1. AutoPETIII: The Tracer Frontier. What Frontier?
- Author
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Mesbah, Zacharia, Mottay, Léo, Modzelewski, Romain, Decazes, Pierre, Hapdey, Sébastien, Ruan, Su, and Thureau, Sébastien
- Subjects
Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Artificial Intelligence ,Computer Science - Computer Vision and Pattern Recognition - 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.
- Published
- 2024