在低剂量腹部CT与基于深度学习的细分上进行纵向变量分析
Xin Yu1, Yucheng Tang2, Qi Yang1
1Computer Science, Vanderbilt University, Nashville, TN, USA.
概括
这项研究开发了用于低剂量计算机断层扫描 (CT) 的自动细分,以评估随着时间的推移身体组成的变化. 结果显示了稳定的细分,用于分析代谢健康的纵向变化.
科学领域:
- 医疗成像医学成像
- 放射学 放射学是一门学科.
- 生物医学工程 生物医学工程
背景情况:
- 代谢健康是各种疾病的关键因素,需要准确的身体成分评估.
- 低剂量,单片计算机断层扫描 (CT) 提供了详细的组织分析,但需要强大的方法进行纵向研究.
- 自动细分技术对于从CT扫描中量化身体组成的变化至关重要.
研究的目的:
- 开发和验证使用低剂量,单片CT分析身体组成的纵向变异性的自动化细分方法.
- 评估不同腹部组织的细分的稳定性和可靠性.
- 为了确定影响纵向CT基体质组合分析变异性的因素.
主要方法:
- 利用监督深度学习和无监督聚类来自动细分1816个来自巴尔的摩老化纵向研究 (BLSA) 数据集的腹部CT片.
- 在300名具有两年扫描间隔的受试者中,使用类内相关系数 (ICC) 和变化系数 (CV) 评估了组织/器官大小和强度的纵向变化.
- 计算了Dice的相似系数,以测量13个腹部组织结构的细分精度.
主要成果:
- 在13个腹部组织结构中,实现了稳定的纵向细分性能,Dice得分从0.821到0.962不等.
- 在肌肉,腹壁,脂肪和身体口罩中显示出低可变性 (平均ICC≥0.8),表明可靠性.
- 在大多数器官中观察到高变异性 (ICC <0.5),与2D切片的截面位置有很强的相关性.
结论:
- 开发的自动化细分方法对于低剂量,单片CT数据的纵向分析具有稳定性和可靠性.
- 器官测量的变化受到切片位置的显著影响,这凸显了需要仔细的质量控制的需要.
- 这项工作为定量探索和在纵向人体组成研究中减少不确定性提供了基础.
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