Organ at Risk (OAR) segmentation from CT scans is a key component of the radiotherapy treatment workflow. In recent years, deep learning techniques have shown remarkable potential in automating this process. In this paper, we investigate the performance of Generative Adversarial Networks (GANs) compared to supervised learning approaches for segmenting OARs from CT images. We propose three GAN-based models with identical generator architectures but different discriminator networks. These models are compared with well-established CNN models, such as SE-ResUnet and DeepLabV3, using the StructSeg dataset, which consists of 50 annotated CT scans containing contours of six OARs. Our work aims to provide insight into the advantages and disadvantages of adversarial training in the context of OAR segmentation. The results are very promising and show that the proposed GAN-based approaches are similar or superior to their CNN-based counterparts, particularly when segmenting more challenging target organs.

翻译：从CT扫描中分割器官危及区（OAR）是放疗治疗工作流程中的关键环节。近年来，深度学习技术在自动化该流程方面展现出显著潜力。本文研究了生成对抗网络（GANs）与监督学习方法在CT图像OAR分割任务中的性能差异。我们提出了三种基于GAN的模型，这些模型具有相同的生成器架构但不同的判别器网络。利用包含50张标注CT扫描（涵盖六个OAR的轮廓）的StructSeg数据集，我们将这些模型与已广泛应用的CNN模型（如SE-ResUnet和DeepLabV3）进行了比较。本研究旨在深入揭示对抗训练在OAR分割场景中的优势与局限。结果表明，所提出的基于GAN的方法在性能上与基于CNN的方法相当或更优，尤其在分割更具挑战性的目标器官时表现突出。