用于autoPETV大赛的三阶段涂鸦自适应课程学习方法

中文摘要 AI 辅助

本报告介绍了Libo Zhang针对autoPETV大赛中全身PET/CT的交互式病灶分割任务提出的算法解决方案。交互过程被编码为两个额外的输入通道，用于光栅化累积的前景和背景涂鸦；一个约含1.4亿参数的残差编码器U-Net模型，采用三阶段课程学习策略训练4000个epoch：网络首先在交互通道静默的情况下学习全自动分割，接着在随机采样的可见模式下观察由真值生成的涂鸦，最后通过在线模拟最多5个错误驱动的修正步骤来适应自身的错误。训练使用了1811项autoPET和DeepPSMA研究数据，提交时通过logit平均集成了5折交叉验证中最佳和最终的检查点。在含6个交互步骤的交互式5折交叉验证中，最终检查点达到平均AUC-Dice为3.836、平均AUC-DMM为3.869，每一折均单调提升，其中约一半的总增益由第一个修正涂鸦提供。我们的代码和训练好的模型检查点可在此httpsURL获取。

英文摘要

This report describes Libo Zhang's algorithmic solution to autoPETV Grand Challenge on interactive lesion segmentation in whole-body PET/CT. Interaction is encoded as two additional input channels that rasterize the accumulated foreground and background scribbles, and a residual-encoder U-Net of about 140 million parameters is trained with a three-phase curriculum over 4000 epochs: the network first learns fully automatic segmentation with silent interaction channels, then observes ground-truth-derived scribbles under randomly sampled visibility modes, and finally adapts to its own mistakes through online simulation of up to five error-driven correction steps. Training draws on 1811 autoPET and DeepPSMA studies, and the submission ensembles the best and final checkpoints of five folds by logit averaging. In interactive five-fold cross-validation with six interaction steps, the final checkpoints reach a mean AUC-Dice of 3.836 and a mean AUC-DMM of 3.869, improving monotonically in every fold, with roughly half of the total gain delivered by the first corrective scribble. Our code and trained model checkpoints are available on https://github.com/Libo1023/autoPETV-Curriculum.

发表机构

• University Hospital Tübingen（蒂宾根大学医院）
• LMU University Hospital Munich（慕尼黑大学医院）
• Peter MacCallum Cancer Centre（彼得·麦卡勒姆癌症中心）

机构由 AI 辅助整理，请以论文原文为准。