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.
Phys Med Biol. 2013 Oct 21;58(20):7375-90. doi: 10.1088/0031-9155/58/20/7375. Epub 2013 Sep 27.
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.
从磁共振（MR）图像中准确分割髋关节区域的骨骼可以提供定量数据，用于检查病理解剖条件，例如从股骨髋臼撞击症到不同阶段的骨关节炎，以监测骨骼和相关软骨形态。我们评估了两种最先进的方法（多图谱和主动形状模型（ASM）方法）在双侧 MR 图像上进行髋关节区域（股骨近端和骨盆骨）的自动 3D 骨骼分割。从 30 名志愿者中采集了双侧髋关节的 3T MR 图像。所有受试者均采集水激发双回波稳态（FOV 38.6×24.1cm，矩阵 576×360，厚度 0.61mm）的图像序列，8 名受试者中的一部分采集多回波数据图像组合（FOV 37.6×23.5cm，矩阵 576×360，厚度 0.70mm）。在手动分割股骨（头颈、近端干）和骨盆（髂骨+坐骨+耻骨）骨骼后，通过两种方法进行自动骨骼分割：（1）多图谱分割，包括非刚性配准，以及（2）基于高级 ASM 的方案。手动分割股骨和骨盆骨骼的组内和组间可靠性 Dice 相似系数（DSC）分别为（0.970，0.963）和（0.971，0.965）。与手动数据相比，使用自动多图谱和基于 ASM 的方法的股骨和骨盆骨体积的平均 DSC 值分别为（0.950，0.922）和（0.946，0.917）。两种方法都提供了准确（高 DSC 值）的分割结果；值得注意的是，ASM 数据的计算时间明显更短（12 分钟对 10 小时）。两种自动算法都为髋关节区域的基于 MR 的测量提供了准确的 3D 骨骼体积描述。高效计算的基于 ASM 的方法更适合未来的临床应用，例如提取骨软骨界面，用于潜在的软骨分割。
