仅需一次医生标注，即可实现肿瘤纵向追踪与全时序分割。
LinGuinE: Longitudinal Guidance Estimation for Volumetric Tumour Segmentation
- 通过图像配准与引导分割融合，实现跨时间的病灶级追踪
- 在4个数据集共456例中达到顶尖分割与追踪性能
- 无需训练、支持任意配准/分割算法，适合放射科研究
纵向体积肿瘤分割对放疗规划和疗效评估至关重要，但该问题研究不足，现有方法多生成单时间点语义掩码，缺乏病灶对应关系，且医生控制能力有限。我们提出LinGuinE（Longitudinal Guidance Estimation），一个PyTorch框架，结合图像配准与引导分割，仅需一次放射科医生提示，即可在纵向研究的所有扫描中实现病灶级追踪与体积分割。LinGuinE具有时间方向无关性，无需纵向数据训练，可兼容任意配准与半自动分割算法。我们在框架内评估多种算法组合，在4个数据集共456例纵向研究中取得当前最优分割与追踪表现。肿瘤分割性能随时间间隔增加几乎无下降。通过消融实验验证了自回归、病理特异性微调及真实医生提示的影响。我们开源代码并提供大规模公共基准，推动后续研究。
原文摘要 · Abstract (English)
Longitudinal volumetric tumour segmentation is critical for radiotherapy planning and response assessment, yet this problem is underexplored and most methods produce single-timepoint semantic masks, lack lesion correspondence, and offer limited radiologist control. We introduce LinGuinE (Longitudinal Guidance Estimation), a PyTorch framework that combines image registration and guided segmentation to deliver lesion-level tracking and volumetric masks across all scans in a longitudinal study from a single radiologist prompt. LinGuinE is temporally direction agnostic, requires no training on longitudinal data, and allows any registration and semi-automatic segmentation algorithm to be repurposed for the task. We evaluate various combinations of registration and segmentation algorithms within the framework. LinGuinE achieves state-of-the-art segmentation and tracking performance across four datasets with a total of 456 longitudinal studies. Tumour segmentation performance shows minimal degradation with increasing temporal separation. We conduct ablation studies to determine the impact of autoregression, pathology specific finetuning, and the use of real radiologist prompts. We release our code and substantial public benchmarking for longitudinal segmentation, facilitating future research.
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