让它不那么复杂:自动编码器去除斑点噪声 - - 应用到乳房和肺部超声波
Duarte Oliveira-Saraiva1,2, João Mendes1,2, João Leote3
1Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisbon, Portugal.
Journal of imaging
|October 27, 2023
概括
一个更简单的深度学习模型有效地从超声波图像中去除斑点噪声 (SN),改善乳腺癌病变的分类,并优于传统的过器. 这种方法在现实世界的临床应用中提高了诊断准确性.
科学领域:
- 医疗成像医学成像
- 医疗保健中的人工智能
- 信号处理 信号处理
背景情况:
- 超声波 (美国) 影像对于诊断COVID-19和乳腺癌等疾病至关重要.
- 美国图像中的斑点噪声 (SN) 降低了图像质量,并降低了病变的可见性.
- 现有的SN删除方法,包括复杂的深度学习模型,往往侧重于模拟噪音,缺乏现实世界的适用性.
研究的目的:
- 开发和评估一种更简单的卷积神经网络自编码器 (CNN-AE),用于从乳房和肺部图像中删除自然存在的SN.
- 为了比较拟议中的CNN-AE与传统的Median和Lee过器的性能.
- 用CNN模型评估SN切除对恶性与良性乳腺病变的分类的影响.
主要方法:
- 使用不到30,000个参数的CNN-AE被训练使用原始的美国图像作为目标和噪音图像 (与四个级别的模拟SN) 作为输入.
- 使用结构相似性指数测量 (SSIM) 和峰值信号噪声比 (PSNR) 对原始图像进行性能评估.
- 一个CNN分类器被训练使用原始,denoised (CNN-AE和过器) 和杂的美国图像来区分乳腺病变.
主要成果:
- 根据SSIM和PSNR的测量,CNN-AE在消除模拟的SN在所有噪声水平上的表现明显优于Median和Lee的过器.
- 虽然美国原始图像获得了最高的整体准确度和病变分类的马修斯相关系数 (MCC),但CNN-AE的图像显示出更高的灵敏度和负的预测值,特别是在更高的噪音水平下.
- 更简单的DL模型显示,与使用原始图像或中位过器无色图像相比,恶性乳腺病变的错误分类较少.
结论:
- 一种不那么复杂,以临床为重点的深度学习方法 (CNN-AE) 有效地消除了现实世界超声图像中的斑点噪声.
- 这种方法提高了乳腺病变分类的诊断性能,特别是提高了灵敏度和负预测值.
- 该研究强调了开发更简单,临床上适用的深度学习模型对于医疗图像降噪的重要性.
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