Fusing multispectral information for retinal layer segmentation

Xiang He

doi:10.1038/s41746-025-01446-z

2025-01-17

DOI：10.1038/s41746-025-01446-z

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摘要

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关于使用深度学习（DL）进行视网膜层分割（RLS）的广泛研究大多已接近性能瓶颈，这主要归因于仅依赖结构信息的局限性。为应对当前状况，我们首次研究了多光谱信息（MSI）对RLS的影响。实验结果表明，引入MSI可显著提升视网膜层光学相干断层扫描（OCT）图像的分割精度。此外，我们探究了影响MSI的主要因素，包括多光谱图像数量、光谱带宽及不同光谱组合，以评估其对RLS精度的影响。在此基础上，我们将MSI整合至RLS方法中，实现了卓越的分割效果，相关发现已在近红外和可见光光谱范围的OCT图像中得到验证。融合MSI提供了一种改进RLS精度的创新方法，进一步凸显了开源MSI信息在OCT设备中的重要性。

Keyword:

SD-OCT IMAGES

OXYGEN-SATURATION

COHERENCE

期刊

npj Digital Medicine

IF:

15.1

论文数:

3.3K

被引数:

1.5W

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