论文标题
探索全身FDG-PET/CT扫描的Vanilla U-NET用于病变细分
Exploring Vanilla U-Net for Lesion Segmentation from Whole-body FDG-PET/CT Scans
论文作者
论文摘要
肿瘤病变分割是医学图像分析中最重要的任务之一。在临床实践中，氟脱氧葡萄糖正电子发射断层扫描〜（FDG-PET）是一种广泛使用的技术，用于识别和量化代谢活性肿瘤。但是，由于FDG-PET扫描仅提供代谢信息，因此可能会误认为葡萄糖不规则的健康组织或良性疾病。为了应对这一挑战，PET通常与计算机断层扫描〜（CT）结合使用，用于获得患者的解剖结构。基于宠物的代谢和基于CT的解剖学信息的组合可以有助于更好的肿瘤分割结果。 ％计算机断层扫描〜（CT）是一种流行的方式，可以说明患者的解剖结构。 PET和CT的组合通过利用代谢和解剖信息来应对这一挑战。在本文中，我们探讨了U-NET对全身FDG-PET/CT扫描中的病变细分的潜力，包括网络体系结构，数据预处理和数据增强。实验结果表明，具有正确输入形状的香草U-NET可以达到令人满意的性能。具体而言，我们的方法在Autopet 2022 Challenge的初步和最终排行榜中都获得了第一名。我们的代码可从https://github.com/yejin0111/autopet2022_blackbean获得。
Tumor lesion segmentation is one of the most important tasks in medical image analysis. In clinical practice, Fluorodeoxyglucose Positron-Emission Tomography~(FDG-PET) is a widely used technique to identify and quantify metabolically active tumors. However, since FDG-PET scans only provide metabolic information, healthy tissue or benign disease with irregular glucose consumption may be mistaken for cancer. To handle this challenge, PET is commonly combined with Computed Tomography~(CT), with the CT used to obtain the anatomic structure of the patient. The combination of PET-based metabolic and CT-based anatomic information can contribute to better tumor segmentation results. %Computed tomography~(CT) is a popular modality to illustrate the anatomic structure of the patient. The combination of PET and CT is promising to handle this challenge by utilizing metabolic and anatomic information. In this paper, we explore the potential of U-Net for lesion segmentation in whole-body FDG-PET/CT scans from three aspects, including network architecture, data preprocessing, and data augmentation. The experimental results demonstrate that the vanilla U-Net with proper input shape can achieve satisfactory performance. Specifically, our method achieves first place in both preliminary and final leaderboards of the autoPET 2022 challenge. Our code is available at https://github.com/Yejin0111/autoPET2022_Blackbean.
