计算机视觉用于在体外受精中自动识别胚胎细胞结构和胚胎细胞形成时间
María Villota1, Jacobo Ayensa-Jiménez1, Clara Malo1
1Aragón Institute of Engineering Research (I3A), University of Zaragoza, 50018, Aragón, Spain; Institute for Health Research Aragón (IIS Aragón), 50009, Aragón, Spain.
Computers in biology and medicine
|July 5, 2025
概括
现在,自动计算机视觉技术可以对人类胚胎囊胚进行细分,从而提高了体外受精成功率. 这项技术提供了客观分析,帮助胚胎学家并推进辅助生殖.
科学领域:
- 生物医学工程 生物医学工程
- 生殖医学 生殖医学
- 计算机视觉 计算机视觉
背景情况:
- 胚胎选择用于体外受精 (IVF) 是至关重要的,但主观和耗时.
- 目前的试管婴儿成功率仍然低于最佳水平,即使使用高质量的胚胎,怀孕率也为34.1%.
- 需要客观的胚胎分析来减少胚胎学家的工作量并改善植入结果.
研究的目的:
- 开发和介绍计算机视觉方法,用于人类胚胎细胞结构的自动细分.
- 为了准确地确定膨胀胚胎细胞形成的时间.
- 提高胚胎囊分析在辅助生殖中的可靠性和可比性.
主要方法:
- 实现各种计算机视觉算法用于图像分析.
- 在胚胎囊中自动细分关键结构.
- 使用子得分指标验证细分精度.
- 精确的时间识别膨胀的胚胎细胞阶段.
主要成果:
- 在胚胎囊结构细分方面取得了高准确性,子得分高达0.89.
- 准确地确定了扩张芽细胞形成的时间,平均误差在4小时以下.
- 开发了客观的,定量数据用于胚胎评估.
结论:
- 计算机视觉为试管婴儿中客观和高效的胚胎细胞分析提供了一个有价值的工具.
- 自动细分和定时可以显著帮助胚胎学家,提高试管婴儿成功率.
- 开源代码促进了辅助生殖技术的复制和进一步研究.
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