AI 中文总结
研究针对床旁超声图像分割计算成本高的问题，提出轻量级框架LANCANet，结合令牌条件神经细胞自动机适配器进行迭代特征细化，实验表明该方法在多数据集上性能优异，能提高分割精度与边界定位，保持计算效率。
AI 中文摘要
床旁超声（POCUS）在床边诊断和临床决策中起着重要作用，特别是在资源有限的环境中。最近的深度学习方法显著改进了超声图像分割，但计算成本限制了其在便携式和低资源设备上的部署。为应对这一挑战，我们提出了LiteAdaNCA-Net（LANCANet），这是一个轻量级超声分割框架，它结合了用于迭代特征细化的令牌条件神经细胞自动机（NCA）适配器。具体来说，结构感知令牌通过Token FiLM指导局部NCA细化，以最小的计算成本实现边界感知特征细化。我们在HC18、CCA和PSFHS上评估了LANCANet，并在从多个临床中心收集的两个独立的非洲胎儿头部数据集上评估了其鲁棒性。实验结果表明，LANCANet取得了与最近基于轻量级CNN和Transformer的方法相当或更优的性能。在HC18和CCA上，LANCANet分别实现了96.62%和92.86%的最高骰子相似系数（DSC）。在PSFHS上，它在具有挑战性的耻骨联合结构上取得了最佳性能。此外，尽管是从头开始训练，LANCANet在显著的域转移下，在外部KEN-FH和AFR-FH数据集上保持了有竞争力的性能。这些结果表明，令牌条件NCA细化提高了分割精度和边界定位，同时保持了资源受限临床部署的计算效率。我们的代码在GitHub上。
英文摘要
Point-of-Care Ultrasound (POCUS) plays an important role in bedside diagnosis and clinical decision-making, particularly in resource-constrained settings. Recent deep learning methods have substantially improved ultrasound image segmentation, enabling accurate diagnosis and biometric estimation. However, their computational cost limits deployment on portable and low-resource devices. To address this challenge, we propose LiteAdaNCA-Net (LANCANet), a lightweight ultrasound segmentation framework that incorporates token-conditioned Neural Cellular Automata (NCA) adapters for iterative feature refinement. Specifically, structure-aware tokens guide local NCA refinement via Token FiLM, enabling boundary-aware feature refinement with minimal computational cost. We evaluate LANCANet on HC18, CCA, and PSFHS, and assess robustness on two independent African fetal head datasets collected from multiple clinical centers. Experimental results demonstrate that LANCANet achieves competitive or superior performance to recent lightweight CNN- and transformer-based methods. On HC18 and CCA, LANCANet achieves the highest Dice Similarity Coefficient (DSC) of 96.62\% and 92.86\%, respectively. On PSFHS, it achieves the best performance on the challenging pubic symphysis structure while maintaining competitive fetal head segmentation accuracy. Furthermore, despite being trained from scratch, LANCANet maintains competitive performance on the external KEN-FH and AFR-FH datasets under substantial domain shifts. These results show that token-conditioned NCA refinement improves segmentation accuracy and boundary localization while maintaining computational efficiency for resource-constrained clinical deployment. Our code is on \href{https://anonymous.4open.science/r/LANCAN-21A0/README.md}{GitHub}.
Comments13 pages
