利用多标签方法改进 FDG-18 全身 PET/CT 扫描中的病灶分割：AutoPET II 挑战赛
Improving Lesion Segmentation in FDG-18 Whole-Body PET/CT scans using Multilabel approach: AutoPET II challenge
AI总结：
针对 FDG-18 全身 PET/CT 中高摄取器官易被误分为病灶的问题，本文在 AutoPET II 数据集上采用器官与病灶多标签及 nnUNet 框架训练 3D UNET，提升了病灶分割精度并获得第一名。
摘要：
使用深度学习模型对 FDG-18 全身（WB）PET/CT 扫描中的病灶进行自动分割，对于确定治疗反应、优化剂量测定以及推进肿瘤学中的治疗诊断应用具有重要作用。然而，存在放射性示踪剂摄取较高的器官，例如肝脏、脾脏、脑和膀胱，这通常会带来挑战，因为这些区域常被深度学习模型错误识别为病灶。为解决这一问题，我们提出了一种同时分割器官和病灶的新方法，旨在提升自动病灶分割方法的性能。在本研究中，我们使用包含 1014 名受试者的 AutoPET II 挑战赛数据集评估了所提方法的有效性。我们评估了加入额外标签和数据对模型分割性能的影响。除专家标注的病灶标签外，我们还引入了八个额外的器官标签，包括肝脏、肾脏、膀胱、脾脏、肺、脑、心脏和胃。这些标签被整合到数据集中，并在 nnUNet 框架内训练了一个 3D UNET 模型。我们的结果表明，该方法在预留测试数据集上取得了第一名，凸显了这种方法显著提高 FDG-18 全身 PET/CT 扫描中病灶分割准确性的潜力，最终将使癌症患者受益并推动临床实践发展。
英文摘要：
Automatic segmentation of lesions in FDG-18 Whole Body (WB) PET/CT scans using deep learning models is instrumental for determining treatment response, optimizing dosimetry, and advancing theranostic applications in oncology. However, the presence of organs with elevated radiotracer uptake, such as the liver, spleen, brain, and bladder, often leads to challenges, as these regions are often misidentified as lesions by deep learning models. To address this issue, we propose a novel approach of segmenting both organs and lesions, aiming to enhance the performance of automatic lesion segmentation methods. In this study, we assessed the effectiveness of our proposed method using the AutoPET II challenge dataset, which comprises 1014 subjects. We evaluated the impact of inclusion of additional labels and data in the segmentation performance of the model. In addition to the expert-annotated lesion labels, we introduced eight additional labels for organs, including the liver, kidneys, urinary bladder, spleen, lung, brain, heart, and stomach. These labels were integrated into the dataset, and a 3D UNET model was trained within the nnUNet framework. Our results demonstrate that our method achieved the top ranking in the held-out test dataset, underscoring the potential of this approach to significantly improve lesion segmentation accuracy in FDG-18 Whole-Body PET/CT scans, ultimately benefiting cancer patients and advancing clinical practice.
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