Knee Joint Image Segmentation and Model Construction Based on Cascaded Network

Abstract

Electromagnetic simulation using a knee model is the main method for calculating local specific absorption rate (SAR) values of knee joint. To construct a knee model, a cascaded network structure containing two convolutional neural networks, i.e. U-Net, was proposed for segmenting knee magnetic resonance images. The first network segmented tissues with large volume from the whole image, such as muscle and fat, and predicted the position information of cartilage and meniscus based on the segmentation results. The second network segmented tissues with small volume from a smaller sub-image based on the acquired position information to improve accuracy. Both networks adopted focal loss function and their segmentation results were merged to form the model. We evaluated the segmentation results of this method and 4 comparison methods, by quantitative metrics, and constructed separate knee joint models to calculate local SAR values. The results indicate that the cascaded network structure proposed in this paper can construct knee joint models for SAR simulation more accurately.

Keywords

• magnetic resonance imaging (mri)
• local specific absorption rate (sar)
• knee joint
• cascaded network
• focal loss function