# From FDG to PSMA: A Hitchhiker's Guide to Multitracer, Multicenter Lesion Segmentation in PET/CT Imaging

arXiv: https://arxiv.org/abs/2409.09478 · HTML: https://mlsanity.com/paper/2409.09478/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 14 Sep 2024.

## Builds on (most cited first, 3 of 3)

- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation](https://mlsanity.com/paper/2404.09556/) (2024)
- [MultiTalent: A Multi-Dataset Approach to Medical Image Segmentation](https://mlsanity.com/paper/2303.14444/) (2023)

## Built on by (most cited first, 16 of 16)

- [nnInteractive: Redefining 3D Promptable Segmentation](https://mlsanity.com/paper/2503.08373/) (2025)
- [LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body Imaging](https://mlsanity.com/paper/2502.20985/) (2025)
- [The autoPET3 Challenge: Automated Lesion Segmentation in Whole-Body PET/CT unicodex2013 Multitracer Multicenter Generalization](https://mlsanity.com/paper/2605.05775/) (2026)
- [Self is the Best Learner: CT-free Ultra-Low-Dose PET Organ Segmentation via Collaborating Denoising and Segmentation Learning](https://mlsanity.com/paper/2503.03786/) (2025)
- [Towards Interactive Lesion Segmentation in Whole-Body PET/CT with Promptable Models](https://mlsanity.com/paper/2508.21680/) (2025)
- [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)
- [GRASPing Anatomy to Improve Pathology Segmentation](https://mlsanity.com/paper/2508.03374/) (2025)
- [Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets](https://mlsanity.com/paper/2508.18612/) (2025)
- [A Generalist Foundation Model for Total-body PET/CT Enables Diagnostic Reporting and System-wide Metabolic Profiling](https://mlsanity.com/paper/2601.12820/) (2026)
- [Bridging MRI and PET physiology: Untangling complementarity through orthogonal representations](https://mlsanity.com/paper/2604.07154/) (2026)
- [Improving PET/CT-Based Whole-Body Lesion Segmentation Using Prediction Uncertainty-Augmented Models](https://mlsanity.com/paper/2606.10115/) (2026)
- [RADIANT-PET: Reasoning-Augmented PET/CT Lesion Segmentation with Large Language Models and Reinforcement Learning](https://mlsanity.com/paper/2606.28392/) (2026)
- [GLOW-FDG: Generalized cancer LesiOn Whole-body segmentation model for 18F-FDG-PET/CT](https://mlsanity.com/paper/2607.03931/) (2026)
- [When Two Tracers Disagree: An Investigation of Multimodal Fusion for Clinical PET/CT Segmentation](https://mlsanity.com/paper/2608.19063/) (2026)
- [MUST-PET: MUltimodal Self-supervised learning across Tracers for whole-body PET/CT-based lesion segmentation](https://mlsanity.com/paper/2608.19666/) (2026)
- [BS: Take the Hint - Interactive Multitracer PET/CT Lesion Segmentation with a Scribble-Conditioned ResEnc U-Net](https://mlsanity.com/paper/2609.01554/) (2026)

## Abstract (arXiv)

Automated lesion segmentation in PET/CT scans is crucial for improving clinical workflows and advancing cancer diagnostics. However, the task is challenging due to physiological variability, different tracers used in PET imaging, and diverse imaging protocols across medical centers. To address this, the autoPET series was created to challenge researchers to develop algorithms that generalize across diverse PET/CT environments. This paper presents our solution for the autoPET III challenge, targeting multitracer, multicenter generalization using the nnU-Net framework with the ResEncL architecture. Key techniques include misalignment data augmentation and multi-modal pretraining across CT, MR, and PET datasets to provide an initial anatomical understanding. We incorporate organ supervision as a multitask approach, enabling the model to distinguish between physiological uptake and tracer-specific patterns, which is particularly beneficial in cases where no lesions are present. Compared to the default nnU-Net, which achieved a Dice score of 57.61, or the larger ResEncL (65.31) our model significantly improved performance with a Dice score of 68.40, alongside a reduction in false positive (FPvol: 7.82) and false negative (FNvol: 10.35) volumes. These results underscore the effectiveness of combining advanced network design, augmentation, pretraining, and multitask learning for PET/CT lesion segmentation. After evaluation on the test set, our approach was awarded the first place in the model-centric category (Team LesionTracer). Code is publicly available at https://github.com/MIC-DKFZ/autopet-3-submission.