基于改进U-Net的关节滑膜磁共振图像的分割

为了准确诊断滑膜炎患者病情，医生主要依靠手工标注和勾画的方法来提取磁共振图像（MRI）中的滑膜增生区域，该方法耗时长、效率低，具有一定的主观性且图像信息利用率低。针对这一问题，提出了一种新的关节滑膜分割算法，即2D ResU-net分割算法。首先，将残差网络（ResNet）中的两层结构的残差块融入到U-Net中，构建2D ResU-net；然后，将样本数据集分为训练集和测试集，而后对训练集进行数据增广；最后，将增广后的所有训练样本用于网络模型的训练。为了检测模型的分割效果，选取测试集中含滑膜炎的断层图像进行分割测试，最终平均分割精度指标可达到：Dice相似系数（DSC）69.98%，交并比（IOU）指标79.90%，体积重叠误差（VOE）系数12.11%。与U-Net算法相比，2D ResU-net算法的DSC系数提升了10.72%，IOU指标升高了4.24%，VOE系数降低了11.57%。实验结果表明，该算法对于MRI图像中的滑膜增生区域可以实现较好的分割效果，能够辅助医生对病情做出及时诊断。

Magnetic resonance image segmentation of articular synovium based on improved U-Net

In order to accurately diagnose the synovitis patient's condition, doctors mainly rely on manual labeling and outlining method to extract synovial hyperplasia areas in the Magnetic Resonance Image (MRI). This method is time-consuming and inefficient, has certain subjectivity and is of low utilization rate of image information. To solve this problem, a new articular synovium segmentation algorithm, named 2D ResU-net segmentation algorithm was proposed. Firstly, the two-layer residual block in the Residual Network (ResNet) was integrated into the U-Net to construct the 2D ResU-net. Secondly, the sample dataset was divided into training set and testing set, and data augmentation was performed to the training set. Finally, all the training samples after augmentation were applied to the training of the network model. In order to test the segmentation effect of the model, the tomographic images containing synovitis in the testing set were selected for segmentation test. The final average segmentation accuracy indexes are as follow:Dice Similarity Coefficient (DSC) of 69.98%, IOU (Intersection over Union) index of 79.90% and Volumetric Overlap Error (VOE)of 12.11%. Compared with U-Net algorithm, 2D ResU-net algorithm has the DSC increased by 10.72%, IOU index increased by 4.24% and VOE decreased by 11.57%. Experimental results show that this algorithm can achieve better segmentation effect of synovial hyperplasia areas in MRI images, and can assist doctors to make diagnosis of the disease condition in time.