在腹部CT扫描中深度学习细分ascites用于自动体积定量自动体积定量
Benjamin Hou1, Sung-Won Lee2, Jung-Min Lee3
1Radiology and Imaging Sciences, Clinical Center - National Institutes of Health, Bethesda, MD, USA.
ArXiv
|October 14, 2024
概括
一种新的深度学习方法可以准确地检测和量化肝硬化和卵巢癌患者的 (腹部液体),并与专家评估有强烈一致.
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:
- 是肝硬化和卵巢癌患者的常见并发症.
- 准确量化炎体积对于治疗规划和监测至关重要.
- 目前用于估计炎体积的方法可能是耗时和主观的.
研究的目的:
- 评估自动深度学习 (DL) 方法用于检测和量化瘤体积的性能.
- 用CT扫描评估DL模型在肝硬化和卵巢癌患者中的准确性.
主要方法:
- 一项回顾性研究,利用两个机构 (NIH和UofW) 的腹腔盆腔CT扫描.
- 在癌症基因组图集卵巢癌数据集上训练了一种DL模型,并在内部 (NIH-LC,NIH-OV) 和外部 (UofW-LC) 数据集上进行了测试.
- 使用Dice系数和中位体体积估计误差来测量性能,与专家放射科医生的评估进行比较.
主要成果:
- 该DL模型获得了高的子得分 (NIH-LC的0.855±0.061,NIH-OV的0.826±0.153,UofW-LC的0.830±0.107).
- 中位数的体积估计误差很低 (NIH-LC为19.6%,NIH-OV为5.3%,UofW-LC为9.7%).
- 该模型与专家评估有很强的一致性 (r2值为0.79,0.98和0.97).
结论:
- 自动化的深度学习方法有效地细分和量化.
- 该DL模型显示高准确性和一致性与专家评估.
- 这种自动化方法有潜力改善临床工作流程,以管理炎.
相关概念视频
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