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图像预处理对皮肤学OCTA血管细分的影响:DERMA-OCTA研究

Giulia Rotunno¹, Massimo Salvi¹, Julia Deinsberger²

¹Politecnico di Torino, Department of Electronics and Telecommunications, Polito BIOMed Lab, Torino, Italy.

Journal of biomedical optics

|November 26, 2025

概括

预处理步骤显著影响皮肤微血管分析的深度学习,使用光学连贯性断层扫描血管学 (OCTA). 优化的预处理和组合的2D/3D模型提高了船舶细分的准确性.

科学领域:

皮肤病学皮肤病学
医疗成像医学成像
人工智能的人工智能

背景情况:

光学连贯断层扫描血管造影 (OCTA) 提供非侵入性,皮肤微血管的3D可视化.
在OCTA分析中的挑战包括各种预处理,有限的数据和船舶架构不充分的评估指标.

研究的目的:

评估预处理技术对OCTA基于深度学习的船舶细分的影响.
确定皮肤学OCTA数据的最佳深度学习架构和评估指标.

主要方法:

使用了DERMA-OCTA数据集,其中包含330个卷和5个预处理变体.
应用2D和3D深度学习模型用于船舶细分.
使用标准指标和连接区域长度指数评估性能.

主要成果:

Bscan规范化,工件减弱和对比度增强提高了细分的准确性.
船体过对性能产生了负面影响.
2D模型在整体上表现出色,而3D模型在更深层组织中表现更好.
模型面临着跨多种病理的泛化挑战.

结论:

组合的2D/3D模型提供了全面的分析.
拓意识索引为OCTA血管细分提供了临床相关的性能评估.

关键词:

深度学习是一种深度学习.光学连贯性断层扫描血管图谱细分化细分化的细分化皮肤成像皮肤成像船舶分析船舶分析

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