Abstract
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.
Abstract (translated)
为实现准确的青光眼诊断与监测，OCT图像中可靠的视网膜层分割至关重要。然而，现有2D分割方法因缺乏相邻B扫描间的上下文信息，常出现切片间不一致问题。3D分割方法虽能更好捕捉切片间上下文，但需要高昂的计算资源。针对这些局限，我们提出一种2.5D分割框架，在类U-Net架构中引入新颖的跨切片特征融合（CFF）模块。该模块通过融合切片间特征有效捕获上下文信息，实现跨切片一致边界检测，并提升噪声区域的鲁棒性。该框架在临床数据集及公开的DUKE DME数据集上均得到验证。相较于不含CFF模块的其他分割方法，所提方法使平均绝对距离降低8.56%，均方根误差降低13.92%，展现出更优的分割精度与鲁棒性。总体而言，所提2.5D框架平衡了上下文感知能力与计算效率，可为自动化青光眼评估及潜在临床实践提供解剖学可靠的视网膜层勾画。
URL
https://arxiv.org/abs/2603.24115
