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

AutoPET Challenge 2023: Sliding Window-based Optimization of U-Net