用物理模型生成逼真X射线准直器阴影，解决数据少难题
A Realistic Collimated X-Ray Image Simulation Pipeline
- 基于物理规律模拟准直器阴影，含随机形状位置与散射光
- 生成数据可提升深度学习模型在真实数据上的泛化能力
- 适合做医疗影像缺陷检测的算法研究者使用
在探测器位置信息不可靠或缺失的情况下，准直器检测仍是X射线系统中的难题。本文提出一种基于物理原理的图像处理流水线，用于模拟X射线图像中准直器阴影的特征。通过随机生成准直器形状与位置标签，整合散射辐射模拟和泊松噪声，该流水线可扩展有限的数据集，用于训练深度神经网络。我们通过定性与定量对比真实准直器阴影验证了该方法的有效性。结果表明，在深度学习框架中使用模拟数据不仅能作为真实数据的良好替代，还能提升模型在真实世界数据上的泛化性能。
原文摘要 · Abstract (English)
Collimator detection remains a challenging task in X-ray systems with unreliable or non-available information about the detectors position relative to the source. This paper presents a physically motivated image processing pipeline for simulating the characteristics of collimator shadows in X-ray images. By generating randomized labels for collimator shapes and locations, incorporating scattered radiation simulation, and including Poisson noise, the pipeline enables the expansion of limited datasets for training deep neural networks. We validate the proposed pipeline by a qualitative and quantitative comparison against real collimator shadows. Furthermore, it is demonstrated that utilizing simulated data within our deep learning framework not only serves as a suitable substitute for actual collimators but also enhances the generalization performance when applied to real-world data.
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