Patellar segmentation from 3D magnetic resonance images using guided recursive ray-tracing for edge pattern detection

Ruida Cheng; Jennifer N. Jackson; Evan S. McCreedy; William Gandler; J. J. F. A. Eijkenboom; M. van Middelkoop; Matthew J. McAuliffe; Frances T. Sheehan

doi:10.1117/12.2217232

29 March 2016 Patellar segmentation from 3D magnetic resonance images using guided recursive ray-tracing for edge pattern detection

Ruida Cheng, Jennifer N. Jackson, Evan S. McCreedy, William Gandler, J. J. F. A. Eijkenboom, M. van Middelkoop, Matthew J. McAuliffe, Frances T. Sheehan

Author Affiliations +

Proceedings Volume 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging; 97880C (2016) https://doi.org/10.1117/12.2217232
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

The paper presents an automatic segmentation methodology for the patellar bone, based on 3D gradient recalled echo and gradient recalled echo with fat suppression magnetic resonance images. Constricted search space outlines are incorporated into recursive ray-tracing to segment the outer cortical bone. A statistical analysis based on the dependence of information in adjacent slices is used to limit the search in each image to between an outer and inner search region. A section based recursive ray-tracing mechanism is used to skip inner noise regions and detect the edge boundary. The proposed method achieves higher segmentation accuracy (0.23mm) than the current state-of-the-art methods with the average dice similarity coefficient of 96.0% (SD 1.3%) agreement between the auto-segmentation and ground truth surfaces.

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Ruida Cheng, Jennifer N. Jackson, Evan S. McCreedy, William Gandler, J. J. F. A. Eijkenboom, M. van Middelkoop, Matthew J. McAuliffe, and Frances T. Sheehan "Patellar segmentation from 3D magnetic resonance images using guided recursive ray-tracing for edge pattern detection", Proc. SPIE 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, 97880C (29 March 2016); https://doi.org/10.1117/12.2217232

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