Semi-automatic segmentation of femur based on harmonic barrier

BACKGROUND AND OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Automatic bone segmentation and bone-cartilage interface extraction for the shoulder joint from magnetic resonance images

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.