Automatic Segmentation of Shoulder Joint in MRI Using Patch-Based and Fully Convolutional Networks

Two deep learning networks, patch-based and fully convolutional networks, are employed for automated detection and segmentation of shoulder joint structure on MRI. First, four segmentation models are build including three U-Net based models (glenoid segmentation model, humeral head segmentation model, glenoid and humeral head as a whole segmentation model) and one patch-based adjusted AlexNet (AANet) segmentation model. Then the four segmentation models are used to get the candidate bone regions from which the correct locations and regions of glenoid and humeral head are obtained by voting. Last, AANet model is further used to segment the edge of the bone with accuracy at the pixel level. From the experimental results, Dice Coefficient, Positive Predicted Value (PPV) and Sensitivity average accuracy are 0.92 ± 0.02,0.96 ± 0.03 and 0.94 ± 0.02 respectively. Note that our framework is also generic enough to be applied to the precise segmentation of specific organs and tissues in CT and MRI under small sample data.