Calibrating Data Mismatches in Deep Learning-Based Quantitative Ultrasound Using Setting Transfer Functions.
Calibrating Data Mismatches in Deep Learning-Based Quantitative Ultrasound Using Setting Transfer Functions.
复制标题
使用设置传输功能，在基于深度学习的定量超声中校准数据不匹配。
DOI：
10.1109/tuffc.2023.3263119
复制
发表时间：
2023-06
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：
当训练和测试数据分布之间存在数据不匹配时，深度学习（DL）可能会失败。由于其依赖于操作员的性质，在超声成像中可能发生由不同的扫描仪设置引起的采集相关数据不匹配。因此，至关重要的是减轻这些失配的影响，以使DL供电的超声成像和组织定征能够在临床上得到更广泛的采用。为了解决这一挑战，我们提出了一种廉价和可推广的方法，涉及收集一个大的训练集在一个单一的设置和一个小的校准集在每个扫描仪设置。然后，校准集将用于通过使用信号和系统的角度来校准数据失配。我们测试了所提出的解决方案，使用连接到SonixOne扫描仪的L9-4阵列对两个幻影进行分类。为了研究所提出的解决方案的普遍性，我们校准了三种类型的数据失配：脉冲频率失配，焦点失配和输出功率失配。两个众所周知的卷积神经网络（CNN），即，ResNet-50和DenseNet-201使用超声射频（RF）数据进行训练。为了校准设置不匹配，我们计算了设置传递函数。未经校准训练的CNN在脉冲频率、焦点和输出功率失配方面的平均分类准确率分别约为52%、84%和85%。通过使用设置的传递函数，它允许匹配的训练和测试域，我们分别获得了96%，96%和98%的平均准确率。因此，在扫描器设置之间合并设置传递函数可以提供用于特定分类任务的DL模型的一般化的经济手段，其中扫描器设置不由操作员固定。
Deep learning (DL) can fail when there are data mismatches between training and testing data distributions. Due to its operator-dependent nature, acquisition-related data mismatches, caused by different scanner settings, can occur in ultrasound imaging. As a result, it is crucial to mitigate the effects of these mismatches to enable wider clinical adoption of DL-powered ultrasound imaging and tissue characterization. To address this challenge, we propose an inexpensive and generalizable method that involves collecting a large training set at a single setting and a small calibration set at each scanner setting. Then, the calibration set will be used to calibrate data mismatches by using a signals and systems perspective. We tested the proposed solution to classify two phantoms using an L9–4 array connected to a SonixOne scanner. To investigate generalizability of the proposed solution, we calibrated three types of data mismatches: pulse frequency mismatch, focus mismatch, and output power mismatch. Two well-known convolutional neural networks (CNNs), i.e., ResNet-50 and DenseNet-201, were trained using the ultrasound radio frequency (RF) data. To calibrate the setting mismatches, we calculated the setting transfer functions. The CNNs trained without calibration resulted in mean classification accuracies of around 52%, 84%, and 85% for pulse frequency, focus, and output power mismatches, respectively. By using the setting transfer functions, which allowed a matching of the training and testing domains, we obtained the mean accuracies of 96%, 96%, and 98%, respectively. Therefore, the incorporation of the setting transfer functions between scanner settings can provide an economical means of generalizing a DL model for specific classification tasks where scanner settings are not fixed by the operator.