论文标题
Autopet挑战2022：基于深度学习和FDG PET/CT的全身肿瘤病变的自动分割
AutoPET Challenge 2022: Automatic Segmentation of Whole-body Tumor Lesion Based on Deep Learning and 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培训策略，以建立能够进行全身性肿瘤分割的深度学习模型。我们的方法在AUTOPET 2022挑战的培训集中得到了验证。我们在初步测试集上实现了0.7574的骰子得分，0.0299的误报量和0.2538假阴性。
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: https://github.com/ZZZsn/MICCAI2022-autopet.
