使用基于StyleGAN2的模型适应技术,对裂面进行无监督的异常评估
Abdullah Hayajneh1, Mohammad Shaqfeh2, Erchin Serpedin1
1Electrical and Computer Engineering Department, Texas A&M University, College Station, TX, United States of America.
PloS one
|August 3, 2023
概括
一个新的机器学习模型使用人工智能驱动的面部分析准确地测量了裂唇严重程度. 这种自动化方法为评估先天性面部形提供了客观的实时临床数据.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 先天性裂唇异常对于客观评估来说是一个重大的临床挑战.
- 现有的测量变形的方法缺乏临床可行性和客观性.
- 需要可靠的工具来量化基线形和手术结果.
研究的目的:
- 引入一种新的机器学习框架,用于检测,定位和测量裂唇异常严重程度.
- 提供客观和临床可行的方法来评估面部形和手术变化.
- 建立一个新的标准,用于自动化,实时临床测量裂唇.
主要方法:
- 使用StyleGAN2生成对抗网络进行面部正常化.
- 采用像素式减去方法来量化变形严重程度.
- 开发了一个管道,包括图像预处理,热图生成和异常评分.
主要成果:
- 该框架与人类评级有很高的相关性 (皮尔森的r=0.89).
- 像素智能测量技术的性能优于现有的最先进的图像质量指标.
- 热图视觉证实了生成的异常得分,突出了解剖学异常.
结论:
- 拟议的机器学习框架为测量裂唇严重程度提供了一个客观和自动化的解决方案.
- 这项技术有可能成为临床评估先天性面部形的新标准.
- 该模型提供了一种可行的方法来跟踪与重建性手术相关的变化.
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