双基础模型的相互蒸馏用于半监督PET/CT分割
Mutual Distillation of Dual-Foundation Models for Semi-Supervised PET/CT Segmentation
AI总结：
提出MuDuo框架，利用SAM-Med3D和SegAnyPET分别从CT和PET中蒸馏知识到轻量学生网络，实现半监督器官分割，仅用5个标注样本在AutoPET数据集上达到最优性能。
摘要：
PET/CT的器官分割对于肿瘤学中的定量分析和放疗计划至关重要。为了降低PET/CT分割的高标注成本，半监督学习（SSL）为使用有限标注数据开发深度模型提供了一种实用且有效的解决方案。视觉基础模型的最新发展展示了显著的适应性和更高的效率。在这项工作中，我们提出了一个相互蒸馏框架，该框架无缝地利用了结构性和功能性基础模型，这些模型作为模态特定的通才，从结构性CT和代谢性PET成像中蒸馏知识。通过弥合学生模型的任务特定精度与通才基础模型的分割先验之间的差距，我们提出了MuDuo，一个相互蒸馏框架，协同利用SAM-Med3D用于CT和SegAnyPET用于PET，将它们的知识蒸馏到一个轻量级学生网络中。我们的方法消除了手动提示的需要，同时最大化未标注数据在自动分割中的效用，在AutoPET数据集上仅使用5个标注案例就达到了最先进的性能。我们的源代码可在https://github.com/Wu-beining/MuDuo获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
