AutoPETIII: The Tracer Frontier. What Frontier?

Zacharia Mesbah, Léo Mottay, Romain Modzelewski, Pierre Decazes, Sébastien Hapdey +2 more
9/19/2024
eess.IVcs.AIcs.CV

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Abstract

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.

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Cite this paper

@article{mesbah2024autopetiii,
  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},
  year   = {2024},
  eprint = {2410.02807},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2410.02807v2}
}

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