Deep learning has shown excellent performance in analysing medical images. However, datasets are difficult to obtain due privacy issues, standardization problems, and lack of annotations. We address these problems by producing realistic synthetic images using a combination of 3D technologies and generative adversarial networks. We propose CUT-seg, a joint training where a segmentation model and a generative model are jointly trained to produce realistic images while learning to segment polyps. We take advantage of recent one-sided translation models because they use significantly less memory, allowing us to add a segmentation model in the training loop. CUT-seg performs better, is computationally less expensive, and requires less real images than other memory-intensive image translation approaches that require two stage training. Promising results are achieved on five real polyp segmentation datasets using only one real image and zero real annotations. As a part of this study we release Synth-Colon, an entirely synthetic dataset that includes 20000 realistic colon images and additional details about depth and 3D geometry: https://enric1994.github.io/synth-colon

翻译：深度学习在医学图像分析中表现出色。然而，由于隐私问题、标准化问题以及缺乏标注，数据集难以获取。我们通过结合3D技术和生成对抗网络生成逼真的合成图像来应对这些挑战。提出CUT-seg联合训练方法，其中分割模型与生成模型协同训练，在生成逼真图像的同时学习对息肉进行分割。我们利用了近期单侧翻译模型的优势，因其显著减少内存占用，使得在训练流程中加入分割模型成为可能。CUT-seg相比需要两阶段训练的其他高内存图像翻译方法，性能更优、计算成本更低且所需真实图像更少。在仅使用一张真实图像且无任何真实标注的条件下，该方法在五个真实息肉分割数据集上取得了令人满意的结果。作为本研究的一部分，我们发布了Synth-Colon数据集——包含20000张逼真结肠图像及深度与3D几何细节的全合成数据集：https://enric1994.github.io/synth-colon