基于深度学习的超声成像足底软组织刚度自动估计

Yori Pusparani, B. Liau, Yih-Kuen Jan, Hsu-Tang Cheng, Peter Ardhianto, Fityanul Akhyar, Chi-Wen Lung, Chih-Yang Lin
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引用次数: 0

摘要

预防糖尿病足溃疡(DFU)对糖尿病(DM)患者至关重要。足底足僵硬度的增加可能会导致更高的足底压力,从而增加患DFU的风险。软组织刚度可以通过测量软组织厚度、压痕深度和应力来确定。因此,我们假设深度学习模型可以检测软组织压缩下超声图像像素的变化。本研究旨在应用深度学习模型分析足底足超声图像像素厚度,进而预测软组织压痕深度和加载力,用于估计足底足的刚度。本研究开发了一种电机驱动的超声压痕系统,用于应用可编程压缩,同时评估软组织的力学特性和压痕深度和加载力的响应。此外,计算了有效杨氏模量来表征第一跖骨头软组织的力学特性。深度学习方法采用YOLOv5x模型对缩进深度的小物体进行训练和检测,如超声图像像素的变化。最后,从软组织压痕深度和加载力两方面对数据集图像进行标注标注。深度学习的平均精密度(mAP)为0.995,精密度为0.999,召回率为1.000,损失率为0.013。超声图像像素变化与软组织压痕深度有显著相关性(r = 0.98, p < 0.05)。超声图像像素变化与第一跖骨头载荷力有显著相关性(r = 0.85, p < 0.05)。验证模型和预测模型的有效杨氏模量均低于训练模型。然而,三种模型的初始模量计算结果相似。我们的研究结果表明,在超声图像中应用深度学习可以预测软组织压痕深度和加载力,从而计算足底足的刚度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Plantar Soft Tissue Stiffness Automatic Estimation in Ultrasound Imaging using Deep learning
Preventing diabetic foot ulcers (DFU) is critical for diabetes mellitus (DM) patients. Increased stiffness of plantar foot may cause higher plantar pressure leading to a higher risk of DFU. Soft tissue stiffness can be determined by measuring the soft tissue thickness with indentation depth and stress. Therefore, we hypothesized that the deep learning model could detect the ultrasound image pixel change under soft tissue compression. This study aimed to apply the deep learning model to analyze the ultrasound image pixel thickness of plantar foot, then predict the soft tissue indentation depth and loading force for estimating the stiffness. This study has developed a motor-driven ultrasound indentation system to apply programmable compression and simultaneously assess soft tissue mechanical properties and responses in indentation depth and loading force. In addition, the effective Young's modulus was calculated to characterize mechanical properties of soft tissues in the first metatarsal head. The deep learning method employed the YOLOv5x model to train and detect the small object in the indentation depth, such as ultrasound image pixel changes. Finally, the dataset images were processed with labeling annotation from the soft tissue indentation depth and loading force. The deep learning results showed 0.995 in mean Average Precision (mAP), 0.999 in precision, 1.000 in Recall, and 0.013 in Loss. A significant correlation was found between the ultrasound image pixel changes and soft tissue indentation depth (r = 0.98, p < 0.05). Furthermore, a significant correlation was observed between the ultrasound image pixel changes and the loading force in the first metatarsal head (r = 0.85, p < 0.05). The validation and prediction models were lower than the training models in the effective Young's modulus results. However, the results of the initial modulus were similar between the three models. Our findings recommend that applying deep learning in the ultrasound image can predict soft tissue indentation depth and loading force to calculate the stiffness of the plantar foot.
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