English(EN) Anatomy-Aware Promptable Segmentation with Online Interactive Training for AUTOPET V

AUTOPET V挑战赛模型采用解剖感知分割技术处理PET/CT扫描

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-31 04:00

研究人员开发了一种用于PET/CT扫描全身病灶检测的解剖感知、可提示分割模型，专门为AUTOPET V挑战赛设计。该模型基于nnU-Net框架构建，采用两阶段训练过程：初始预训练以实现强大的分割能力，以及在线交互阶段，通过涂鸦提示精炼预测。通过整合器官监督和示踪剂分类系统，该模型旨在减少假阳性，并提高对不同示踪剂（如氟代脱氧葡萄糖（18F）和谷氨酸羧肽酶II（PSMA））的准确性。 AI

影响这项研究推动了医学影像领域AI能力的发展，有望提高PET/CT扫描中病灶检测的诊断准确性和效率。

排序理由该项目是一篇学术论文，详细介绍了一种新的生物医学图像分割方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CV TIER_1 English(EN) · Pablo Lozano-Jimenez, Sergio Romero-Tapiador, Ruben Tolosana · 2026-08-31 04:00

面向AUTOPET V的解剖感知可提示分割与在线交互式训练

arXiv:2608.28461v1 Announce Type: new Abstract: We present an anatomy-aware, promptable model for whole-body lesion segmentation in FDG and PSMA PET/CT, developed for the AUTOPET V challenge. The proposed method is built as family of nnU-Net-based models and trained in two stages…

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面向AUTOPET V的解剖感知可提示分割与在线交互式训练

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