Accurate segmentation of multiple organs in Computed Tomography (CT) images plays a vital role in computer-aided diagnosis systems. Various supervised-learning approaches have been proposed recently. However, these methods heavily depend on a large amount of high-quality labeled data, which is expensive to obtain in practice. In this study, we present a label-efficient learning approach using a pre-trained diffusion model for multi-organ segmentation tasks in CT images. First, a denoising diffusion model was trained using unlabeled CT data, generating additional two-dimensional (2D) CT images. Then the pre-trained denoising diffusion network was transferred to the downstream multi-organ segmentation task, effectively creating a semi-supervised learning model that requires only a small amount of labeled data. Furthermore, linear classification and fine-tuning decoder strategies were employed to enhance the network's segmentation performance. Our generative model at 256x256 resolution achieves impressive performance in terms of Fr\'echet inception distance, spatial Fr\'echet inception distance, and F1-score, with values of 11.32, 46.93, and 73.1\%, respectively. These results affirm the diffusion model's ability to generate diverse and realistic 2D CT images. Additionally, our method achieves competitive multi-organ segmentation performance compared to state-of-the-art methods on the FLARE 2022 dataset, particularly in limited labeled data scenarios. Remarkably, even with only 1\% and 10\% labeled data, our method achieves Dice similarity coefficients (DSCs) of 71.56\% and 78.51\% after fine-tuning, respectively. The method achieves a DSC score of 51.81\% using just four labeled CT scans. These results demonstrate the efficacy of our approach in overcoming the limitations of supervised learning heavily reliant on large-scale labeled data.

翻译：计算机断层扫描（CT）图像中多个器官的精确分割在计算机辅助诊断系统中扮演着至关重要的角色。近年来，各种监督学习方法已被提出。然而，这些方法严重依赖大量高质量标注数据，而这在实际应用中获取成本高昂。本研究提出了一种标签高效的学习方法，利用预训练扩散模型实现CT图像中的多器官分割任务。首先，使用未标注的CT数据训练去噪扩散模型，生成额外的二维（2D）CT图像。随后，将预训练的去噪扩散网络迁移至下游的多器官分割任务，有效构建了一个仅需少量标注数据的半监督学习模型。此外，采用线性分类和微调解码器策略以增强网络的分割性能。我们的生成模型在256x256分辨率下取得了令人印象深刻的性能，其弗雷歇初始距离、空间弗雷歇初始距离和F1分数分别为11.32、46.93和73.1%。这些结果证实了扩散模型生成多样且逼真的二维CT图像的能力。此外，与FLARE 2022数据集上的最新方法相比，我们的方法在标注数据有限的情况下实现了具有竞争力的多器官分割性能。值得注意的是，即使仅使用1%和10%的标注数据，经过微调后我们的方法仍分别达到了71.56%和78.51%的Dice相似系数。仅使用四例标注CT扫描时，该方法获得了51.81%的DSC分数。这些结果证明了我们的方法在克服依赖大规模标注数据的监督学习局限性方面的有效性。