基于级联网络的膝关节图像分割与模型构建

采用膝关节模型进行电磁仿真是计算膝关节局部射频功率沉积（SAR）的主要方法，为了构建膝关节模型，本文提出了一种包含两个卷积神经网络——U-Net的级联网络结构，用于膝关节磁共振图像的分割.第一个网络在整幅图上分割肌肉、脂肪等占比较大的组织，并从分割结果中预测软骨与半月板的大致位置信息，第二个网络基于该信息在一个更小的子图上分割小组织以提高分割精度.两个网络均采用焦点损失函数，它们的分割结果合并在一起构成膝关节模型.我们将该方法与其它4种方法的分割结果进行了定量指标的对比研究，并分别构建膝关节模型，计算局部SAR值.结果表明本文提出的级联网络结构可以更精确的构建用于SAR仿真的膝关节模型.

Knee Joint Image Segmentation and Model Construction Based on Cascaded Network

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.