Despite advances with deep learning (DL), automated airway segmentation from chest CT scans continues to face challenges in segmentation quality and generalization across cohorts. To address these, we propose integrating Curriculum Learning (CL) into airway segmentation networks, distributing the training set into batches according to ad-hoc complexity scores derived from CT scans and corresponding ground-truth tree features. We specifically investigate few-shot domain adaptation, targeting scenarios where manual annotation of a full fine-tuning dataset is prohibitively expensive. Results are reported on two large open-cohorts (ATM22 and AIIB23) with high performance using CL for full training (Source domain) and few-shot fine-tuning (Target domain), but with also some insights on potential detrimental effects if using a classic Bootstrapping scoring function or if not using proper scan sequencing.

翻译：尽管深度学习（DL）取得了进展，但从胸部CT扫描中自动分割气道在分割质量和跨队列泛化方面仍面临挑战。为解决这些问题，我们提出将课程学习（CL）整合到气道分割网络中，根据从CT扫描和相应真实树特征导出的临时复杂度评分，将训练集分配到批次中。我们特别研究了少样本域适应，针对手动标注完整微调数据集成本过高的情况。结果在两个大型开放队列（ATM22和AIIB23）上报告，使用CL进行完整训练（源域）和少样本微调（目标域）均取得了高性能，但也揭示了如果使用经典的自举评分函数或未采用适当的扫描序列可能产生的潜在不利影响。