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深度学习模型用于评估尿膀超声图像的质量,使用多级和高阶处理

Deepak Raina, S H Chandrashekhara, Richard Voyles

IEEE transactions on ultrasonics, ferroelectrics, and frequency control

|April 10, 2024

概括

我们开发了USQNet,这是一种用于自主超声波图像质量评估的深度学习模型. USQNet准确评估图像质量,优于现有方法,并帮助超声波仪解读超声波图像.

科学领域:

医疗成像医学成像
人工智能的人工智能
计算机视觉计算机视觉

背景情况:

自主超声波图像质量评估 (US-IQA) 对于临床解释和机器人程序至关重要.
美国IQA的挑战包括图像工件 (噪音,探头定位错误) 和患者特定的解剖变异.

研究的目的:

开发一个深层卷积神经网络 (CNN),USQNet,用于类似于超声波的自主超声波图像质量评估.
通过使用多尺度和局部到全球二级聚合 (MS-L2GSoP) 分类器来解决US-IQA中的挑战.

主要方法:

USQNet采用了CNN架构,并使用了新的MS-L2GSoP分类器.
MS-L2GSoP分类器提取了解剖变异的多尺度特征,并使用二级聚合 (SoP) 来捕获统计依赖.
验证是在人类泌尿膀超声图像的新数据集上进行的,与放射科医生评估和最先进的CNN进行比较.

主要成果:

在自主超声波图像质量评估中,USQNet实现了92.4%的高精度.
该模型的性能比现有的最先进的美国-IQA CNNs 优于3% -14%.
USQNet显示了与其他模型相比的可比计算时间.

结论:

USQNet为自主超声波图像质量评估提供了强大而准确的解决方案.
开发的模型可以帮助超声波仪,并推进超声波程序的机器人化.
该MS-L2GSoP分类器有效地捕获图像特征可靠的US-IQA.

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