From Lines to Shapes: Geometric-Constrained Segmentation of X-Ray Collimators via Hough Transform

• URL: http://arxiv.org/abs/2509.04437v1
• Date: Thu, 04 Sep 2025 17:53:45 GMT
• Title: From Lines to Shapes: Geometric-Constrained Segmentation of X-Ray Collimators via Hough Transform
• Authors: Benjamin El-Zein, Dominik Eckert, Andreas Fieselmann, Christopher Syben, Ludwig Ritschl, Steffen Kappler, Sebastian Stober,
• Abstract summary: Collimation in X-ray imaging restricts exposure to the region-of-interest (ROI) and minimizes the radiation dose applied to the patient.<n>We introduce a deep learning-based segmentation that is inherently constrained to its geometry.<n>We demonstrate robust reconstruction of collimated regions achieving median Hausdorff distances of 4.3-5.0mm on diverse test sets of real Xray images.
• Score: 2.289908748072682
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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