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

arXiv: https://arxiv.org/abs/2608.19666 · HTML: https://mlsanity.com/paper/2608.19666/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 15 references that are in the map.
- First posted on arXiv 20 Aug 2026.

## Builds on (most cited first, 8 of 8)

- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [From FDG to PSMA: A Hitchhiker's Guide to Multitracer, Multicenter Lesion Segmentation in PET/CT Imaging](https://mlsanity.com/paper/2409.09478/) (2024)
- [SegAnyPET: Universal Promptable Segmentation from Positron Emission Tomography Images](https://mlsanity.com/paper/2502.14351/) (2025)
- [Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection](https://mlsanity.com/paper/2304.07147/) (2023)
- [Toward a Vision-Language Foundation Model for Medical Data: Multimodal Dataset and Benchmarks for Vietnamese PET/CT Report Generation](https://mlsanity.com/paper/2509.24739/) (2025)
- [Developing a PET/CT Foundation Model for Cross-Modal Anatomical and Functional Imaging](https://mlsanity.com/paper/2503.02824/) (2025)
- [An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation](https://mlsanity.com/paper/2605.21835/) (2026)
- [Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets](https://mlsanity.com/paper/2608.11076/) (2026)

## Abstract (arXiv)

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.