Label-efficient multi-organ segmentation with a diffusion model
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913746218123264 |
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| author | Huang, Yongzhi Xi, Fengjun Tu, Liyun Zhu, Jinxin Hassan, Haseeb Su, Liyilei Peng, Yun Li, Jingyu Ma, Jun Huang, Bingding |
| author_facet | Huang, Yongzhi Xi, Fengjun Tu, Liyun Zhu, Jinxin Hassan, Haseeb Su, Liyilei Peng, Yun Li, Jingyu Ma, Jun Huang, Bingding |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_15216 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Label-efficient multi-organ segmentation with a diffusion model Huang, Yongzhi Xi, Fengjun Tu, Liyun Zhu, Jinxin Hassan, Haseeb Su, Liyilei Peng, Yun Li, Jingyu Ma, Jun Huang, Bingding Computer Vision and Pattern Recognition 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. |
| title | Label-efficient multi-organ segmentation with a diffusion model |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.15216 |