孟加拉OCT2025:一个以人口为特征的以Fovea为中心的OCT数据集,使用翻转式旋转变压器进行自我监督的体积测量恢复
Chinmay Bepery1, G M Atiqur Rahaman2, Rameswar Debnath2
1Department of Computer Science and Information Technology, Patuakhali Science and Technology University, Patuakhali 8602, Bangladesh.
Diagnostics (Basel, Switzerland)
|February 13, 2026
概括
这项研究介绍了BanglaOCT2025,一个用于与年龄相关的黄斑变性 (AMD) 研究的多样化数据集,以及一个新的AI管道,以提高光学连贯断层扫描 (OCT) 扫描的诊断精度.
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:
- 与年龄相关的黄斑变性 (AMD) 是导致视力丧失的主要原因.
- 现有的光学一致性断层扫描 (OCT) 数据集缺乏人口多样性,特别是来自南亚人口,限制了AI的概括性.
- 原始的OCT卷包含噪音和冗余的空间信息,阻碍了分析.
研究的目的:
- 介绍BanglaOCT2025,这是第一个经过临床验证的代表孟加拉人口的OCT数据集.
- 开发一种新的预处理管道,用于以叶为中心的OCT体积分析.
- 通过先进的否定技术,提高AI驱动的AMD分类准确性.
主要方法:
- 从孟加拉国国家眼科研究所和医院收集了1585个OCT卷.
- 开发了一种基于约束的算法,用于自动定位foveal中心和固定子体积提取.
- 实现了一种自主监督的翻转式旋转变压器 (FFSwin),用于体积光斑噪声抑制.
主要成果:
- 孟加拉国OCT2025包括857例专家注释的AMD病例 (干性AMD,湿性AMD,非AMD).
- 在临床审查中,denoising保留了病理生物标志物,并且没有显示出幻觉.
- 随着拟议的管道,AMD分类的准确性从69.08%提高到99.88%.
结论:
- 孟加拉OCT2025是第一个代表孟加拉人口的OCT数据集,用于AMD研究.
- 建立了一个可重复的框架,用于以叶为中心的体积预处理和恢复在OCT分析.
- 开发的方法显示出在AMD检测中提高诊断准确性的巨大潜力.
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