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对乳房动态对比度增强-MRI,包括电子病变移除的背景对细胞增强的计算机评估,包括电子病变移除

Lindsay Douglas¹, Jordan Fuhrman¹, Qiyuan Hu¹

¹University of Chicago, Department of Radiology Committee on Medical Physics, Chicago, Illinois, United States.

Journal of medical imaging (Bellingham, Wash.)

|May 13, 2024

概括

这项研究引入了一种自动化方法,用于量化乳房MRI中的背景对细胞增强 (BPE). 该方法准确地估计了BPE水平,提供了没有瘤影响的客观评估.

科学领域:

医疗成像医学成像
放射学放射学是一门学科.
人工智能的人工智能

背景情况:

在动态对比增强MRI (DCE-MRI) 中对背景对细胞增强 (BPE) 的临床评估是定性.
瘤增强可以膨胀BPE估计,影响诊断的准确性.
需要对BPE进行客观和定量评估.

研究的目的:

在DCE-MRI中开发一种用于对乳房和病变进行细分的自动化方法.
计算用于估计BPE水平的定量得分.
与放射科医生评级相比,评估自动BPE评分的准确性.

主要方法:

一个U-Net模型被训练为乳房细分.
模糊的C-means集群被用于病变细分和去除.
BPE分数是从各种损伤移除后的投影图像中计算出来的.
使用接收器运行特征分析来评估预测值.

主要成果:

在放射科医生BPE评级和计算的BPE分数之间发现了统计学上显著的趋势.
自动化的BPE得分表现明显好于随机猜测.
损伤去除没有显著改变性能,但受影响的乳房的得分显示有所改善.

结论:

自动BPE评分显示了在乳腺DCE-MRI中客观BPE水平分类的潜力.
这种方法可以提供独立于损伤增强的定量BPE值.
开发的技术为BPE评估提供了更精确的方法.

关键词:

这就是U-Net.背景对酶体增强背景对酶体增强乳房核磁共振成像模糊的c-意味着c的意思.机器学习是机器学习.

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Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...