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This paper proposes 3D-MedGAN, MLU-Net and Info-Max-Net models for overcoming the lack of labeled data and extracting the multi-level feature of images in medical image segmentation. 3D-MedGAN is aimed at dealing with the lack of labeled data in medical images. It uses a generative adversarial network to simulate data and then draws newly generated samples from the distribution learned by the model. Training the segmentation model by mixing generated samples with real samples can effectively improve the effect of the segmentation model. MLU-Net uses multiple layers of different levels of convolutional angles to extract feature information from multiple angles in medical images. By adopting the attention mechanism to fuse the multi-level feature information, MLU-Net is able to improve the feature expressions and segmentation effect. Info-Max-Net is aimed at handling the noise problem in medical images. When the information in the images is complex, it is difficult to extract features. Using mutual information to measure the dependency between the image and the extracted features can effectively reduce noise in the image and improve the effect of segmentation. At the same time, for solving the problem that the high dimension of the image makes it difficult to measure mutual information, this paper uses a lower bound BL-estimator to measure the mutual information between the optimized image and the extracted features. Therefore, the model can maintain a high convergence speed as it approaches the true value of mutual information. Considering that the quality of images generated by 3D-MedGAN are not as good as the original images, we combine the 3D-MedGAN, MLU-Net, and Info-Max-Net to improve the sensitivity and the power of feature extraction of the hybird model. The effectiveness of our model is verified through experiments of the 3D-MedGAN, MLU-Net, Info-Max-Net, and the hybrid model over the LIVER100 dataset.

Hybrid Segmentation Algorithm for Medical Image Segmentation Based on Generating Adversarial Networks, Mutual Information and Multi-Scale Information