一个深度学习框架,以使用水平可见虹膜直径将线性面部测量量量为实际尺寸:对伊朗人口的一项研究
Zeynab Pirayesh1,2, Sahel Hassanzadeh-Samani2,3, Arash Farzan1
1Department of Orthodontics and Dentofacial Orthopedics, School of Dentistry, Zanjan University of Medical Sciences, Zanjan, Iran.
Scientific reports
|August 23, 2023
概括
现在可以使用新的深度学习工具准确测量数字图像,该工具将图像放大与虹膜直径校准. 该方法从照片中提供精确的线性测量,用于临床研究和评估.
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:
- 数字成像可以客观地评估面部特征和治疗结果.
- 自动摄影评估可以取代手动临床测量.
- 图像放大变化阻碍了从照片中准确的毫米测量.
研究的目的:
- 开发一种深度学习工具,用于估计未知放大度的数字图像上的线性测量.
- 用虹膜直径作为可靠的尺度来校准图像放大.
主要方法:
- 设计了一个框架来分割虹膜,并以像素计算水平可见虹膜直径 (HVID).
- 一个恒定的HVID值为12.2mm被分配.
- 线性距离用像素测量,然后用HVID. 来计算的放大比率用毫米估计.
- 布兰德-阿尔特曼分析将手动测量与估计测量的比较.
主要成果:
- 深度学习工具的平均绝对百分比误差 (MAPE) 为2.9%的水平测量和4.3%的垂直测量.
- 虹膜直径被证明是图像校准的一致和可靠的尺度.
- 该方法允许从数字图像中进行精确的毫米测量.
结论:
- 虹膜直径是一种可靠的尺度,用于校准数字照片中的图像放大.
- 这种深度学习方法可以为临床研究和评估提供准确的线性测量.
- 这些发现支持基于虹膜的校准用于客观的摄影评估.
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