这篇论文解决了OCT分割跨域泛化难题，用空间归一化让不同来源的数据对齐，实验扎实，适合研究视网膜影像和医学图像分析的朋友。
该论文针对OCT图像中视网膜层分割因散斑噪声、伪影、域偏移等挑战，提出一种以中央凹为中心的空间归一化预处理框架。该方法在7种深度学习架构上评估，结合B-scan级的重叠度量、A-scan级的拓扑感知度量和en-face级的厚度度量。无真实标签时，论文提出无需标注的拓扑违反定量指标和基于厚度的定性评估。实验表明空间归一化显著提升分割鲁棒性和一致性，有助于神经退行性疾病中的生物标志物提取。
Spatial Normalization for Cross-Domain Retinal Layer Segmentation in Optical Coherence Tomography
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.