使用ProGAN生成的数据集自动测量胸部x射线图像中的心胸比率

IF 12.3 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Worapan Kusakunniran, P. Saiviroonporn, T. Siriapisith, T. Tongdee, Amphai Uraiverotchanakorn, Suphawan Leesakul, Penpitcha Thongnarintr, Apichaya Kuama, Pakorn Yodprom
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引用次数: 0

摘要

目的心脏肥大可以通过胸部x射线图像中的心胸比值(CTR)来确定。它是根据心脏大小和胸部横向尺寸之间的关系来计算的。当比值大于临界阈值时,可确定心脏肥大。本文旨在提出一种计算胸部x射线图像中心脏肥大分类比率的解决方案。设计/方法论/方法所提出的方法首先基于U-Net架构,使用公开可用的数据集,以心脏和肺部掩码为基础,构建肺部和心脏分割模型。然后使用分割的肺和心脏区域的大小来计算该比率。此外,本文采用了GANs的渐进生长(PGAN)来构建新的数据集,该数据集包含三个类别的胸部x射线图像,包括男性正常、女性正常和心脏肥大类别。然后使用该数据集来评估所提出的解决方案。此外,所提出的解决方案还用于评估由PGAN生成的胸部x射线图像的质量。在实验中,将训练的模型应用于在自收集的数据集上分割胸部x射线图中的心脏和肺部区域。将计算出的CTR值与由人类专家手动测量的值进行比较。平均误差为3.08%。然后,在PGAN计算的数据集上,将该模型应用于心肺区域的CTR计算。然后,使用不同截止阈值的各种尝试来确定心脏肥大。在标准截止值为0.50的情况下,该方法的准确率为94.61%,灵敏度为88.31%,特异性为94.20%。独创性/价值所提出的解决方案被证明在分割、CTR计算和心脏肥大分类的未发现数据集上是稳健的,包括PGAN生成的数据集。为了提高灵敏度,可以将截止值调整为低于0.50。例如,在0.45的截止值下可以实现97.04%的灵敏度。但特异性从94.20%下降到79.78%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic measurement of cardiothoracic ratio in chest x-ray images with ProGAN-generated dataset
PurposeThe cardiomegaly can be determined by the cardiothoracic ratio (CTR) which can be measured in a chest x-ray image. It is calculated based on a relationship between a size of heart and a transverse dimension of chest. The cardiomegaly is identified when the ratio is larger than a cut-off threshold. This paper aims to propose a solution to calculate the ratio for classifying the cardiomegaly in chest x-ray images.Design/methodology/approachThe proposed method begins with constructing lung and heart segmentation models based on U-Net architecture using the publicly available datasets with the groundtruth of heart and lung masks. The ratio is then calculated using the sizes of segmented lung and heart areas. In addition, Progressive Growing of GANs (PGAN) is adopted here for constructing the new dataset containing chest x-ray images of three classes including male normal, female normal and cardiomegaly classes. This dataset is then used for evaluating the proposed solution. Also, the proposed solution is used to evaluate the quality of chest x-ray images generated from PGAN.FindingsIn the experiments, the trained models are applied to segment regions of heart and lung in chest x-ray images on the self-collected dataset. The calculated CTR values are compared with the values that are manually measured by human experts. The average error is 3.08%. Then, the models are also applied to segment regions of heart and lung for the CTR calculation, on the dataset computed by PGAN. Then, the cardiomegaly is determined using various attempts of different cut-off threshold values. With the standard cut-off at 0.50, the proposed method achieves 94.61% accuracy, 88.31% sensitivity and 94.20% specificity.Originality/valueThe proposed solution is demonstrated to be robust across unseen datasets for the segmentation, CTR calculation and cardiomegaly classification, including the dataset generated from PGAN. The cut-off value can be adjusted to be lower than 0.50 for increasing the sensitivity. For example, the sensitivity of 97.04% can be achieved at the cut-off of 0.45. However, the specificity is decreased from 94.20% to 79.78%.
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来源期刊
Applied Computing and Informatics
Applied Computing and Informatics Computer Science-Information Systems
CiteScore
12.20
自引率
0.00%
发文量
0
审稿时长
39 weeks
期刊介绍: Applied Computing and Informatics aims to be timely in disseminating leading-edge knowledge to researchers, practitioners and academics whose interest is in the latest developments in applied computing and information systems concepts, strategies, practices, tools and technologies. In particular, the journal encourages research studies that have significant contributions to make to the continuous development and improvement of IT practices in the Kingdom of Saudi Arabia and other countries. By doing so, the journal attempts to bridge the gap between the academic and industrial community, and therefore, welcomes theoretically grounded, methodologically sound research studies that address various IT-related problems and innovations of an applied nature. The journal will serve as a forum for practitioners, researchers, managers and IT policy makers to share their knowledge and experience in the design, development, implementation, management and evaluation of various IT applications. Contributions may deal with, but are not limited to: • Internet and E-Commerce Architecture, Infrastructure, Models, Deployment Strategies and Methodologies. • E-Business and E-Government Adoption. • Mobile Commerce and their Applications. • Applied Telecommunication Networks. • Software Engineering Approaches, Methodologies, Techniques, and Tools. • Applied Data Mining and Warehousing. • Information Strategic Planning and Recourse Management. • Applied Wireless Computing. • Enterprise Resource Planning Systems. • IT Education. • Societal, Cultural, and Ethical Issues of IT. • Policy, Legal and Global Issues of IT. • Enterprise Database Technology.
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