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.

翻译：纵向体积肿瘤分割对于放疗规划和疗效评估至关重要，然而该问题尚未得到充分探索，现有方法大多仅生成单时间点的语义分割掩码，缺乏病灶对应关系，且为放射科医生提供的控制能力有限。我们提出了LinGuinE（纵向引导估计），这是一个基于PyTorch的框架，它结合了图像配准与引导分割技术，能够根据放射科医生的单次提示，在纵向研究的所有扫描图像中实现病灶级别的追踪和体积掩码生成。LinGuinE对时间方向不敏感，无需在纵向数据上进行训练，并且允许将任何配准和半自动分割算法重新用于此任务。我们在该框架内评估了多种配准与分割算法的组合。LinGuinE在总计包含456项纵向研究的四个数据集上，实现了最先进的分割和追踪性能。肿瘤分割性能随时间间隔增大而出现的退化极小。我们进行了消融研究，以评估自回归、针对特定病理的微调以及使用真实放射科医生提示的影响。我们公开了代码和用于纵向分割的大量公共基准测试，以促进未来的研究。