Label-efficient multi-organ segmentation with a diffusion model

Accurate segmentation of multiple organs in Computed Tomography (CT) images plays a vital role in computer-aided diagnosis systems. While various supervised learning approaches have been proposed recently, these methods heavily depend on a large amount of high-quality labeled data, which are expensive to obtain in practice. To address this challenge, we propose a label-efficient framework using knowledge transfer from a pre-trained diffusion model for CT multi-organ segmentation. Specifically, we first pre-train a denoising diffusion model on 207,029 unlabeled 2D CT slices to capture anatomical patterns. Then, the model backbone is transferred to the downstream multi-organ segmentation task, followed by fine-tuning with few labeled data. In fine-tuning, two fine-tuning strategies, linear classification and fine-tuning decoder, are employed to enhance segmentation performance while preserving learned representations. Quantitative results show that the pre-trained diffusion model is capable of generating diverse and realistic 256x256 CT images (Fréchet inception distance (FID): 11.32, spatial Fréchet inception distance (sFID): 46.93, F1-score: 73.1%). Compared to state-of-the-art methods for multi-organ segmentation, our method achieves competitive performance on the FLARE 2022 dataset, particularly in limited labeled data scenarios. After fine-tuning with 1% and 10% labeled data, our method achieves dice similarity coefficients (DSCs) of 71.56% and 78.51%, respectively. Remarkably, the method achieves a DSC score of 51.81% using only four labeled CT slices. These results demonstrate the efficacy of our approach in overcoming the limitations of supervised learning approaches that is highly dependent on large-scale labeled data.

@article{huang2025_2402.15216,
  title={ Label-efficient multi-organ segmentation with a diffusion model },
  author={ Yongzhi Huang and Fengjun Xi and Liyun Tu and Jinxin Zhu and Haseeb Hassan and Liyilei Su and Yun Peng and Jingyu Li and Jun Ma and Bingding Huang },
  journal={arXiv preprint arXiv:2402.15216},
  year={ 2025 }
}