AutoPET III Challenge: PET/CT Semantic Segmentation
<|reference_start|>AutoPET III Challenge: PET/CT Semantic Segmentation: In this study, we implemented a two-stage deep learning-based approach to segment lesions in PET/CT images for the AutoPET III challenge. The first stage utilized a DynUNet model for coarse segmentation, identifying broad regions of interest. The second stage refined this segmentation using an ensemble of SwinUNETR, SegResNet, and UNet models. Preprocessing involved resampling images to a common resolution and normalization, while data augmentation techniques such as affine transformations and intensity adjustments were applied to enhance model generalization. The dataset was split into 80% training and 20% validation, excluding healthy cases. This method leverages multi-stage segmentation and model ensembling to achieve precise lesion segmentation, aiming to improve robustness and overall performance.<|reference_end|>
@article{safdari2024autopet,
    title={AutoPET III Challenge: PET/CT Semantic Segmentation},
    author={Reza Safdari, Mohammad Koohi-Moghaddam, Kyongtae Tyler Bae},
    journal={arXiv preprint arXiv:2409.13006},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13006},
    primaryClass={eess.IV cs.CV}
}