一种基于深度学习的计算机辅助确定方法,用于在盆腔MRI图像中进行主导卵泡识别和腹卵泡计数
Jingde Hong1, Chunxia Chen2, Ming Li1
1School of Optoelectronic and Communication Engineering, Xiamen University of Technology, No.600 Ligong Road, Jimei District, Xiamen, 361024, Fujian, China.
Biomedical engineering online
|February 20, 2026
概括
一种新的计算机辅助检测 (CAD) 方法使用盆腔MRI自动化毛囊数 (AFC) 和主导毛囊识别. 这种人工智能工具减少了手工工作量,并有助于卵巢储备和生育能力的评估.
科学领域:
- 医学成像分析分析 医学成像分析
- 医疗保健中的人工智能
- 生殖医学是一种生殖医学.
背景情况:
- 盆腔磁共振成像 (MRI) 对于卵泡分析至关重要.
- 用手工解读MRI进行毛囊分析是耗时且昂贵的.
- 需要自动化方法来提高效率和准确性.
研究的目的:
- 开发和评估一个多阶段的计算机辅助测定 (CAD) 方法,用于自动化毛囊数 (AFC) 和主导毛囊识别.
- 为了克服手动基于MRI的毛囊分析的局限性.
- 为了提高卵巢储备和生育能力的评估.
主要方法:
- 从124名患者中使用了417个T2加权的MRI切片.
- 采用了你只看一次的第11版检测模型来识别卵巢结构.
- 实施了LCR-UNet,这是一个具有先进的聚合和解码模块的新型细分模型.
主要成果:
- 在毛囊细分方面获得了0.8571的Dice相似系数.
- 达到了92.86%的准确性和0.8929的F1分数,用于主要的毛囊识别.
- 自动计数毛囊显示82.91%的精度.
结论:
- 开发的CAD方法显示性能与经验丰富的放射科医生相美.
- 自动化系统显著减少了手动翻译工作量.
- 这为评估卵巢储备和生育能力提供了可靠的工具.
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