无需知道示踪剂类型，自动分割PET/CT病灶。
AutoPETIII: The Tracer Frontier. What Frontier?
- 用nnUNetv2训练两组6重集成模型应对不同示踪剂。
- 引入MIP-CNN自动选择适配的模型集进行分割。
- 适合医学影像自动化分析与多示踪剂场景应用。
过去三年，AutoPET竞赛聚焦正电子发射断层扫描（PET）图像中的病灶分割问题。2024年挑战的核心是现有多种示踪剂共存的现实：算法需在未知示踪剂类型（FDG或PSMA）的前提下，实现全自动的PET/CT病灶分割。本文提出基于nnUNetv2框架，训练两组6重集成模型以应对不同示踪剂，并采用MIP-CNN自动判断应使用哪一组模型进行分割，从而实现端到端的自动分割流程。
原文摘要 · Abstract (English)
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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