Abstract:

Objectives: This study aimed to evaluate the effectiveness of Mask Region-Based Convolutional Neural Network (R-CNN) in humerus and scapula segmentation. Patients and methods: The study included 665 axial proton density (PD)-weighted magnetic resonance images of 665 consecutive shoulder instability patients (412 males, 253 females; mean age: 27±5.2 years; range, 18 to 42 years) between January 2011 and December 2014. Mask R-CNN was used to automatically segment humerus and scapula regions simultaneously. Segmentation success of Mask R-CNN was compared to the manual segmentation results of an orthopedic surgeon. Statistical evaluation was done with the Dice coefficient and the mean average precision) score. According to the humeral head structure three groups were generated: the healthy humeral head group, the edematous humeral head group, and the Hill-Sachs group (humeral heads with Hill-Sachs lesions). Results: In the test images, 81 humeral heads were healthy, 100 were edematous, and 38 had a Hill-Sachs lesion. According to the Dice metric, the overall success rate of Mask R-CNN configuration was 96.47 and 93.87% for the segmentation of the humeral head and scapula, respectively, and 95.86 and 92.35% for an intersection over union of 0.5 according to the mean average precision. According to the Dice metric, the segmentation success of the humerus and scapula of the healthy group was 94.58 and 97.42%, the segmentation success of the edematous humerus group was 93.56 and 96.53%, and the segmentation success of the Hill-Sachs group was 93.47 to 95.48%. The segmentation success of scapula in the case of discontinuity was 92.86% according to Dice metric. Conclusion: Mask R-CNN-based humerus and scapula segmentation provided promising results compared to manual segmentation of an expert. Mask R-CNN overcomes the problem of discontinuous edges and Rician noise in axial PD-weighted shoulder magnetic resonance imaging.

Keywords: