论文标题
全身多模式PET/CT图像通过假阳性还原网络的自动肿瘤分割
Automatic Tumor Segmentation via False Positive Reduction Network for Whole-Body Multi-Modal PET/CT Images
论文作者
论文摘要
多模式性荧光脱氧葡萄糖（FDG）正电子发射断层扫描 /计算机断层扫描（PET / CT）常规用于评估常见癌症，例如肺癌，淋巴瘤和黑色素瘤。这主要归因于以下事实：PET/CT结合了对PET肿瘤检测的高灵敏度和CT的解剖学信息。在PET/CT图像评估中，自动肿瘤分割是一个重要的步骤，近年来，基于深度学习的方法已成为最新方法。不幸的是，现有的方法倾向于过度细分肿瘤区域，并包括正常摄取器官，炎症和其他感染等区域。在这项研究中，我们引入了一个假阳性还原网络以克服这一限制。我们首先引入了一个自制的预训练的全球分割模块，以使用自我监督的预训练的编码器粗略地描绘候选肿瘤区域。然后，通过局部细化模块去除误报来完善候选肿瘤区域。我们对MICCAI 2022自动病变分割进行了全身FDG-PET/CT（AUTOPET）挑战数据集的实验表明，我们的方法在初步测试数据中获得了0.9324的骰子得分，并在排行榜上排名第一。我们的方法在最终测试数据的前7位方法中也排名，最终排名将在2022 MICCAI AUTOPET研讨会上宣布。我们的代码可在以下网址提供：https：//github.com/yigepeng/autopet_false_posisity_reduction。
Multi-modality Fluorodeoxyglucose (FDG) positron emission tomography / computed tomography (PET/CT) has been routinely used in the assessment of common cancers, such as lung cancer, lymphoma, and melanoma. This is mainly attributed to the fact that PET/CT combines the high sensitivity for tumor detection of PET and anatomical information from CT. In PET/CT image assessment, automatic tumor segmentation is an important step, and in recent years, deep learning based methods have become the state-of-the-art. Unfortunately, existing methods tend to over-segment the tumor regions and include regions such as the normal high uptake organs, inflammation, and other infections. In this study, we introduce a false positive reduction network to overcome this limitation. We firstly introduced a self-supervised pre-trained global segmentation module to coarsely delineate the candidate tumor regions using a self-supervised pre-trained encoder. The candidate tumor regions were then refined by removing false positives via a local refinement module. Our experiments with the MICCAI 2022 Automated Lesion Segmentation in Whole-Body FDG-PET/CT (AutoPET) challenge dataset showed that our method achieved a dice score of 0.9324 with the preliminary testing data and was ranked 1st place in dice on the leaderboard. Our method was also ranked in the top 7 methods on the final testing data, the final ranking will be announced during the 2022 MICCAI AutoPET workshop. Our code is available at: https://github.com/YigePeng/AutoPET_False_Positive_Reduction.
