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This study presents FP-PET, a comprehensive approach to medical imagesegmentation with a focus on CT and PET images. Utilizing a dataset from theAutoPet2023 Challenge, the research employs a variety of machine learningmodels, including STUNet-large, SwinUNETR, and VNet, to achievestate-of-the-art segmentation performance. The paper introduces an aggregatedscore that combines multiple evaluation metrics such as Dice score, falsepositive volume (FPV), and false negative volume (FNV) to provide a holisticmeasure of model effectiveness. The study also discusses the computationalchallenges and solutions related to model training, which was conducted onhigh-performance GPUs. Preprocessing and postprocessing techniques, includinggaussian weighting schemes and morphological operations, are explored tofurther refine the segmentation output. The research offers valuable insightsinto the challenges and solutions for advanced medical image segmentation.

FP-PET: Large Model, Multiple Loss And Focused Practice