Intelligent localization and quantitative evaluation of anterior talofibular ligament injury using magnetic resonance imaging of ankle

Abstract

Background: There is a high incidence of injury to the lateral ligament of the ankle in daily living and sports activities. The anterior talofibular ligament (ATFL) is the most frequent types of ankle injuries. It is of great clinical significance to achieve intelligent localization and injury evaluation of ATFL due to its vulnerability.

Methods: According to the specific characteristics of bones in different slices, the key slice was extracted by image segmentation and characteristic analysis. Then, the talus and fibula in the key slice were segmented by distance regularized level set evolution (DRLSE), and the curvature of their contour pixels was calculated to find useful feature points including the neck of talus, the inner edge of fibula, and the outer edge of fibula. ATFL area can be located using these feature points so as to quantify its first-order gray features and second-order texture features. Support vector machine (SVM) was performed for evaluation of ATFL injury.

Results: Data were collected retrospectively from 158 patients who underwent MRI, and were divided into normal (68) and tear (90) group. The positioning accuracy and Dice coefficient were used to measure the performance of ATFL localization, and the mean values are 87.7% and 77.1%, respectively, which is helpful for the following feature extraction. SVM gave a good prediction ability with accuracy of 93.8%, sensitivity of 88.9%, specificity of 100%, precision of 100%, and F1 score of 94.2% in the test set.

Conclusion: Experimental results indicate that the proposed method is reliable in diagnosing ATFL injury. This study may provide a potentially viable method for aided clinical diagnoses of some ligament injury.

Keywords: Anterior talofibular ligament; DRLSE; Intelligent localization of ATFL; Magnetic resonance imaging; Quantitative evaluation of ATFL injury.

Fig. 1

The structure diagram of this study

Fig. 2

The key slice extraction from cross-sectional T2 weighted images with fat suppression

Fig. 3

Schematic diagram of ATFL localization process. (a. the original image, b. the prepocessed image after guided filter and CLAHE, c. the initial zero level set of the talus, d. the talus contour is obtained by DRLSE, e. the initial zero level set of the fibula is determined by the contours of foreground and talus, f. the initial zero level set of the fibula, g. the fibula contour is obtained by DRLSE, h. feature points extraction of ATFL, and i. the final result of ATFL localization.)

Fig. 4

Curvature calculation method of discrete points based on chord length

Fig. 5

The gray histograms of the ATFL area for normal, partial tear and complete tear

Fig. 6

The explained variance ratio of different principal components