Fluorodeoxyglucose (FDG) positron emission tomography(PET) combined with computed tomography (CT) is considered the primary solution for detecting some cancers, such as lung cancer and melanoma. Automatic segmentation of tumors in PET/CT images can help reduce doctors' workload, thereby improving diagnostic quality. However, precise tumor segmentation is challenging due to the small size of many tumors and the similarity of high-uptake normal areas to the tumor regions. To address these issues, this paper proposes a localization-to-segmentation framework (L2SNet) for precise tumor segmentation. L2SNet first localizes the possible lesions in the lesion localization phase and then uses the location cues to shape the segmentation results in the lesion segmentation phase. To further improve the segmentation performance of L2SNet, we design an adaptive threshold scheme that takes the segmentation results of the two phases into consideration. The experiments with the MICCAI 2023 Automated Lesion Segmentation in Whole-Body FDG-PET/CT challenge dataset show that our method achieved a competitive result and was ranked in the top 7 methods on the preliminary test set. Our work is available at: https://github.com/MedCAI/L2SNet.

翻译：氟脱氧葡萄糖（FDG）正电子发射断层扫描（PET）联合计算机断层扫描（CT）被认为是检测某些癌症（如肺癌和黑色素瘤）的主要方案。通过PET/CT图像中肿瘤的自动分割，有助于减轻医生的工作量，从而提高诊断质量。然而，由于许多肿瘤体积较小且高摄取正常区域与肿瘤区域相似，精确的肿瘤分割极具挑战性。为解决这些问题，本文提出了一种基于定位到分割的框架（L2SNet）以实现精确的肿瘤分割。L2SNet首先在病灶定位阶段定位可能的病变区域，随后在病灶分割阶段利用定位线索形成分割结果。为进一步提升L2SNet的分割性能，我们设计了一个自适应阈值方案，同时考虑两个阶段的分割结果。基于MICCAI 2023全身FDG-PET/CT自动病变分割挑战赛数据集的实验表明，我们的方法取得了有竞争力的结果，并在初步测试集中排名前七。我们的工作已开源：https://github.com/MedCAI/L2SNet。