Automated bone segmentation from large field of view 3D MR images of the hip joint.
Accurate bone segmentation in the hip joint region from magnetic resonance (MR) images can provide quantitative data for examining pathoanatomical conditions such as femoroacetabular impingement through to varying stages of osteoarthritis to monitor bone and associated cartilage morphometry. We evaluate two state-of-the-art methods (multi-atlas and active shape model (ASM) approaches) on bilateral MR images for automatic 3D bone segmentation in the hip region (proximal femur and innominate bone). Bilateral MR images of the hip joints were acquired at 3T from 30 volunteers. Image sequences included water-excitation dual echo stead state (FOV 38.6 × 24.1 cm, matrix 576 × 360, thickness 0.61 mm) in all subjects and multi-echo data image combination (FOV 37.6 × 23.5 cm, matrix 576 × 360, thickness 0.70 mm) for a subset of eight subjects. Following manual segmentation of femoral (head-neck, proximal-shaft) and innominate (ilium+ischium+pubis) bone, automated bone segmentation proceeded via two approaches: (1) multi-atlas segmentation incorporating non-rigid registration and (2) an advanced ASM-based scheme. Mean inter- and intra-rater reliability Dice's similarity coefficients (DSC) for manual segmentation of femoral and innominate bone were (0.970, 0.963) and (0.971, 0.965). Compared with manual data, mean DSC values for femoral and innominate bone volumes using automated multi-atlas and ASM-based methods were (0.950, 0.922) and (0.946, 0.917), respectively. Both approaches delivered accurate (high DSC values) segmentation results; notably, ASM data were generated in substantially less computational time (12 min versus 10 h). Both automated algorithms provided accurate 3D bone volumetric descriptions for MR-based measures in the hip region. The highly computational efficient ASM-based approach is more likely suitable for future clinical applications such as extracting bone-cartilage interfaces for potential cartilage segmentation.
Australian e-Health Research Centre, CSIRO ICT Centre, Brisbane QLD 4029, Australia. School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, QLD 4027, Australia.
Ying
Y
Xia
Jurgen
J
Fripp
Shekhar S
SS
Chandra
Raphael
R
Schwarz
Craig
C
Engstrom
Stuart
S
Crozier
D000293:Adolescent; D000328:Adult; D001331:Automation; D001842:Bone and Bones / Q000000981:diagnostic imaging; D005260:Female; D006621:Hip Joint / Q000000981:diagnostic imaging; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D010003:Osteoarthritis / Q000000981:diagnostic imaging; D013997:Time Factors; D014057:Tomography, X-Ray Computed; D055815:Young Adult