Zou Zheng, Liao Sheng-Hui, Luo San-Ding, Liu Qing, Liu Shi-Jian
School of Information Science and Engineering, Central South University, Changsha, Hunan, China.
School of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China.
Comput Methods Programs Biomed. 2017 May;143:171-184. doi: 10.1016/j.cmpb.2017.03.005. Epub 2017 Mar 3.
Segmentation of the femur from the hip joint in computed tomography (CT) is an important preliminary step in hip surgery planning and simulation. However, this is a time-consuming and challenging task due to the weak boundary, the varying topology of the hip joint, and the extremely narrow or blurred space between the femoral head and the acetabulum. To address these problems, this study proposed a semi-automatic segmentation framework based on harmonic fields for accurate segmentation.
The proposed method comprises three steps. First, with high-level information provided by the user, shape information provided by neighboring slices as well as the statistical information in the mask, a region selection method is proposed to effectively locate joint space for the harmonic field. Second, incorporated with an improved gradient, the harmonic field is used to adaptively extract a curve as the barrier that separates the femoral head from the acetabulum accurately. Third, a divide and conquer segmentation strategy based on the harmonic barrier is used to combine the femoral head part and body part as the final segmentation result.
We have tested 40 hips with considerately narrow or disappeared joint spaces. The experimental results are evaluated based on Jaccard, Dice, directional cut discrepancy (DCD) and receiver operating characteristic (ROC), and we achieve the higher Jaccard of 84.02%, Dice of 85.96%, area under curve (AUC) of 89.3%, and the lower error with DCD of 0.52mm. The effective ratio of our method is 79.1% even for cases with severe malformation. The results show that our method performs best in terms of effectiveness and accuracy on the whole data set.
The proposed method is efficient to segment femurs with narrow joint space. The accurate segmentation results can assist the physicians for osteoarthritis diagnosis in future.
在计算机断层扫描（CT）中从髋关节分割出股骨是髋关节手术规划与模拟的重要前期步骤。然而，由于边界模糊、髋关节拓扑结构多变以及股骨头与髋臼之间的间隙极其狭窄或模糊，这是一项耗时且具有挑战性的任务。为解决这些问题，本研究提出了一种基于调和场的半自动分割框架以实现精确分割。
所提出的方法包括三个步骤。首先，利用用户提供的高级信息、相邻切片提供的形状信息以及掩码中的统计信息，提出一种区域选择方法以有效定位调和场的关节空间。其次，结合改进的梯度，利用调和场自适应地提取一条曲线作为准确分离股骨头与髋臼的边界。第三，基于调和边界采用分而治之的分割策略将股骨头部分和主体部分组合作为最终分割结果。
我们对40个关节间隙相当狭窄或消失的髋关节进行了测试。实验结果基于杰卡德指数、骰子系数、方向切割差异（DCD）和接收者操作特征（ROC）进行评估，我们实现了84.02%的较高杰卡德指数、85.96%的骰子系数、89.3%的曲线下面积（AUC）以及DCD为0.52mm的较低误差。即使对于严重畸形的病例，我们方法的有效率也为79.1%。结果表明，我们的方法在整个数据集的有效性和准确性方面表现最佳。
所提出的方法对于分割关节间隙狭窄的股骨是有效的。准确的分割结果可为未来医生进行骨关节炎诊断提供帮助。
