种非自适应算法，即基于元学习的扫描台移除和相似性引导的参数搜索。与其他方法相比，我们的方法以直观设计和易于医学专业人员理解而著称，从而增强了其在临床场景中的适用性。实验表明，我们的方法在多种知名分割框架下，无需更多数据样本即可提升两种模态的准确性。我们的预览代码可在 https://github.com/MGAMZ/PSBPD 获取。
英文摘要：
Most data-driven models for medical image analysis rely on universal augmentations to improve accuracy. 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. In contrast to other methodologies, our method is highlighted for its intuitive design and ease of understanding for medical professionals, thereby enhancing its applicability in clinical scenarios. Experiments show our method improves accuracy with two modality across multiple famous segmentation frameworks without requiring more data samples. Our preview code is available in: https://github.com/MGAMZ/PSBPD.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
