Purpose The identification of abnormalities that are relatively rare within otherwise normal anatomy is a major challenge for deep learning in the semantic segmentation of medical images. The small number of samples of the minority classes in the training data makes the learning of optimal classification challenging, while the more frequently occurring samples of the majority class hamper the generalization of the classification boundary between infrequently occurring target objects and classes. In this paper, we developed a novel generative multi-adversarial network, called Ensemble-GAN, for mitigating this class imbalance problem in the semantic segmentation of abdominal images.

Method The Ensemble-GAN framework is composed of a single-generator and a multi-discriminator variant for handling the class imbalance problem to provide a better generalization than existing approaches. The ensemble model aggregates the estimates of multiple models by training from different initializations and losses from various subsets of the training data. The single generator network analyzes the input image as a condition to predict a corresponding semantic segmentation image by use of feedback from the ensemble of discriminator networks. To evaluate the framework, we trained our framework on two public datasets, with different imbalance ratios and imaging modalities: the Chaos 2019 and the LiTS 2017.

Result In terms of the F1 score, the accuracies of the semantic segmentation of healthy spleen, liver, and left and right kidneys were 0.93, 0.96, 0.90 and 0.94, respectively. The overall F1 scores for simultaneous segmentation of the lesions and liver were 0.83 and 0.94, respectively.

Conclusion The proposed Ensemble-GAN framework demonstrated outstanding performance in the semantic segmentation of medical images in comparison with other approaches on popular abdominal imaging benchmarks. The Ensemble-GAN has the potential to segment abdominal images more accurately than human experts.

目的识别在其他正常解剖结构中相对罕见的异常是医学图像语义分割中深度学习的主要挑战。训练数据中少数类的少量样本使得最优分类的学习具有挑战性，而多数类的更频繁出现的样本阻碍了不经常出现的目标对象和类之间分类边界的泛化。在本文中，我们开发了一种新的生成多对抗网络，称为 Ensemble-GAN，用于缓解腹部图像语义分割中的此类不平衡问题。

方法Ensemble-GAN 框架由单生成器和多判别器变体组成，用于处理类不平衡问题，以提供比现有方法更好的泛化。集成模型通过来自不同初始化的训练和来自训练数据的不同子集的损失来聚合多个模型的估计。单个生成器网络分析输入图像作为条件，通过使用来自判别器网络集合的反馈来预测相应的语义分割图像。为了评估该框架，我们在两个公共数据集上训练了我们的框架，这些数据集具有不同的不平衡率和成像模式：Chaos 2019 和 LiTS 2017。

结果在F1评分上，健康脾脏、肝脏和左右肾的语义分割准确率分别为0.93、0.96、0.90和0.94。病变和肝脏同时分割的总体 F1 分数分别为 0.83 和 0.94。

结论与流行的腹部成像基准上的其他方法相比，所提出的 Ensemble-GAN 框架在医学图像的语义分割方面表现出出色的性能。Ensemble-GAN 有可能比人类专家更准确地分割腹部图像。