ArthroVision, 1871 Sherbrooke Street East, Montreal, QC H2K 1B6, Canada.
Med Biol Eng Comput. 2011 Dec;49(12):1413-24. doi: 10.1007/s11517-011-0838-8. Epub 2011 Oct 29.
This study aimed at developing a fully automated bone segmentation method for the human knee (femur and tibia) from magnetic resonance (MR) images. MR imaging was acquired on a whole body 1.5T scanner with a gradient echo fat suppressed sequence using an extremity coil. The method was based on the Ray Casting technique which relies on the decomposition of the MR images into multiple surface layers to localize the boundaries of the bones and several partial segmentation objects being automatically merged to obtain the final complete segmentation of the bones. Validation analyses were performed on 161 MR images from knee osteoarthritis patients, comparing the developed fully automated to a validated semi-automated segmentation method, using the average surface distance (ASD), volume correlation coefficient, and Dice similarity coefficient (DSC). For both femur and tibia, respectively, data showed excellent bone surface ASD (0.50 ± 0.12 mm; 0.37 ± 0.09 mm), average oriented distance between bone surfaces within the cartilage domain (0.02 ± 0.07 mm; -0.05 ± 0.10 mm), and bone volume DSC (0.94 ± 0.05; 0.92 ± 0.07). This newly developed fully automated bone segmentation method will enable large scale studies to be conducted within shorter time durations, as well as increase stability in the reading of pathological bone.
本研究旨在开发一种全自动的膝关节（股骨和胫骨）骨骼分割方法，该方法基于磁共振（MR）图像。MR 成像在全身 1.5T 扫描仪上进行，使用梯度回波脂肪抑制序列和肢体线圈。该方法基于光线投射技术，该技术依赖于将 MR 图像分解为多个表面层，以定位骨骼的边界，并自动合并几个部分分割对象，以获得骨骼的最终完整分割。在 161 例膝关节骨关节炎患者的 MR 图像上进行了验证分析，将开发的全自动方法与经过验证的半自动分割方法进行了比较，使用平均表面距离（ASD）、体积相关系数和 Dice 相似系数（DSC）。对于股骨和胫骨，分别得到了优秀的骨骼表面 ASD（0.50±0.12mm；0.37±0.09mm）、软骨区域内骨骼表面之间的平均定向距离（0.02±0.07mm；-0.05±0.10mm）和骨体积 DSC（0.94±0.05；0.92±0.07）。这种新开发的全自动骨骼分割方法将能够在更短的时间内进行大规模研究，并提高对病理性骨骼的阅读稳定性。
