灵魂:一个基于人机协作注释框架的OCTA数据集
Jingyan Xue1, Zhenhua Feng2, Lili Zeng1
1School of Computer Science and Technology, Beijing Jiaotong University, Beijing, 100044, China.
Scientific data
|August 2, 2024
概括
为了分支视网膜静脉封闭 (BRVO) 研究,开发了新的数据集Soul和人机注释框架. 这个资源有助于分析视网膜血管疾病,使用先进的成像技术,如OCTA.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 计算机视觉 计算机视觉
背景情况:
- 分支视网膜静脉封闭 (BRVO) 是由于静脉压力增加而导致视力受损的主要原因.
- 光学连贯断层扫描血管图 (OCTA) 提供高分辨率的3D视网膜血管图像.
- 现有的数据集缺乏对BRVO和全面注释的关注,阻碍了研究.
研究的目的:
- 为了介绍灵魂数据集,专门为BRVO研究策划.
- 为高效的数据标签提出一个人机协作注释框架 (HMCAF).
- 为了促进机器学习应用在分析视网膜血管疾病.
主要方法:
- 灵魂数据集的开发,包括原始图像,血管标签和临床数据.
- 数据集根据注射频率和随访时间分为6个子集的分类.
- 实施HMCAF用于注释编码的视网膜血管数据.
主要成果:
- 创建一个专门的BRVO数据集 (Soul),包含各种各样的子集.
- 建立一个协作框架 (HMCAF) 以实现高效和准确的注释.
- 为眼科医学的机器学习模型开发提供了有价值的资源.
结论:
- 灵魂数据集和HMCAF为BRVO研究提供了重大进展.
- 这种资源使得视网膜血管疾病的机器学习驱动分析更有效.
- 未来的研究可以利用这个数据集来改进诊断和预后工具.
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