Automatic Segmentation Of CT Liver And Liver Tumor Based On Hybrid Convolutional Neural Network

The search for liver and tumors in liver from computed tomography(CT)images using machine learning and image processing techniques is important for computer-aided diagnosis and treatment.In recent years,deep learning has become the mainstream of medical image segmentation due to the improvement of computer performance.The CT image of liver is a three-dimensional data,and although the two-dimensional Fully Convolutional Networks(FCN)performs well in the Semantic Segmentation task,it lacks the process of utilizing the spatial contextual information in the three-dimensional data.Meanwhile,although 3D fully convolutional neural networks integrate and optimize the information on the Z-axis with that on the 2D plane,the network performance is often limited by the fact that it cannot take a certain depth or even over-consider the information on the Z-axis due to its huge number of parameters.Based on the above deficiencies of the respective dimensional convolutional neural networks,this paper proposes an end-to-end 2.5D convolutional neural network with improvements consisting of the following two main components:(1)By adding a 3D convolutional part to the 2D full convolutional neural network to extract spatial features simultaneously,the model can accept inputs from both 2D and 3D images,and the 2D convolutional and 3D convolutional layers can participate in training together.(2)In order to effectively fuse 2D and 3D information,the AFM(Attention Fuse Module)module is proposed in this paper for generating an attention mask from 3D information and multiplying it with the 2D feature map to make the model get a focused attention region during training.The model is predicted on the test set of the MICCAI 2017 Liver Tumor Segmentation Challenge,and the ablation experiments show that the proposed approach of extracting both 2D and 3D features and fusing them makes the hybrid Fuse multi-dimensional network(FMD-Net)model more efficient than the pure 2D convolutional network and pure 3D convolutional network.The Dice per case value for liver tumor prediction on the test set of 2D Unet and 3D Unet is improved from 0.61 and 0.55 to0.662 respectively,while the Dice global value is improved from 0.774 and0.774 respectively.The Dice global value was increased from 0.774 and0.729 to 0.803,and the Precision of 2D Unet’s liver tumor prediction in the test set was increased from 0.193 to 0.253,which is important for the accurate identification of liver tumors from CT scans.