A Data-Efficient Deep Learning Strategy for Tissue Characterization via Quantitative Ultrasound: Zone Training.
A Data-Efficient Deep Learning Strategy for Tissue Characterization via Quantitative Ultrasound: Zone Training.
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DOI：
10.1109/tuffc.2023.3245988
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发表时间：
2023-05
影响因子：
3.6
通讯作者：
Oelze, Michael L.
中科院分区：
文献类型：
--
作者：
Soylu, Ufuk;Oelze, Michael L.
关键词：
深度学习（DL）驱动的生物医学超声成像是一个新兴的研究领域，研究人员将深度学习算法的图像分析能力应用于生物医学超声成像设置。广泛采用深度学习驱动的生物医学超声成像的一个主要障碍是，在临床环境中获取大型和多样化的数据集是昂贵的，这是成功实施深度学习的必要条件。因此，不断需要开发数据高效的深度学习技术，以将深度学习驱动的生物医学超声成像变为现实。在这项工作中，我们开发了一种基于超声后向散射射频数据的数据高效DL训练策略，即定量超声（QUS），我们将其命名为区域训练。在区域训练中，我们建议将超声图像的完整视场划分为与衍射图案的不同区域相关的多个区域，然后为每个区域训练单独的DL网络。区域训练的主要优点是它需要较少的训练数据来达到较高的准确性。在这项工作中，通过深度学习网络对三种不同的组织模拟幻象进行分类。结果表明，与传统的训练策略相比，在低数据状态下，区域训练所需的训练数据可以减少2-3倍，以达到相似的分类精度。
Deep learning (DL) powered biomedical ultrasound imaging is an emerging research field where researchers adapt the image analysis capabilities of DL algorithms to biomedical ultrasound imaging settings. A major roadblock to wider adoption of DL powered biomedical ultrasound imaging is that acquisition of large and diverse datasets is expensive in clinical settings, which is a requirement for successful DL implementation. Hence, there is a constant need for developing data-efficient DL techniques to turn DL powered biomedical ultrasound imaging into reality. In this work, we develop a data-efficient DL training strategy for classifying tissues based on the ultrasonic backscattered RF data, i.e., quantitative ultrasound (QUS), which we named zone training. In zone training, we propose to divide the complete field of view of an ultrasound image into multiple zones associated with different regions of a diffraction pattern and then, train separate DL networks for each zone. The main advantage of zone training is that it requires less training data to achieve high accuracy. In this work, three different tissue-mimicking phantoms were classified by a DL network. The results demonstrated that zone training can require a factor of 2–3 less training data in low data regime to achieve similar classification accuracies compared to a conventional training strategy.