Tumor segmentation in medical imaging is crucial and relies on precise delineation. Fluorodeoxyglucose Positron-Emission Tomography (FDG-PET) is widely used in clinical practice to detect metabolically active tumors. However, FDG-PET scans may misinterpret irregular glucose consumption in healthy or benign tissues as cancer. Combining PET with Computed Tomography (CT) can enhance tumor segmentation by integrating metabolic and anatomic information. FDG-PET/CT scans are pivotal for cancer staging and reassessment, utilizing radiolabeled fluorodeoxyglucose to highlight metabolically active regions. Accurately distinguishing tumor-specific uptake from physiological uptake in normal tissues is a challenging aspect of precise tumor segmentation. The AutoPET challenge addresses this by providing a dataset of 1014 FDG-PET/CT studies, encouraging advancements in accurate tumor segmentation and analysis within the FDG-PET/CT domain. Code: https://github.com/matt3o/AutoPET2-Submission/

翻译：医学影像中的肿瘤分割至关重要，且依赖于精确的边界勾勒。氟代脱氧葡萄糖正电子发射断层扫描（FDG-PET）在临床实践中广泛用于检测代谢活跃的肿瘤。然而，FDG-PET扫描可能将健康或良性组织中不规则的葡萄糖消耗误判为癌症。将PET与计算机断层扫描（CT）结合，通过整合代谢与解剖信息，可增强肿瘤分割效果。FDG-PET/CT扫描利用放射性标记的氟代脱氧葡萄糖突出代谢活跃区域，对癌症分期与再评估至关重要。准确区分肿瘤特异性摄取与正常组织中的生理性摄取，是精确肿瘤分割中的一大难点。AutoPET挑战赛通过提供包含1014例FDG-PET/CT研究的数据集，致力于推动FDG-PET/CT领域精准肿瘤分割与分析的发展。代码：https://github.com/matt3o/AutoPET2-Submission/