用于全身PET/CT图像中自动肿瘤分割的定位到分割框架
A Localization-to-Segmentation Framework for Automatic Tumor Segmentation in Whole-Body PET/CT Images
AI总结：
本文提出一种定位到分割框架L2SNet，通过病灶定位与分割两阶段及自适应阈值方案解决全身PET/CT图像中肿瘤精确分割难题，在MICCAI 2023挑战数据集上取得前7的竞争性结果。
摘要：
氟脱氧葡萄糖（FDG）正电子发射断层扫描（PET）结合计算机断层扫描（CT）被认为是检测某些癌症（如肺癌和黑色素瘤）的主要解决方案。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，由于许多肿瘤体积小且高摄取正常区域与肿瘤区域相似，精确的肿瘤分割具有挑战性。为了解决这些问题，本文提出了一种定位到分割框架（L2SNet）用于精确的肿瘤分割。L2SNet首先在病灶定位阶段定位可能的病灶，然后在病灶分割阶段利用位置线索来塑造分割结果。为了进一步提高L2SNet的分割性能，我们设计了一种自适应阈值方案，该方案考虑了两个阶段的分割结果。使用MICCAI 2023全身FDG-PET/CT挑战数据集进行的自动病灶分割实验表明，我们的方法取得了有竞争力的结果，并在初步测试集上排名前7。我们的工作可在以下网址获取：https://github.com/MedCAI/L2SNet。
英文摘要：
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.
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