基于光流场与语义特征融合的心脏Cine-MRI左心室心肌分割方法

由于运动原因会造成活体心脏MRI图像中左心室心内膜与心肌边缘轮廓模糊,进而导致分割不准确以及分割精度较低,针对这些问题,本文提出一种基于光流场与语义特征融合的心脏4D Cine-MRI (magnetic resonance imaging)左心室心肌分割模型OSFNet.该模型包含了光流场计算和语义分割网络:将光流场计算得到的运动特征与图像语义特征进行融合,通过网络学习达到了最优的分割效果.模型采用编码器-解码器结构,本文提出的多感受野平均池化模块用于提取多尺度语义特征,减少了特征丢失;解码器部分使用了多路上采样方法和跳跃连接,保证了语义特征被有效还原.本文使用ACDC公开数据集对模型进行训练与测试,并分别与DenseNet和U-Net在左心室内膜分割、左心室内膜和心肌分割目标上进行对比.实验结果表明, OSFNet在Dice和HD等多个指标上取得了最佳效果.

Left Ventricular Myocardium Segmentation Method of Cardiac Cine-MRI Based on Optical Flow and Semantic Feature Fusion

In magnetic resonance imaging (MRI) of living hearts, the edges of the left ventricular endocardium and myocardium are blurred due to movement, which results in inaccurate segmentation. To address this problem, we propose a left ventricular myocardium segmentation model OSFNet of 4D cardiac Cine-MRI based on the optical flow field and semantic feature fusion. The model includes the optical flow field calculation and semantic segmentation network, where the motion features calculated by the optical flow field are fused with the semantic features of images to achieve the optimal segmentation effect through network learning. The model employs the encoder-decoder architecture, and the proposed multi-receptive field module with average pooling is used to extract multi-scale semantic features and reduce feature losses. The decoder uses the multi-path up-sampling method and skip connections to ensure that semantic features are effectively restored. Then, the open dataset ACDC is applied to train and test the model, and the proposed model is compared with DenseNet and U-Net by the experiments of the left ventricular endocardium segmentation and the left ventricular endocardium and myocardium segmentation. Experimental results indicate that OSFNet achieves the best performance in several indicators such as Dice and HD.