For the last three years, the AutoPET competition gathered the medical imaging community around a hot topic: lesion segmentation on Positron Emitting Tomography (PET) scans. Each year a different aspect of the problem is presented; in 2024 the multiplicity of existing and used tracers was at the core of the challenge. Specifically, this year's edition aims to develop a fully automatic algorithm capable of performing lesion segmentation on a PET/CT scan, without knowing the tracer, which can either be a FDG or PSMA-based tracer. In this paper we describe how we used the nnUNetv2 framework to train two sets of 6 fold ensembles of models to perform fully automatic PET/CT lesion segmentation as well as a MIP-CNN to choose which set of models to use for segmentation.
@article{arxiv.2410.02807,
title = {AutoPETIII: The Tracer Frontier. What Frontier?},
author = {Zacharia Mesbah and Léo Mottay and Romain Modzelewski and Pierre Decazes and Sébastien Hapdey and Su Ruan and Sébastien Thureau},
journal= {arXiv preprint arXiv:2410.02807},
year = {2025}
}