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ISOSNet:一个统一的框架用于从AO-OCT B扫描中检测形光感应器和测量内段和外段长度

Mengxi Zhou^1,2, Yue Zhang¹, Eli Kirkendall³

¹The Ohio State University, Department of Computer Science and Engineering, 2015 Neil Ave., Columbus, OH 43210, USA.

Biomedical optics express

|August 14, 2025

概括

一个新的AI工具ISOSNet准确地检测状光受体,并使用自适应光学-光学连贯断层扫描 (AO-OCT) 成像在视网膜中测量它们的内部和外部部分.

科学领域:

眼科医生眼科眼科
生物医学成像技术生物医学成像技术
人工智能的人工智能

背景情况:

适应光学-光学连贯性断层扫描 (AO-OCT) 提供了视网膜结构的细胞分辨率.
圆光受体形态对于诊断外视网膜疾病至关重要.
目前用于结构的分析方法有限,通常只专注于检测.

研究的目的:

开发一个统一的深度学习模型,用于同时检测和测量内段 (IS) 和外段 (OS) 长度的形光感应器.
解决现有的自动化方法在分析详细的形结构特征方面的局限性.

主要方法:

一个新的神经网络ISOSNet被设计用于集成的形检测和IS/OS长度量化.
该模型使用标记的AO-OCTB扫描数据集从不同地点的健康人体视网膜中进行训练和验证.

主要成果:

对于圆检测,ISOSNet 获得了 0.886 的 F1 评分.
该模型在测量IS和OS长度方面表现出高精度,相对误差分别为6%和11%.
该框架表现出强大的通用性,在患病的视网膜图像上表现良好,尽管仅在健康数据上进行训练.

结论:

ISOSNet提供了一种强大的,自动化的解决方案,用于分析AO-OCT图像中的圆光受体结构.
这种工具具有早期检测和监测外视网膜疾病的潜力.
ISOSNet的通用性表明其在视网膜成像分析中的广泛适用性.

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