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在使用U-Net和nnU-Net的经阴道超声波中自动分离子宫
Dilara Tank1,2, Bianca G S Schor3, Lisa M Trommelen1,4
1Department of Obstetrics and Gynecology, Amsterdam University Medical Center, location Vrije Universiteit Amsterdam, Amsterdam, North-Holland, the Netherlands.
PloS one
|November 12, 2025
概括
像nnU-Net这样的深度学习模型显示出在跨阴道超声波 (TVUS) 图像中对子宫进行细分的前景. 对特定成像类型的培训,特别是3D卷,提高了这些AI工具的性能.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 妇科超声波 妇科超声波
背景情况:
- 经阴道超声波 (TVUS) 对于诊断妇科疾病至关重要.
- 人工智能驱动的细分工具正在进步,但需要对妇科超声数据进行特定应用.
研究的目的:
- 训练和评估深度学习 (DL) 分段模型 (U-Net,nnU-Net) 用于使用TVUS数据进行子宫分段.
- 评估不同成像类型 (静态,视频,3D) 对模型性能的影响.
主要方法:
- 由专家对124名患者的TVUS数据集进行子宫手动细分.
- 在整个数据集和单个成像类型上训练U-Net和nnU-Net模型.
- 使用子得分 (DSC) 进行绩效评估.
主要成果:
- 无论是U-Net还是nnU-Net都实现了高细分性能 (0.75-0.97 DSC).
- 对特定成像类型的培训优于对综合数据集的培训.
- 与U-Net相比,nnU-Net表现出更高的性能和更快的训练速度.
结论:
- TVUS数据适用于基于DL的子宫细分.
- 由于其效率和性能,推使用nnU-Net而不是U-Net.
- 创建单一成像类型,无杂乱的数据集,特别是3D卷,可以提高DL模型的性能.
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