SAM-Driven Weakly Supervised Nodule Segmentation with Uncertainty-Aware Cross Teaching
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910533729386496 |
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| author | Zhao, Xingyue Li, Peiqi Luo, Xiangde Yang, Meng Chang, Shi Li, Zhongyu |
| author_facet | Zhao, Xingyue Li, Peiqi Luo, Xiangde Yang, Meng Chang, Shi Li, Zhongyu |
| contents | Automated nodule segmentation is essential for computer-assisted diagnosis in ultrasound images. Nevertheless, most existing methods depend on precise pixel-level annotations by medical professionals, a process that is both costly and labor-intensive. Recently, segmentation foundation models like SAM have shown impressive generalizability on natural images, suggesting their potential as pseudo-labelers. However, accurate prompts remain crucial for their success in medical images. In this work, we devise a novel weakly supervised framework that effectively utilizes the segmentation foundation model to generate pseudo-labels from aspect ration annotations for automatic nodule segmentation. Specifically, we develop three types of bounding box prompts based on scalable shape priors, followed by an adaptive pseudo-label selection module to fully exploit the prediction capabilities of the foundation model for nodules. We also present a SAM-driven uncertainty-aware cross-teaching strategy. This approach integrates SAM-based uncertainty estimation and label-space perturbations into cross-teaching to mitigate the impact of pseudo-label inaccuracies on model training. Extensive experiments on two clinically collected ultrasound datasets demonstrate the superior performance of our proposed method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_13553 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SAM-Driven Weakly Supervised Nodule Segmentation with Uncertainty-Aware Cross Teaching Zhao, Xingyue Li, Peiqi Luo, Xiangde Yang, Meng Chang, Shi Li, Zhongyu Computer Vision and Pattern Recognition Automated nodule segmentation is essential for computer-assisted diagnosis in ultrasound images. Nevertheless, most existing methods depend on precise pixel-level annotations by medical professionals, a process that is both costly and labor-intensive. Recently, segmentation foundation models like SAM have shown impressive generalizability on natural images, suggesting their potential as pseudo-labelers. However, accurate prompts remain crucial for their success in medical images. In this work, we devise a novel weakly supervised framework that effectively utilizes the segmentation foundation model to generate pseudo-labels from aspect ration annotations for automatic nodule segmentation. Specifically, we develop three types of bounding box prompts based on scalable shape priors, followed by an adaptive pseudo-label selection module to fully exploit the prediction capabilities of the foundation model for nodules. We also present a SAM-driven uncertainty-aware cross-teaching strategy. This approach integrates SAM-based uncertainty estimation and label-space perturbations into cross-teaching to mitigate the impact of pseudo-label inaccuracies on model training. Extensive experiments on two clinically collected ultrasound datasets demonstrate the superior performance of our proposed method. |
| title | SAM-Driven Weakly Supervised Nodule Segmentation with Uncertainty-Aware Cross Teaching |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.13553 |