不确定性估计对放射性细分的可重现性和在脏MRI中扫描-重新扫描重复性的影响
Rossella Damiano1, Ettore Lanzarone2, Francesca Lussana2
1Institute of Biomedical Technologies (ITB), National Research Council (CNR), Milan, Italy.
Medical physics
|July 16, 2025
概括
深度学习细分中的不确定性量化显著提高了放射性特征的稳定性和可重复性. 这种方法提高了放射性分析的可靠性,以区分健康和病态受试者,帮助临床决策.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 无线电学 (Radiomics) 是一种无线电学.
背景情况:
- 放射学具有潜力,但由于细分和扫描-重新扫描的可变性,它在可重复性和可重复性方面面临挑战.
- 深度学习 (DL) 在提高细分精度和放射性稳定性方面表现有前途.
- 在DL模型中,不确定性量化可以提高细分信心,提高放射性输出可靠性.
研究的目的:
- 调查从有信心的细分区域提取放射性特征是否可以提高可重复性和可重复性.
- 评估来自不确定性告知细分的稳定特征是否改善了健康与病态受试者的分类.
主要方法:
- 使用了患有慢性病 (CKD) 和健康对照 (HC) 的脏MRI数据集.
- 应用了确定性U-Net,蒙特卡洛丢失 (MCD) 和测试时间增大 (TTA) 进行细分,与Dice和Dice Plus损失.
- 从基准真相,确定性和基于信心水平的面具中提取放射性特征,以评估变异性和改进分类.
主要成果:
- 在不同模型中,细分的准确性是可比的 (子分数~0.9).
- 不确定性量化显著增加了可重复的放射性特征,特别是MCD和Dice Plus损失 (在105分中24到70分).
- 基于不确定性的方法中的分类性能与参考结果相似.
结论:
- 将不确定性量化整合到基于DL的细分中,可以提高放射性分析对变化的稳定性.
- 这种方法提高了放射性分析的可靠性和可解释性,用于临床决策.
- 基于不确定性的放射学表明,它有望区分病态和健康的受试者.
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