在超声波成像上对卵巢/尾质量及其内部组件进行基于AI的自动细分
Heather M Whitney1, Roni Yoeli-Bik2, Jacques S Abramowicz3
1The University of Chicago, Department of Radiology, Chicago, Illinois, United States.
Journal of medical imaging (Bellingham, Wash.)
|August 8, 2024
概括
这项研究开发了一种人工智能工具,用于在超声波图像中对卵巢质量进行细分,准确地概述外部边界和内部组件,以便更好地进行放射性分析.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 卵巢/尾质量的细分对于诊断和治疗规划至关重要.
- 放射性特征提取需要精确细分质量边界和内部组件.
- 目前用于跨阴道超声波图像的细分方法具有挑战性.
研究的目的:
- 开发一种基于人工智能的,用于跨阴道超声波图像的自动细分方法.
- 要概述附属质量的外部边界.
- 为了分离附属质量的内部组件进行放射性分析.
主要方法:
- 使用了54个附带质量的回顾性数据库,分为训练和测试集.
- 采用U-net模型进行细分,使用子相似系数 (DSC) 和豪斯多夫距离评估性能.
- 两级模糊c-means (FCM) 集群方法被用于内部组件分离.
主要成果:
- 该U-net模型实现了0.91的中位数DSC和0.04的中位数豪斯多夫距离比,表明与专家概要有很强的一致性.
- 在临床审查中,使用FCM进行内部成分分离显示了与质量特征的强烈关联.
- 综合人工智能方法在对附带质量的外部和内部特征进行细分方面表现出高准确性.
结论:
- 一个结合U-net和FCM的AI算法有效地细分附带质量及其内部组件.
- 该方法与专家细分和临床审查有很好的一致性.
- 这种方法支持未来根据放射性特征对附属质量按其组成部分进行分类.
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