论文标题
priornet：PET-CT中的病变分割，包括先前的肿瘤外观信息
PriorNet: lesion segmentation in PET-CT including prior tumor appearance information
论文作者
论文摘要
由于获得信息的双重性质，PET-CT图像中的肿瘤分割具有挑战性：CT中的代谢低和PET中的空间分辨率低。在医学领域开发全自动图像分割方法时，U-NET体系结构是最常见和广泛认可的方法。我们提出了一种两步方法，旨在完善和改善PET-CT中肿瘤病变的分割性能。第一步从PET-CT体积产生了先前的肿瘤外观图，被视为先前的肿瘤信息。由标准的U-NET组成的第二步将获得先前的肿瘤外观图和PET-CT图像，以生成病变面膜。我们评估了可用于AUTOPET 2022挑战的1014例案例的方法，结果显示阳性病例的平均骰子得分为0.701。
Tumor segmentation in PET-CT images is challenging due to the dual nature of the acquired information: low metabolic information in CT and low spatial resolution in PET. U-Net architecture is the most common and widely recognized approach when developing a fully automatic image segmentation method in the medical field. We proposed a two-step approach, aiming to refine and improve the segmentation performances of tumoral lesions in PET-CT. The first step generates a prior tumor appearance map from the PET-CT volumes, regarded as prior tumor information. The second step, consisting of a standard U-Net, receives the prior tumor appearance map and PET-CT images to generate the lesion mask. We evaluated the method on the 1014 cases available for the AutoPET 2022 challenge, and the results showed an average Dice score of 0.701 on the positive cases.
