关键词: 肝脏分割, 肝肿瘤分割, Unet, 能量生成对抗网络（EBGAN）

Abstract:

The segmentation of liver and liver tumor from the image is one of the important means of liver disease diagnosis. The existing methods based on Convolutional Neural Network（CNN） achieve the segmentation of liver and liver tumor by assigning category labels to each pixel in the input image. In the process of classifying each pixel, CNN fails to use other pixel category information in the neighborhood, which is prone to suffer from missing detection of small objects and fuzzy segmentation boundaries. To address these issues, a conditional energy-based GAN is proposed for liver and liver tumor segmentation. This method, based on Energy-Based Generative Adversarial Network（EBGAN） and Conditional Generative Adversarial Network（CGAN）, uses a CNN-based segmentation network as a generator and an auto-encoder as a discriminator. The discriminator is used as a loss function to measure and improve the similarity between segmentation result and ground truth. In the process of adversarial training, the discriminator takes the segmentation output by the generator as the input and the raw image as the conditional constraint, improves the segmentation accuracy by learning the higher-order consistency between pixel categories, and uses the energy function as the discriminator to avoid vanishing gradient or exploding gradient, so it is tractable to train. The proposed method is evaluated on the dataset MICCAI 2017 Liver Tumor Segmentation（LiTS） Challenge and 3DIRCADb dataset. The experimental results show that, the proposed method can not only extract liver and liver tumor automatically, but also make use of the higher-order consistency between pixel categories to improve the segmentation accuracy of tumor and liver boundary. Furthermore, the missing detection of small tumors is reduced.

Key words: liver segmentation, liver tumor segmentation, Unet; , Energy-Based Generative Adversarial Network（EBGAN）