论文标题
18F-FDG PET/CT中的全身病变细分
Whole-Body Lesion Segmentation in 18F-FDG PET/CT
论文作者
论文摘要
对使用基于深度学习的方法来实现正电子发射断层扫描（PET CT）扫描中的病变的完全自动分割的研究兴趣越来越多，以实现各种癌症的预后。医学图像细分的最新进展表明，NNUNET对于各种任务是可行的。但是，PET图像中的病变分割并不简单，因为病变和生理摄取具有相似的分布模式。它们的区别需要CT图像中的额外结构信息。本文引入了一种基于NNUNET的病变分割任务的方法。提出的模型是根据关节2D和3D NNUNET结构设计的，以预测整个身体的病变。它允许对潜在病变的自动分割。我们在AUTOPET挑战的背景下评估了所提出的方法，该方法衡量了骰子评分指标，假阳性体积和假阴性体积的病变细分性能。
There has been growing research interest in using deep learning based method to achieve fully automated segmentation of lesion in Positron emission tomography computed tomography(PET CT) scans for the prognosis of various cancers. Recent advances in the medical image segmentation shows the nnUNET is feasible for diverse tasks. However, lesion segmentation in the PET images is not straightforward, because lesion and physiological uptake has similar distribution patterns. The Distinction of them requires extra structural information in the CT images. The present paper introduces a nnUNet based method for the lesion segmentation task. The proposed model is designed on the basis of the joint 2D and 3D nnUNET architecture to predict lesions across the whole body. It allows for automated segmentation of potential lesions. We evaluate the proposed method in the context of AutoPet Challenge, which measures the lesion segmentation performance in the metrics of dice score, false-positive volume and false-negative volume.
