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塞乌西亚-乳房:计算机辅助诊断与对比增强超声波图像分析乳房病变

Satoshi Kondo¹, Megumi Satoh², Mutsumi Nishida²

¹Muroran Institute of Technology, Hokkaido, Japan. kondo@muroran-it.ac.jp.

BMC medical imaging

|August 29, 2023

概括

这项研究介绍了Ceucia-Breast,这是一种机器学习方法,用于使用对比增强超声波 (CEUS) 来分类乳腺病变. 与传统方法相比,这种方法显著提高了诊断准确度,有助于更好地检测乳腺癌.

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:

对比增强超声波 (CEUS) 对于乳腺损伤诊断越来越重要,其性能优于传统的B模式成像.
虽然计算机辅助诊断 (CAD) 存在于B模式超声波中,但它对CEUS的发展较少.
这凸显了CEUS对乳腺癌的先进诊断工具的需求.

研究的目的:

开发和评估一种半自动机器学习方法,用于使用CEUS对乳腺病变进行分类.
利用CEUS和B模式图像的空间,时间和形态特征来提高诊断准确性.
为弥补CEUS乳腺损伤分析CAD中的差距.

主要方法:

使用机器学习 (线性支向量机器 - SVM) 开发了一种半自动分类方法,Ceucia-Breast.
该方法提取空间,时间 (来自CEUS视频),形态/纹理特征 (来自B模式和CEUS图像).
瘤区域以检查人员的指导来定义,然后进行特征提取和SVM分类,分为良性或恶性类别.

主要成果:

在119名受试者中,Ceucia-Breast方法实现了高性能指标:AUC为0.893,准确率为0.816,精度为0.841,回忆率为0.920.
分类性能明显优于仅使用B模式图像的方法.
选择的特征与乳腺瘤诊断的临床CEUS指南一致,区域提取的操作员依赖性是最小的 (kappa=1.0内操作员,0.798间操作员).

结论:

拟议的Ceucia-Breast方法在CEUS中显示了乳腺病变的优异分类性能,与传统的B模式分析相比.
鉴定的特征具有临床相关性,支持该方法在乳腺癌诊断中的实际应用.
该方法显示出强大的可靠性和一致性,在关键区域提取步骤中对操作员的依赖性最小.

关键词:

乳腺损伤乳腺损伤计算机辅助诊断是一种计算机辅助的诊断.与对比度增强的超声波学.支持矢量机器的支持矢量机器.

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