Dual channel CW nnU-Net for 3D PET-CT Lesion Segmentation in 2024 autoPET III Challenge

Ching-Wei Wang, Ting-Sheng Su, Keng-Wei Liu
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Abstract

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.
2024 autoPET III 挑战赛中用于三维 PET-CT 病灶分割的双通道 CW nnU-Net
正电子发射计算机断层显像/计算机断层扫描(PET/CT)被广泛应用于恶性肿瘤的成像,因为它能突出显示葡萄糖代谢增加的区域,而葡萄糖代谢增加是癌症活动的标志。在这项研究中,我们为全身 PET/CT 自动病灶分割--多示踪剂多中心泛化(autoPET III)挑战赛开发了一种先进的三维残留 U-Net 模型,该挑战赛将与 2024 年医学影像计算和计算机辅助干预(MICCAI)会议在摩洛哥马拉喀什联合举行。在大挑战平台的初步测试集中,拟议模型的表现优于挑战基线模型,我们的团队目前在来自全球 53 个国家的 497 名参赛者中排名第二(访问日期:2024/9/4),Dice 分数为 0.8700,假阴性量为 19.3969,假阳性量为 1.0857。
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