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基准分析扩散模型与最先进的架构对比,用于OCT流体生物标志物细分的OCT流体生物标志物细分

Katherine Du¹, Utkarsh Doshi¹, Benjamin DiCenzo¹

¹Department of Ophthalmology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, United States of America.

|October 29, 2025

概括

深度学习模型可以从OCT扫描中对视网膜疾病进行细分. 虽然扩散模型显示出有希望的结果,但nnU-Net在自动分析下腺液,内腺液和色素上皮脱落方面表现最好.

科学领域:

眼科医生眼科眼科
医疗成像医学成像
人工智能的人工智能

背景情况:

视网膜疾病导致显著的视力丧失,需要准确的诊断工具.
光学连贯断层扫描 (OCT) 对于评估视网膜病理至关重要.
使用深度学习的OCT扫描的自动解释可以标准化疾病评估.

研究的目的:

为了评估一个扩散模型的性能,在OCT扫描中对子液 (SRF),内液 (IRF) 和色素上皮脱落 (PED) 进行细分.
将扩散模型的细分精度与其他领先的深度学习模型进行比较.
评估自动细分在视网膜疾病管理中的临床应用的潜力.

主要方法:

在269,224和114个OCT扫描中分别对SRF,IRF和PED进行手动细分,由三个审查员进行.
使用5倍交叉验证进行扩散模型,嵌套U-Net,nnU-Net,TransUNet和SwinUNet的培训和评估.
性能指标包括子系数,灵敏度,特异性,皮尔森相关系数和R2.2.

主要成果:

所有评估的模型都与地面真相细分具有很高的相似性.
与大多数其他模型相比,扩散模型的灵敏度相对较高.
nnU-Net获得了最强的整体表现,表明在自动化OCT分析中具有卓越的准确性.

结论:

扩散模型能够比较地对视网膜病理进行细分,即使有有限的注释数据.
nnU-Net被认为是视网膜OCT扫描自动化分析的最有效模型.
这些发现支持深度学习的发展,以实现标准化和高效的视网膜疾病诊断.

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