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Robust Hough and Spatial-To-Angular Transform Based Rotation Estimation for Orthopedic X-Ray Images

Bachmaier M, Rohleder M, Swartman B, Privalov M, Maier A, Kunze H (2023)

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Publication Type: Conference contribution

Publication year: 2023

Journal

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 14226 LNCS

Pages Range: 446-455

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Vancouver, BC, CAN

ISBN: 9783031439896

DOI: 10.1007/978-3-031-43990-2_42

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 ^∘ using the Hu moments-based method to 3. 54 ^∘. Similar results for the spinal and knee images can be reported. Furthermore, a large improvement of the 90 ^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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How to cite

APA:

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 Hayit Greenspan, Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 446-455). Vancouver, BC, CAN: Springer Science and Business Media Deutschland GmbH.

MLA:

Bachmaier, Magdalena, et al. "Robust Hough and Spatial-To-Angular Transform Based Rotation Estimation for Orthopedic X-Ray Images." Proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, Vancouver, BC, CAN Ed. Hayit Greenspan, Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor, Springer Science and Business Media Deutschland GmbH, 2023. 446-455.

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