用几何约束提升X光准直器阴影分割精度
From Lines to Shapes: Geometric-Constrained Segmentation of X-Ray Collimators via Hough Transform
- 融合可微Hough变换，利用多边形几何先验约束分割
- 实测中对准直区域重建的平均豪斯多夫距离为4.3-5.0mm
- 适合需要高精度图像预处理的医学影像分析场景
X射线成像中的准直可限制照射区域至感兴趣区（ROI），降低患者辐射剂量。检测准直器阴影是数字放射摄影中关键的基于图像的预处理步骤，但散射射线会使边缘模糊，造成挑战。然而，准直形成的阴影具有明显的多边形形状这一先验知识明确存在。为此，我们提出一种基于深度学习的分割方法，其内在地受几何形状约束。通过引入可微的基于Hough变换的网络来检测准直边界，并增强对ROI中心信息的提取能力。推理时，将两项任务的信息结合，生成精细的、线性约束的分割掩码。在多样化的真实X射线图像测试集上，该方法实现了稳健的准直区域重建，中位豪斯多夫距离为4.3-5.0mm。尽管该应用最多涉及四条阴影边界，但本方法在本质上不依赖于特定边数。
原文摘要 · Abstract (English)
Collimation in X-ray imaging restricts exposure to the region-of-interest (ROI) and minimizes the radiation dose applied to the patient. The detection of collimator shadows is an essential image-based preprocessing step in digital radiography posing a challenge when edges get obscured by scattered X-ray radiation. Regardless, the prior knowledge that collimation forms polygonal-shaped shadows is evident. For this reason, we introduce a deep learning-based segmentation that is inherently constrained to its geometry. We achieve this by incorporating a differentiable Hough transform-based network to detect the collimation borders and enhance its capability to extract the information about the ROI center. During inference, we combine the information of both tasks to enable the generation of refined, line-constrained segmentation masks. We demonstrate robust reconstruction of collimated regions achieving median Hausdorff distances of 4.3-5.0mm on diverse test sets of real Xray images. While this application involves at most four shadow borders, our method is not fundamentally limited by a specific number of edges.
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