Mutual Distillation of Dual-Foundation Models for Semi-Supervised PET/CT Segmentation

• URL: http://arxiv.org/abs/2606.15611v1
• Date: Sun, 14 Jun 2026 05:34:26 GMT
• Title: Mutual Distillation of Dual-Foundation Models for Semi-Supervised PET/CT Segmentation
• Abstract summary: Organ segmentation from PET/CT is critical for quantitative analysis and radiotherapy planning in oncology.<n>Semi-supervised learning (SSL) provides a practical and effective solution for developing deep models with limited labeled data.<n>We propose a mutual distillation framework that seamlessly exploits both structural and functional foundation models.
• Score: 8.784478648071547
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Organ segmentation from PET/CT is critical for quantitative analysis and radiotherapy planning in oncology. To ease the high annotation cost of PET/CT segmentation, semi-supervised learning (SSL) provides a practical and effective solution for developing deep models with limited labeled data. Recent developments in visual foundation models have demonstrated remarkable adaptability with improved efficiency. In this work, we propose a mutual distillation framework that seamlessly exploits both structural and functional foundation models, which act as modality-specific generalists for distilling knowledge from structural CT and metabolic PET imaging. By bridging the gap between the task-specific precision of student models and the segmentation priors of generalist foundation models, we propose \textbf{MuDuo}, a mutual distillation framework that synergistically leverages SAM-Med3D for CT and SegAnyPET for PET to distill their knowledge into a lightweight student network. Our approach eliminates the need for manual prompts while maximizing the utility of unlabeled data for automatic segmentation, achieving state-of-the-art performance on the AutoPET dataset with only 5 labeled cases. Our source code is available at https://github.com/Wu-beining/MuDuo.

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