AutoPETIII: The Tracer Frontier. What Frontier?
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
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| _version_ | 1866918135392632832 |
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| author | Mesbah, Zacharia Mottay, Léo Modzelewski, Romain Decazes, Pierre Hapdey, Sébastien Ruan, Su Thureau, Sébastien |
| author_facet | Mesbah, Zacharia Mottay, Léo Modzelewski, Romain Decazes, Pierre Hapdey, Sébastien Ruan, Su Thureau, Sébastien |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_02807 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AutoPETIII: The Tracer Frontier. What Frontier? Mesbah, Zacharia Mottay, Léo Modzelewski, Romain Decazes, Pierre Hapdey, Sébastien Ruan, Su Thureau, Sébastien Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | AutoPETIII: The Tracer Frontier. What Frontier? |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.02807 |