Abstract
Standardized image rotation is essential to improve reading performance in interventional X-ray imaging. To minimize user interaction and streamline the 2D imaging workflow, we present a new automated image rotation method. Image rotation can follow two steps: First, an anatomy specific centerline image is predicted which depicts the desired anatomical axis to be aligned vertically after rotation. In a second step, the necessary rotation angle is calculated from the orientation of the predicted line image. We propose an end-to-end trainable model with the Hough transform (HT) and a differentiable spatial-to-angular transform (DSAT) embedded as known operators. This model allows to robustly regress a rotation angle while maintaining an explainable inner structure and allows to be trained with both a centerline segmentation and angle regression loss. The proposed method is compared to a Hu moments-based method on anterior-posterior X-ray images of spine, knee, and wrist. For the wrist images, the HT based method reduces the mean absolute angular error (MAE) from \({}{9.28^{\circ }}{}\) using the Hu moments-based method to \({}{3.54^{\circ }}{}\). Similar results for the spinal and knee images can be reported. Furthermore, a large improvement of the 90\(^\textrm{th}\) percentile of absolute angular error by a factor of 3 indicates a better robustness and reduction of outliers for the proposed method.
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Bachmaier, M., Rohleder, M., Swartman, B., Privalov, M., Maier, A., Kunze, H. (2023). Robust Hough and Spatial-To-Angular Transform Based Rotation Estimation for Orthopedic X-Ray Images. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14226. Springer, Cham. https://doi.org/10.1007/978-3-031-43990-2_42
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