改进训练策略提升PET/CT肿瘤分割精度，有效减少假阳性。
Advanced Tumor Segmentation in PET/CT Imaging: A Training Strategy Study with nnU-Net for AutoPET III
- 采用nnU-Net框架，结合强度归一化与CraveMix数据增强。
- 最佳配置在预赛阶段达0.80的Dice分数，显著降低假阳性。
- 适合多中心、多示踪剂场景下的医学图像分割研究者。
全身影像中肿瘤分割对精准疾病评估和治疗规划至关重要，但受病灶大小、对比度及解剖分布差异影响，仍具挑战。手动分割耗时且存在观察者间差异。本文针对AutoPET III挑战，基于nnU-Net与ResNet编码器构建全身影像肿瘤分割方法，系统研究了强度归一化、批次Dice优化及CraveMix数据增强对模型性能的影响。实验表明，这些策略显著提升模型鲁棒性，减少假阳性，尤其在应对病灶变异时表现优异。最佳配置在预赛阶段实现0.80的Dice分数，方法排名第三。代码已公开。
原文摘要 · Abstract (English)
Tumor segmentation in whole-body PET/CT imaging is crucial for precise disease evaluation and treatment planning. However, it remains challenging due to variability in lesion size, contrast, and anatomical distribution. Relying on manual segmentation makes the process time-consuming and prone to intra- and inter-observer variability. This work presents a whole-body tumor segmentation method developed for the AutoPET III challenge, where the goal is to build models that generalize across tracers and multi-center data. We employ the nnU-Net framework with a ResNet-based encoder as our baseline and systematically investigate the impact of training strategies, including intensity normalization, batch dice optimization, and data augmentation using CraveMix. Our experiments show that these strategies significantly influence model performance, particularly in reducing false positives and improving robustness to lesion variability. The best-performing configuration achieves a Dice score of up to 0.80 on the preliminary test phase, and our method ranked third in the AutoPET III challenge. The code is publicly available here.
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