多阶段模型融合提升PET/CT图像病灶分割精度
AutoPET III Challenge: PET/CT Semantic Segmentation
- 两阶段设计：先用DynUNet粗分割，再用三模型集成精调
- 在AutoPET III挑战数据集上实现高精度病灶定位与边界细化
- 适合医学影像分析、放射科辅助诊断研究者参考
本研究针对AutoPET III挑战，提出一种两阶段深度学习方法对PET/CT图像中的病灶进行语义分割。第一阶段采用DynUNet模型进行粗略分割，识别感兴趣区域；第二阶段通过SwinUNETR、SegResNet和UNet模型的集成进行精细化分割。预处理包括图像重采样至统一分辨率及归一化，数据增强采用仿射变换和强度调整以提升模型泛化能力。数据集按80%训练、20%验证划分，剔除健康病例。该方法结合多阶段分割与模型集成策略，显著提升病灶分割的准确性与鲁棒性。
原文摘要 · Abstract (English)
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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