论文标题
Autopet Challenge 2022：全身FDG-PET/CT中的分步病变细分
AutoPET Challenge 2022: Step-by-Step Lesion Segmentation in Whole-body FDG-PET/CT
论文作者
论文摘要
肿瘤病变的自动分割是定量PET/CT分析的关键初始处理步骤。但是，许多具有不同形状，大小和摄取强度的肿瘤病变可能在整个身体的不同解剖环境中分布，并且健康器官也有明显的吸收。因此，建立系统性的PET/CT肿瘤病变细分模型是一项具有挑战性的任务。在本文中，我们提出了一种新颖的逐步3D分割方法来解决此问题。我们在初步测试集上达到了0.92的骰子分数，假正量为0.89，假阴性体积为0.53。
Automatic segmentation of tumor lesions is a critical initial processing step for quantitative PET/CT analysis. However, numerous tumor lesions 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 step-by-step 3D segmentation method to address this problem. We achieved Dice score of 0.92, false positive volume of 0.89 and false negative volume of 0.53 on preliminary test set.The code of our work is available on the following link: https://github.com/rightl/autopet.
