用深度学习提升PET/CT影像中病灶分割精度
Enhancing Lesion Segmentation in PET/CT Imaging with Deep Learning and Advanced Data Preprocessing Techniques
- 结合预处理与数据增强，优化深度学习模型输入
- 在900例FDG-PET/CT和600例PSMA-PET/CT上验证效果
- 适合关注医学影像分析与癌症诊断的科研人员
全球癌症负担持续上升，亟需精准的诊断工具。本研究利用深度学习提升PET/CT影像中的病灶分割性能，基于来自AutoPET挑战赛III的900例全身FDG-PET/CT和600例PSMA-PET/CT数据集。通过系统性地设计预处理流程与数据增强策略，包括非零归一化、引入RandGaussianSharpen以及调整Gamma变换参数，显著提升了模型的鲁棒性与泛化能力。研究旨在推动PET/CT影像标准化预处理与增强方法的发展，有望提升癌症诊断准确率及个体化治疗管理。代码将开源，地址为https://github.com/jiayiliu-pku/DC2024。
原文摘要 · Abstract (English)
The escalating global cancer burden underscores the critical need for precise diagnostic tools in oncology. This research employs deep learning to enhance lesion segmentation in PET/CT imaging, utilizing a dataset of 900 whole-body FDG-PET/CT and 600 PSMA-PET/CT studies from the AutoPET challenge III. Our methodical approach includes robust preprocessing and data augmentation techniques to ensure model robustness and generalizability. We investigate the influence of non-zero normalization and modifications to the data augmentation pipeline, such as the introduction of RandGaussianSharpen and adjustments to the Gamma transform parameter. This study aims to contribute to the standardization of preprocessing and augmentation strategies in PET/CT imaging, potentially improving the diagnostic accuracy and the personalized management of cancer patients. Our code will be open-sourced and available at https://github.com/jiayiliu-pku/DC2024.
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