AI 中文总结
研究光学相干断层扫描中跨域视网膜层分割难题，引入以中央凹为中心的归一化框架减轻域偏移，结合多种度量评估分割质量，证明空间归一化对开发可靠视网膜分析工具、提取生物标志物及下游计算分析的重要性。
AI 中文摘要
光学相干断层扫描（OCT）中的视网膜层分割是提取视网膜结构定量生物标志物的基本步骤。在神经退行性疾病背景下对OCT分析的兴趣日益增长。然而，由于斑点噪声、阴影伪影、相邻层之间的低对比度、个体间的解剖变异以及不同采集协议和临床人群引起的域偏移，分割仍然具有挑战性。深度学习方法虽取得显著性能，但其在异构数据集上的鲁棒性和泛化性仍有限。本文研究空间归一化作为预处理策略以减轻几何域偏移并提高视网膜层分割一致性的作用。受神经成像标准做法启发，引入以中央凹为中心的归一化框架将OCT体积对齐到共同解剖参考。对现有深度学习架构进行全面评估，结合多种度量方法评估分割质量。结果表明空间归一化在OCT分割管道中对开发强大且具有临床意义的视网膜分析工具很重要，有助于在神经退行性研究中可靠提取生物标志物和进行下游计算分析。
英文摘要
Retinal layer segmentation in Optical Coherence Tomography (OCT) is a fundamental step for extracting quantitative biomarkers of retinal structure. Indeed, there is a growing interest in the analysis of OCTs in the context of neurodegenerative diseases. However, segmentation remains challenging due to speckle noise, shadowing artifacts, low contrast between adjacent layers, anatomical variability across subjects, and domain shifts arising from different acquisition protocols and clinical populations. While deep learning methods have achieved remarkable performance, their robustness and generalization across heterogeneous datasets remain limited. In this work, we investigate the role of spatial normalization as a preprocessing strategy to mitigate geometric domain shifts and improve the consistency of retinal layer segmentation. Inspired by standard practices in neuroimaging, we introduce a fovea-centered normalization framework that aligns OCT volumes into a common anatomical reference. We perform a comprehensive evaluation of state-of-the-art deep learning architectures. To provide a comprehensive assessment of segmentation quality, we combine conventional overlap-based metrics at B-scan level with topology-aware metrics at A-scan level and thickness-based measures at the en-face level. In cases where a ground truth is not available, we propose topology violation quantitative metrics that do not require ground truth annotations and a thickness-based qualitative assessment that captures structural consistency and clinically relevant patterns at the en-face level. The results demonstrate the importance of spatial normalization in OCT segmentation pipelines toward the development of robust and clinically meaningful retinal analysis tools, enabling reliable biomarker extraction and downstream computational analysis in neurodegenerative research.
