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

• URL: http://arxiv.org/abs/2409.14475v1
• Date: Sun, 22 Sep 2024 14:50:46 GMT
• Title: Lesion Segmentation in Whole-Body Multi-Tracer PET-CT Images; a Contribution to AutoPET 2024 Challenge
• Abstract summary: This study contributes 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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