Yang Zhengyi, Fripp Jurgen, Chandra Shekhar S, Neubert Aleš, Xia Ying, Strudwick Mark, Paproki Anthony, Engstrom Craig, Crozier Stuart
School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia.
Phys Med Biol. 2015 Feb 21;60(4):1441-59. doi: 10.1088/0031-9155/60/4/1441. Epub 2015 Jan 22.
We present a statistical shape model approach for automated segmentation of the proximal humerus and scapula with subsequent bone-cartilage interface (BCI) extraction from 3D magnetic resonance (MR) images of the shoulder region. Manual and automated bone segmentations from shoulder MR examinations from 25 healthy subjects acquired using steady-state free precession sequences were compared with the Dice similarity coefficient (DSC). The mean DSC scores between the manual and automated segmentations of the humerus and scapula bone volumes surrounding the BCI region were 0.926 ± 0.050 and 0.837 ± 0.059, respectively. The mean DSC values obtained for BCI extraction were 0.806 ± 0.133 for the humerus and 0.795 ± 0.117 for the scapula. The current model-based approach successfully provided automated bone segmentation and BCI extraction from MR images of the shoulder. In future work, this framework appears to provide a promising avenue for automated segmentation and quantitative analysis of cartilage in the glenohumeral joint.
我们提出了一种统计形状模型方法，用于对肱骨近端和肩胛骨进行自动分割，并随后从肩部区域的三维磁共振（MR）图像中提取骨软骨界面（BCI）。使用稳态自由进动序列采集的25名健康受试者肩部MR检查的手动和自动骨分割结果，通过骰子相似系数（DSC）进行比较。围绕BCI区域的肱骨和肩胛骨骨体积的手动和自动分割之间的平均DSC分数分别为0.926±0.050和0.837±0.059。肱骨和肩胛骨BCI提取获得的平均DSC值分别为0.806±0.133和0.795±0.117。当前基于模型的方法成功地从肩部MR图像中提供了自动骨分割和BCI提取。在未来的工作中，该框架似乎为盂肱关节软骨的自动分割和定量分析提供了一条有前景的途径。
