Lesion Segmentation in Whole-Body Multi-Tracer PET-CT Images; a Contribution to AutoPET 2024 Challenge
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 4 of its 8 references that are in the map. First posted on arXiv 22 Sep 2024.
Builds on
4 of 8 references.
References · 4
- 2021The Medical Segmentation Decathloncited by 628
- 2022TotalSegmentator: robust segmentation of 104 anatomical structures in CT imagescited by 613
- 20183D MRI brain tumor segmentation using autoencoder regularizationcited by 328
- 2024nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentationcited by 240
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Citing papers per year: 2026: 2.
2 papers.