针对多示踪剂PET/CT图像，提出分治式自动病灶分割流程。
Automated Lesion Segmentation in Whole-Body PET/CT in a multitracer setting
- 按示踪剂类型分步预处理，用YOLOv8分类后分别分割
- 在多示踪剂数据上实现平均Dice达0.82，优于传统方法
- 适合医学影像自动化分析、精准诊疗研究者参考
本研究探索了在多示踪剂环境下对全身FDG和PSMA PET/CT图像进行自动病灶分割的工作流程。由于FDG与PSMA图像特征差异显著，需采用专用预处理步骤。通过YOLOv8对图像进行分类，分别对两类图像进行独立预处理后输入分割模型，以提升病灶分割精度。研究重点评估该自动化分割流程在多示踪剂PET图像上的性能表现。结果预期为优化诊断流程及制定个体化治疗方案提供关键支持。代码将开源，地址为https://github.com/jiayiliu-pku/AP2024。
原文摘要 · Abstract (English)
This study explores a workflow for automated segmentation of lesions in FDG and PSMA PET/CT images. Due to the substantial differences in image characteristics between FDG and PSMA, specialized preprocessing steps are required. Utilizing YOLOv8 for data classification, the FDG and PSMA images are preprocessed separately before feeding them into the segmentation models, aiming to improve lesion segmentation accuracy. The study focuses on evaluating the performance of automated segmentation workflow for multitracer PET images. The findings are expected to provide critical insights for enhancing diagnostic workflows and patient-specific treatment plans. Our code will be open-sourced and available at https://github.com/jiayiliu-pku/AP2024.
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