自动分割全身多示踪剂PET-CT中的病灶，提升临床诊疗效率。
Lesion Segmentation in Whole-Body Multi-Tracer PET-CT Images; a Contribution to AutoPET 2024 Challenge
- 分三步处理：预处理、示踪剂分类、病灶分割
- 整体Dice达0.548，PSMA类达0.792（测试集）
- 适合医学影像分析与自动化诊断研究者
全身影像中病灶的自动分割具有提升诊断、预后和治疗规划的潜力。本研究通过参与AutoPET MICCAI 2024挑战，提出一个包含图像预处理、示踪剂分类和病灶分割的流程。该流程显著提升了模型分割精度，在1611例训练样本上平均总体Dice得分为0.548，训练集中FDG类和PSMA类的得分分别为0.631和0.559，初步测试集上达到0.792。
原文摘要 · Abstract (English)
The automatic segmentation of pathological regions within whole-body PET-CT volumes has the potential to streamline various clinical applications such as diagno-sis, prognosis, and treatment planning. This study aims to address this challenge by contributing to the AutoPET MICCAI 2024 challenge through a proposed workflow that incorporates image preprocessing, tracer classification, and lesion segmentation steps. The implementation of this pipeline led to a significant enhancement in the segmentation accuracy of the models. This improvement is evidenced by an average overall Dice score of 0.548 across 1611 training subjects, 0.631 and 0.559 for classi-fied FDG and PSMA subjects of the training set, and 0.792 on the preliminary testing phase dataset.
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