SAM-Driven Weakly Supervised Nodule Segmentation with Uncertainty-Aware Cross Teaching

• URL: http://arxiv.org/abs/2407.13553v1
• Date: Thu, 18 Jul 2024 14:27:54 GMT
• Title: SAM-Driven Weakly Supervised Nodule Segmentation with Uncertainty-Aware Cross Teaching
• Authors: Xingyue Zhao, Peiqi Li, Xiangde Luo, Meng Yang, Shi Chang, Zhongyu Li,
• Abstract summary: Automated nodule segmentation is essential for computer-assisted diagnosis in ultrasound images. Recently, segmentation foundation models like SAM have shown impressive generalizability on natural images. In this work, we devise a novel weakly supervised framework that effectively utilizes the segmentation foundation model to generate pseudo-labels.
• Score: 13.5553526185399
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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