Oral Presentation at the European Congress of Radiology, Vienna, 2019

Purpose

We propose a novel ensemble-approach of traditional image processing combined with deep learning to detect ACL tear on knee MRI.

Methods and Materials

FSPD knee MRI images of 66 patients (16 normal, 50 with full-or partial-thickness ACL tears) done on a 3.0 Tesla MRI were extracted, the intensity values of MRI images were standardized by histogram matching followed by intensity normalization image pre-processing techniques. Automatic delineation of tibia, femur and bounding box of the ACL bundle was achieved by a three-dimensional CNN implemented in the research version of HealthSuite Insights (Philips HealthTech). A total of 88 features comprising of first order statistics and texture measures were computed from Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GRLM) of the image volume within the ACL bounding box. Feature subset selection was performed by 2 sample t-test with statistical significance (p < 0.05).

Results

The performance of the deep learning approach for segmentation of femur and tibia is evaluated on 20 randomly selected MRI datasets. The ground truth was created by manual segmentation of femur and tibia by a MSK radiologist. The dice score was 0.91 +/- 0.084. The performance of the predictive model for discrimination of ACL tear was assessed by k-fold crossvalidation and the accuracy, sensitivity, and specificity were 89.5%, 93.3%, and 81% respectively.

Conclusion

The proposed machine learning technique gave good performance in the delineation of knee anatomical structures and detection of ACL tears in the knee MRI.