Lesion Segmentation in Whole-Body Multi-Tracer PET-CT Images; a Contribution to AutoPET 2024 Challenge
<|reference_start|>Lesion Segmentation in Whole-Body Multi-Tracer PET-CT Images; a Contribution to AutoPET 2024 Challenge: The automatic segmentation of pathological regions within whole-body PET-CT volumes has the potential to streamline various clinical applications such as diagno-sis, prognosis, and treatment planning. This study aims to address this challenge by contributing to the AutoPET MICCAI 2024 challenge through a proposed workflow that incorporates image preprocessing, tracer classification, and lesion segmentation steps. The implementation of this pipeline led to a significant enhancement in the segmentation accuracy of the models. This improvement is evidenced by an average overall Dice score of 0.548 across 1611 training subjects, 0.631 and 0.559 for classi-fied FDG and PSMA subjects of the training set, and 0.792 on the preliminary testing phase dataset.<|reference_end|>
@article{astaraki2024lesion,
    title={Lesion Segmentation in Whole-Body Multi-Tracer PET-CT Images; a
  Contribution to AutoPET 2024 Challenge},
    author={Mehdi Astaraki, Simone Bendazzoli},
    journal={arXiv preprint arXiv:2409.14475},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.14475},
    primaryClass={eess.IV cs.CV}
}