基于改进U型神经网络的脑出血CT图像分割

针对脑出血CT图像病灶部位的多尺度性导致分割精度较低的问题,该文提出一种基于改进U型神经网络的图像分割模型(AU-Net+).首先,该模型利用U-Net中的编码器对脑出血CT图像特征编码,将提出的残差八度卷积(ROC)块应用到U型神经网络的跳跃连接部分,使不同层次的特征更好地融合;其次,对融合后的特征,分别引入混合注意...

Computed-Tomography Image Segmentation of Cerebral Hemorrhage Based on Improved U-shaped Neural Network

In view of the problem of low segmentation accuracy caused by the multi-scale of the lesion location in Computed-Tomography (CT) images of cerebral hemorrhage, an image segmentation model based on Attention improved U-shaped neural Network plus (AU-Net+) is proposed. Firstly, the model uses the encoder in U-Net to encode the features of the CT image of cerebral hemorrhage, and applies the proposed Residual Octave Convolution (ROC) block to the jump connection part of the U-shaped neural network to make the features of different levels more blend well. Secondly, for the merged features, a hybrid attention mechanism is introduced to improve the feature extraction ability of the target area. Finally, the Dice loss function is improved to enhance further the feature learning of the model for small and medium-sized target regions in CT images of cerebral hemorrhage. To verify the performance of the model, the mIoU index is improved by 20.9%, 3.6%, 7.0%, 3.1% compared with U-Net, Attention U-Net, UNet++ and CE-Net respectively, which indicates that AU-Net+ model has better segmentation effect.