  This study presents FP-PET, a comprehensive approach to medical image
segmentation with a focus on CT and PET images. Utilizing a dataset from the
AutoPet2023 Challenge, the research employs a variety of machine learning
models, including STUNet-large, SwinUNETR, and VNet, to achieve
state-of-the-art segmentation performance. The paper introduces an aggregated
score that combines multiple evaluation metrics such as Dice score, false
positive volume (FPV), and false negative volume (FNV) to provide a holistic
measure of model effectiveness. The study also discusses the computational
challenges and solutions related to model training, which was conducted on
high-performance GPUs. Preprocessing and postprocessing techniques, including
gaussian weighting schemes and morphological operations, are explored to
further refine the segmentation output. The research offers valuable insights
into the challenges and solutions for advanced medical image segmentation.
