Hernandez-Torres Sofia I, Boice Emily N, Snider Eric J
U.S. Army Institute of Surgical Research, Joint Base San Antonio Fort Sam Houston, San Antonio, TX 78234, USA.
J Imaging. 2022 Oct 2;8(10):270. doi: 10.3390/jimaging8100270.
Tissue phantoms are important for medical research to reduce the use of animal or human tissue when testing or troubleshooting new devices or technology. Development of machine-learning detection tools that rely on large ultrasound imaging data sets can potentially be streamlined with high quality phantoms that closely mimic important features of biological tissue. Here, we demonstrate how an ultrasound-compliant tissue phantom comprised of multiple layers of gelatin to mimic bone, fat, and muscle tissue types can be used for machine-learning training. This tissue phantom has a heterogeneous composition to introduce tissue level complexity and subject variability in the tissue phantom. Various shrapnel types were inserted into the phantom for ultrasound imaging to supplement swine shrapnel image sets captured for applications such as deep learning algorithms. With a previously developed shrapnel detection algorithm, blind swine test image accuracy reached more than 95% accuracy when training was comprised of 75% tissue phantom images, with the rest being swine images. For comparison, a conventional MobileNetv2 deep learning model was trained with the same training image set and achieved over 90% accuracy in swine predictions. Overall, the tissue phantom demonstrated high performance for developing deep learning models for ultrasound image classification.
组织模型对于医学研究非常重要，可在测试新设备或技术或排查故障时减少动物或人体组织的使用。依赖大型超声成像数据集的机器学习检测工具的开发，可能会因高质量的模型而得到简化，这些模型能紧密模拟生物组织的重要特征。在此，我们展示了如何将由多层明胶组成的符合超声要求的组织模型用于模拟骨、脂肪和肌肉组织类型，以进行机器学习训练。这种组织模型具有异质成分，可在组织模型中引入组织层面的复杂性和个体差异。将各种类型的弹片插入模型进行超声成像，以补充为深度学习算法等应用所采集的猪弹片图像集。使用先前开发的弹片检测算法，当训练由75%的组织模型图像组成，其余为猪图像时，盲法猪测试图像准确率超过95%。相比之下，使用相同的训练图像集对传统的MobileNetv2深度学习模型进行训练，在猪的预测中准确率超过90%。总体而言，该组织模型在开发用于超声图像分类的深度学习模型方面表现出高性能。
