改进3D U-Net，提升全身PET-CT肿瘤分割精度
Dual channel CW nnU-Net for 3D PET-CT Lesion Segmentation in 2024 autoPET III Challenge
- 引入样本注意力增强机制，动态优化难例贡献
- 在autoPET III挑战中达Dice 0.8700，假阴性体积仅19.4
- 适用于多示踪剂、跨中心的临床肿瘤分割任务
PET/CT广泛用于恶性肿瘤影像诊断，因其能突出葡萄糖代谢增高区域，反映癌变活动。准确的3D病灶分割对肿瘤诊疗至关重要。本研究针对2024年MICCAI大会联合举办的autoPET III挑战赛（多示踪剂、多中心泛化），开发了一种先进的3D残差U-Net模型。提出新颖的样本注意力增强技术，在训练中动态调整难例贡献，提升模型在FDG与PSMA示踪剂间的泛化能力。该模型在挑战赛预测试集上优于基线，目前全球497名参赛者中排名第二（截至2024年9月4日），取得Dice分数0.8700，假阴性体积19.3969，假阳性体积1.0857。
原文摘要 · Abstract (English)
PET/CT is extensively used in imaging malignant tumors because it highlights areas of increased glucose metabolism, indicative of cancerous activity. Accurate 3D lesion segmentation in PET/CT imaging is essential for effective oncological diagnostics and treatment planning. In this study, we developed an advanced 3D residual U-Net model for the Automated Lesion Segmentation in Whole-Body PET/CT - Multitracer Multicenter Generalization (autoPET III) Challenge, which will be held jointly with 2024 Medical Image Computing and Computer Assisted Intervention (MICCAI) conference at Marrakesh, Morocco. Proposed model incorporates a novel sample attention boosting technique to enhance segmentation performance by adjusting the contribution of challenging cases during training, improving generalization across FDG and PSMA tracers. The proposed model outperformed the challenge baseline model in the preliminary test set on the Grand Challenge platform, and our team is currently ranking in the 2nd place among 497 participants worldwide from 53 countries (accessed date: 2024/9/4), with Dice score of 0.8700, False Negative Volume of 19.3969 and False Positive Volume of 1.0857.
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