Lesion Segmentation in Whole-Body Multi-Tracer PET-CT Images; a Contribution to AutoPET 2024 Challenge

Mehdi Astaraki, Simone Bendazzoli
9/22/2024
eess.IVcs.CV

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Abstract

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.

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Cite this paper

@article{astaraki2024lesion,
  title  = {Lesion Segmentation in Whole-Body Multi-Tracer PET-CT Images; a Contribution to AutoPET 2024 Challenge},
  author = {Mehdi Astaraki and Simone Bendazzoli},
  year   = {2024},
  eprint = {2409.14475},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2409.14475v1}
}

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