AutoPETIII: The Tracer Frontier. What Frontier?
This work addresses a domain-specific challenge in medical imaging for lesion segmentation, but it is incremental as it builds on existing methods like nnUNetv2.
The authors tackled the problem of automatic lesion segmentation on PET/CT scans without prior knowledge of the tracer type (FDG or PSMA), using the nnUNetv2 framework with ensembles and a MIP-CNN for model selection, achieving results in the AutoPETIII competition.
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.