用跨切片特征融合提升OCT图像视网膜层分割精度，助力青光眼自动评估。
Retinal Layer Segmentation in OCT Images With 2.5D Cross-slice Feature Fusion Module for Glaucoma Assessment
- 设计2.5D框架，通过跨切片特征融合模块捕捉相邻B-scan上下文信息。
- 相比无该模块方法，平均绝对距离降低8.56%，均方根误差减少13.92%。
- 兼顾精度与效率，适合临床场景下的青光眼自动化诊断应用。
为实现精准的青光眼诊断与监测，准确分割OCT图像中的视网膜层至关重要。然而，现有2D分割方法因缺乏相邻B-scan间的上下文信息，常出现切片间不一致问题；3D方法虽能更好捕捉上下文，但计算成本高昂。为此，本文提出一种2.5D分割框架，将新型跨切片特征融合（CFF）模块嵌入类似U-Net的架构中。CFF模块融合切片间特征，有效捕捉上下文信息，实现切片间边界的一致性检测，并提升噪声区域的鲁棒性。在临床数据集和公开的DUKE DME数据集上验证表明，相较于无CFF模块的方法，本方法使平均绝对距离降低8.56%，均方根误差减少13.92%，显著提升分割精度与鲁棒性。整体框架在上下文感知与计算效率间取得平衡，支持解剖学可靠的视网膜层划分，适用于自动化青光眼评估及潜在临床应用。
原文摘要 · Abstract (English)
For accurate glaucoma diagnosis and monitoring, reliable retinal layer segmentation in OCT images is essential. However, existing 2D segmentation methods often suffer from slice-to-slice inconsistencies due to the lack of contextual information across adjacent B-scans. 3D segmentation methods are better for capturing slice-to-slice context, but they require expensive computational resources. To address these limitations, we propose a 2.5D segmentation framework that incorporates a novel cross-slice feature fusion (CFF) module into a U-Net-like architecture. The CFF module fuses inter-slice features to effectively capture contextual information, enabling consistent boundary detection across slices and improved robustness in noisy regions. The framework was validated on both a clinical dataset and the publicly available DUKE DME dataset. Compared to other segmentation methods without the CFF module, the proposed method achieved an 8.56% reduction in mean absolute distance and a 13.92% reduction in root mean square error, demonstrating improved segmentation accuracy and robustness. Overall, the proposed 2.5D framework balances contextual awareness and computational efficiency, enabling anatomically reliable retinal layer delineation for automated glaucoma evaluation and potential clinical applications.
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