MUST-PET: MUltimodal Self-supervised learning across Tracers for whole-body PET/CT-based lesion segmentation

TL;DR - MUST-PET is a multimodal, multi-tracer self-supervised framework for whole-body PET/CT lesion segmentation. It aims to reduce annotation requirements while improving generalization across cancer types, radiotracers, institutions, and unseen datasets.

• Pretraining uses context-aware masked reconstruction, reconstructing a partially masked PET or CT modality with complementary information from both.
• The study covers multi-institutional, pan-cancer scans acquired with FDG and PSMA-targeted radiotracers.
• Fine-tuning improves lesion segmentation compared with training from scratch and remains effective with limited labeled data.
• Performance on independent external datasets indicates improved robustness to domain shifts.