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. Eijkenboom, Marienke van Middelkoop, Matthew McAuliffe, Frances T. Sheehan · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2016
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.