发表机构
Geisel School of Medicine at Dartmouth; Dartmouth Hitchcock Medical Center（达特茅斯盖泽尔医学院; 达特茅斯-希区柯克医疗中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出MUST-PET多模态自监督学习框架，通过跨示踪剂的上下文感知掩码重建，实现全身PET-CT病灶的标注高效、可泛化分割，性能优于从头训练模型。
AI 中文摘要
基于深度学习的全身PET-CT病灶分割可支持癌症分期、治疗计划制定及疗效评估，但稀缺的标注数据与域偏移限制了其泛化能力。自监督学习（SSL）可应对这些挑战，但在泛癌、多示踪剂PET-CT场景中仍未得到充分探索。本研究提出MUST-PET（跨示踪剂多模态自监督学习框架），用于可泛化的全身PET-CT病灶分割。MUST-PET在来自多家机构的多样化泛癌PET-CT扫描数据集上进行训练与验证，该数据集采用FDG及前列腺特异性膜抗原（PSMA）靶向放射性示踪剂采集。MUST-PET采用上下文感知的掩码重建策略：对一种模态进行部分掩码，利用PET与CT的互补信息进行重建。预训练后的模型随后通过标注样本进行微调，从重建质量、病灶分割、标注效率及在独立保留数据集上的泛化能力等维度进行评估。实验结果显示，MUST-PET可降低重建误差，相较于从头训练的模型提升病灶分割性能，且在标注数据有限及未见过的外部数据集上表现良好，证明了多示踪剂SSL在实现标注高效、可泛化的全身PET-CT病灶分割方面的潜力。
英文摘要
Deep learning-based whole-body PET-CT lesion segmentation can support cancer staging, treatment planning, and response assessment, but generalization is limited by scarce annotations and domain shifts. Self-supervised learning (SSL) can address these challenges but remains underexplored in pan-cancer, multi-tracer PET-CT. In this work, we propose MUST-PET (MUltimodal Self-Supervised learning across Tracers), a multimodal, multi-tracer SSL framework for generalizable whole-body PET-CT lesion segmentation. MUST-PET is trained and validated on a diverse, multi-institutional collection of pan-cancer PET-CT scans acquired with FDG and prostate-specific membrane antigen (PSMA)-targeted radiotracers. MUST-PET uses context-aware masked reconstruction, where one modality is partially masked and reconstructed using complementary information from both PET and CT. The pretrained model is subsequently fine-tuned with labeled samples and evaluated for reconstruction quality, lesion segmentation, label efficiency, and generalizability across independent held-out datasets. MUST-PET reduces reconstruction error, improves lesion segmentation over training from scratch, and performs well with limited labeled data and on unseen external datasets, demonstrating the potential of multi-tracer SSL for label-efficient, generalizable whole-body PET-CT. segmentation.
CommentsSubmitted to SPIE CAD 2027
