AutoPET III Challenge: PET/CT Semantic Segmentation

• URL: http://arxiv.org/abs/2409.13006v1
• Date: Thu, 19 Sep 2024 17:45:17 GMT
• Title: AutoPET III Challenge: PET/CT Semantic Segmentation
• Authors: Reza Safdari, Mohammad Koohi-Moghaddam, Kyongtae Tyler Bae,
• Abstract summary: We implemented a two-stage deep learning 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.
• Score: 0.4905104543244113
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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