Magnetic Resonance Images Segmentation of Synovium Based on Dense-UNet++

Abstract

To further improve the segmentation accuracy, robustness, and training efficiency of existing articular synovium segmentation algorithms, a new deep learning network based on Dense-UNet++ was proposed. First, we inserted the DenseNet module into the UNet++ network, then applied the Swish activation function to train the model. The network was trained through 14 512 synovial images augmented from 1 036 synovial images, and tested through 68 images. The average accuracy of the model reached 0.819 9 for dice similarity coefficient (DSC), and 0.927 9 for intersection over union (IOU) index. Compared with UNet, ResUNet and VGG-UNet++, DSC coefficient and IOU index were improved, and DSC oscillation coefficient reduced. In addition, when applied in the same synovial image set and using the same network structure, the Swish function can help improve the accuracy of segmentation compared with the ReLu function. The experimental results show that the proposed algorithm performs better in segmenting articular synovium and may assist doctors in disease diagnosis.

Keywords

• magnetic resonance imaging (mri)
• deep learning
• medical image segmentation
• synovitis