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.
翻译:研究中越来越关注使用深层学习法实现对各种癌症预测的光子排放光学计算断层扫描(PET CT)中损伤的完全自动分解,医学图像截面的最近进展表明,NNUNET对于各种任务是可行的,但是,PET图像中的损伤分解并非直截了当,因为腐蚀和生理吸收的分布模式相似,它们的区别要求在CT图像中提供额外的结构信息。本文件介绍了基于nnUNet的内分层任务方法。拟议的模型是在2D和3D NENET联合结构的基础上设计的,以预测整个身体的损伤。它允许对潜在损伤进行自动分解。我们评估了在AutoPet挑战中建议的方法,该方法测量了dice评分、假阳性体积和假负负体积等量的测量中损害分的性能。