通过交互提示提升全身CT病变长期追踪精度
Promptable Longitudinal Lesion Segmentation in Whole-Body CT
- 引入可提示的分割框架，支持点/掩码交互追踪病变
- 预训练使Dice分数最高提升6点，显著增强时序建模能力
- 适合医学影像分析、肿瘤动态监测研究者使用
纵向全身体积CT中病灶的精准分割对疾病进展与治疗反应监测至关重要。尽管自动化方法可通过引入时序信息提升性能，但其在跨时间一致追踪单个病灶方面仍受限。autoPET/CT IV挑战赛任务2提供了病灶定位与基线勾画，将问题定义为纵向可提示分割。本文在LongiSeg框架基础上扩展可提示能力，通过点与掩码交互实现病灶级追踪。针对训练数据规模有限的问题，我们利用大规模合成纵向CT数据进行预训练。实验表明，预训练显著提升了模型对时序上下文的利用能力，相比从零训练的模型，Dice分数最高提升6点。结果验证了结合时序信息与交互提示在稳健病灶追踪中的有效性。代码已公开于https://github.com/MIC-DKFZ/LongiSeg/tree/autoPET。
原文摘要 · Abstract (English)
Accurate segmentation of lesions in longitudinal whole-body CT is essential for monitoring disease progression and treatment response. While automated methods benefit from incorporating longitudinal information, they remain limited in their ability to consistently track individual lesions across time. Task 2 of the autoPET/CT IV Challenge addresses this by providing lesion localizations and baseline delineations, framing the problem as longitudinal promptable segmentation. In this work, we extend the recently proposed LongiSeg framework with promptable capabilities, enabling lesion-specific tracking through point and mask interactions. To address the limited size of the provided training set, we leverage large-scale pretraining on a synthetic longitudinal CT dataset. Our experiments show that pretraining substantially improves the ability to exploit longitudinal context, yielding an improvement of up to 6 Dice points compared to models trained from scratch. These findings demonstrate the effectiveness of combining longitudinal context with interactive prompting for robust lesion tracking. Code is publicly available at https://github.com/MIC-DKFZ/LongiSeg/tree/autoPET.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。