Recent progress in automated PET/CT lesion segmentation using deep learning methods has demonstrated the feasibility of this task. However, tumor lesion detection and segmentation in whole-body PET/CT is still a chal-lenging task. To promote research on machine learning-based automated tumor lesion segmentation on whole-body FDG-PET/CT data, Automated Lesion Segmentation in Whole-Body FDG-PET/CT (autoPET) challenge is held, and a large, publicly available training dataset is provided. In this report, we present our solution to the autoPET challenge. We employ multi-modal residual U-Net with deep super vision. The experimental results for five preliminary test cases show that Dice score is 0.79 +/- 0.21.
@article{arxiv.2302.12774,
title = {Automated Lesion Segmentation in Whole-Body FDG-PET/CT with Multi-modality Deep Neural Networks},
author = {Satoshi Kondo and Satoshi Kasai},
journal= {arXiv preprint arXiv:2302.12774},
year = {2023}
}
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arXiv admin note: text overlap with arXiv:2302.08016