CONSeg:使用合规预测对质瘤细分的声向不确定性量化
Danial Elyassirad1, Benyamin Gheiji1, Mahsa Vatanparast1
1From the Student Research Committee (D.E., B.G, M.V.) and Department of Radiology (A.M.A.), Mashhad University of Medical Sciences, Mashhad, Iran; Department of Radiology (S.F.), Mayo Clinic, Rochester, Minnesota, United States.
AJNR. American journal of neuroradiology
|July 3, 2025
概括
符合性预测 (CP) 有效量化了质瘤细分的不确定性,提高了模型可靠性,并确定了需要手动审查的病例. 这种方法通过区分某些和不确定的细分来增强临床决策.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 神经瘤学神经瘤学
背景情况:
- 准确的质瘤细分对于临床决策和治疗规划至关重要.
- 不确定性量化 (UQ) 方法,如合规预测 (CP),提高了细分模型的可靠性.
- 在医疗图像分析中,CP为UQ提供了统计可靠性保证.
研究的目的:
- 实施符合性预测 (CP) 在质瘤细分中的不确定性量化.
- 评估CP增强型质瘤细分模型的可靠性和性能.
- 评估不确定性指标与细分精度之间的相关性.
主要方法:
- 利用UCSF和UPenn质瘤数据集进行培训,验证,校准和测试.
- 训练了一个UNet模型,并应用了0.5.5的最佳值的预测正常化.
- 通过根据校准不符合性得分选择符合性值来实现CP.
- 定义了一个不确定性比率 (UR),并评估其与子得分系数 (DSC) 和豪斯多夫距离95 (HD95) 的相关性.
主要成果:
- 基本模型实现了0.86 (内部) 和0.83 (外部) 的DSC,HD95分别为7.35和11.71.
- CP显示了高覆盖率 (0.9982内部,0.9977外部).
- 在UR和细分指标 (DSC,HD95) (p < 0.001) 之间发现了显著的相关性.
- 被归类为"确定"的案例显示出明显更好的细分表现,而不是"不确定"的案例 (p < 0.001).
结论:
- 符合性预测 (CP) 有效量化了质瘤细分的不确定性,提高了模型的可靠性.
- 拟议的符合细分 (CONSeg) 方法通过识别不确定的细分来改善人机交互.
- 康塞格可以标记不确定的病例,建议它们进行手动细分,并改善整体临床工作流程.
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