LUMOS提升OCT视网膜层分割精度，解决标注少与粒度不一致难题
LUMOS: Universal Semi-Supervised OCT Retinal Layer Segmentation with Hierarchical Reliable Mutual Learning
- 双分支网络+分层提示策略抑制伪标签噪声
- 跨粒度一致性对齐使平均DSC达0.912，超越现有方法
- 适合医学图像分割、弱监督学习领域研究者参考
光学相干断层扫描（OCT）视网膜层分割面临标注稀缺和数据集间标签粒度异质性的挑战。尽管半监督学习缓解了标注不足问题，但现有方法通常假设固定粒度，未能充分利用跨粒度监督。本文提出LUMOS，一种基于双解码器网络与分层提示策略（DDN-HPS）及可靠渐进式多粒度学习（RPML）的通用半监督OCT视网膜层分割框架。DDN-HPS通过双分支结构与多粒度提示策略有效抑制伪标签噪声传播；RPML引入区域级可靠性加权与渐进式训练，引导模型从简单任务逐步过渡到复杂任务，确保跨粒度一致性目标的可靠选择，实现稳定的跨粒度对齐。在六个OCT数据集上的实验表明，LUMOS显著优于现有方法，展现出卓越的跨域与跨粒度泛化能力。
原文摘要 · Abstract (English)
Optical Coherence Tomography (OCT) layer segmentation faces challenges due to annotation scarcity and heterogeneous label granularities across datasets. While semi-supervised learning helps alleviate label scarcity, existing methods typically assume a fixed granularity, failing to fully exploit cross-granularity supervision. This paper presents LUMOS, a semi-supervised universal OCT retinal layer segmentation framework based on a Dual-Decoder Network with a Hierarchical Prompting Strategy (DDN-HPS) and Reliable Progressive Multi-granularity Learning (RPML). DDN-HPS combines a dual-branch architecture with a multi-granularity prompting strategy to effectively suppress pseudo-label noise propagation. Meanwhile, RPML introduces region-level reliability weighing and a progressive training approach that guides the model from easier to more difficult tasks, ensuring the reliable selection of cross-granularity consistency targets, thereby achieving stable cross-granularity alignment. Experiments on six OCT datasets demonstrate that LUMOS largely outperforms existing methods and exhibits exceptional cross-domain and cross-granularity generalization capability.
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