Dynamic computerized tomography (4D-CT) enables detailed analysis of musculoskeletal (MSK) joint motion. Estimating useful kinematic information from these images requires a registration of the obtained images. This study proposes a point-based registration method utilizing pre-segmented 4D-CT knee joint images to generate point clouds, employing 3D local deep descriptors (DIPs) encoded via a pre-trained PointNet-based deep neural network. We ask whether a network trained on indoor and outdoor datasets can effectively generalize to anatomical structures. The method was compared to traditional intensity-based registration using Target Registration Error (TRE) as a metric. Our evaluation involved registrations from different subjects, focusing on various anatomical structures of the knee, including the femur, tibia, and patella. The mean TRE for the intensity-based method was 2.29 ± 0.80 mm, while the point-based method achieved a mean TRE of 2.26 ± 0.73 mm. These results indicate that the point-based method offers comparable accuracy to intensity-based methods while reducing computational time. Although both methods require sequential registration across all timestamps, the point-based approach avoids the failures with distant timestamps encountered by the intensity-based method, which requires proper initialization. Additionally, the proposed method provides reliable extraction of kinematic parameters which have potential in understanding joint motion and MSK disorders.
Mekhzoum, H, Keelson, B, Scheerlinck, T & Vandemeulebroucke, J 2024, Towards Point Cloud-Based Medical Image Registration for Dynamic 4D-CT Imaging. in Shape in Medical Imaging: International Workshop, ShapeMI 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings. 1 edn, vol. 15275, Springer, Cham, Switzerland, pp. 205-223. https://doi.org/10.1007/978-3-031-75291-9_16
Mekhzoum, H., Keelson, B., Scheerlinck, T., & Vandemeulebroucke, J. (2024). Towards Point Cloud-Based Medical Image Registration for Dynamic 4D-CT Imaging. In Shape in Medical Imaging: International Workshop, ShapeMI 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings (1 ed., Vol. 15275, pp. 205-223). Springer, Cham. https://doi.org/10.1007/978-3-031-75291-9_16
@inbook{796f81cb024b49c0b2cc612d31514baf,
title = "Towards Point Cloud-Based Medical Image Registration for Dynamic 4D-CT Imaging",
abstract = "Dynamic computerized tomography (4D-CT) enables detailed analysis of musculoskeletal (MSK) joint motion. Estimating useful kinematic information from these images requires a registration of the obtained images. This study proposes a point-based registration method utilizing pre-segmented 4D-CT knee joint images to generate point clouds, employing 3D local deep descriptors (DIPs) encoded via a pre-trained PointNet-based deep neural network. We ask whether a network trained on indoor and outdoor datasets can effectively generalize to anatomical structures. The method was compared to traditional intensity-based registration using Target Registration Error (TRE) as a metric. Our evaluation involved registrations from different subjects, focusing on various anatomical structures of the knee, including the femur, tibia, and patella. The mean TRE for the intensity-based method was 2.29 ± 0.80 mm, while the point-based method achieved a mean TRE of 2.26 ± 0.73 mm. These results indicate that the point-based method offers comparable accuracy to intensity-based methods while reducing computational time. Although both methods require sequential registration across all timestamps, the point-based approach avoids the failures with distant timestamps encountered by the intensity-based method, which requires proper initialization. Additionally, the proposed method provides reliable extraction of kinematic parameters which have potential in understanding joint motion and MSK disorders.",
author = "Hamza Mekhzoum and Benyameen Keelson and Thierry Scheerlinck and Jef Vandemeulebroucke",
year = "2024",
month = oct,
day = "26",
doi = "10.1007/978-3-031-75291-9_16",
language = "English",
isbn = "978-3-031-75290-2",
volume = "15275",
pages = "205--223",
booktitle = "Shape in Medical Imaging",
publisher = "Springer, Cham",
edition = "1",
}