Automatic segmentation of tumor lesions is a critical initial processing step
for quantitative PET/CT analysis. However, numerous tumor lesion with different
shapes, sizes, and uptake intensity may be distributed in different anatomical
contexts throughout the body, and there is also significant uptake in healthy
organs. Therefore, building a systemic PET/CT tumor lesion segmentation model
is a challenging task. In this paper, we propose a novel training strategy to
build deep learning models capable of systemic tumor segmentation. Our method
is validated on the training set of the AutoPET 2022 Challenge. We achieved
0.7574 Dice score, 0.0299 false positive volume and 0.2538 false negative
volume on preliminary test set.The code of our work is available on the
following link: this https URL.

本研究提出一种新的深度学习模型训练策略，用于构建能够对肿瘤进行系统分割的 PET/CT 肿瘤分割模型，该方法基于 AutoPET 2022 挑战赛的训练集进行验证，并在预测试集上实现了 0.7574 的 Dice 分数、0.0299 的假阳性体积和 0.2538 的假阴性体积，代码已在链接中提供。