基于深度神经网络的椎体坡度自动检测的计算机辅助Cobb测量。

IF 3.3 Q2 ENGINEERING, BIOMEDICAL
International Journal of Biomedical Imaging Pub Date : 2017-01-01 Epub Date: 2017-10-03 DOI:10.1155/2017/9083916
Junhua Zhang, Hongjian Li, Liang Lv, Yufeng Zhang
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引用次数: 52

摘要

目的:开发一种计算机辅助方法,减少脊柱侧凸评估中Cobb角测量的可变性。方法:利用从脊柱模型x线片中提取的椎体斑块进行深度神经网络训练。根据DNN预测的椎体斜率自动计算脊柱曲线的Cobb角。分析65张活体x线片和40张模型x线片。一位经验丰富的外科医生对上述x光片进行了手动测量。两名审查员使用了建议的和人工测量方法来分析上述x光片。结果:模型x线片类内相关系数均大于0.98,平均绝对差值小于3°。这表明所提出的系统对模型射线照相机的测量具有很高的重复性。对于活体x线片,可靠性低于模型x线片,计算机辅助测量与外科医生人工测量的差异大于5°。结论:采用足够的椎体补片训练DNN系统,可降低Cobb角测量的变异性。为了提高DNN的性能,必须包括活体x线片的训练数据。意义:DNN可以预测椎体斜率。计算机辅助系统可用于自动测量Cobb角,用于对脊柱侧凸进行可靠和客观的评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Computer-Aided Cobb Measurement Based on Automatic Detection of Vertebral Slopes Using Deep Neural Network.

Computer-Aided Cobb Measurement Based on Automatic Detection of Vertebral Slopes Using Deep Neural Network.

Computer-Aided Cobb Measurement Based on Automatic Detection of Vertebral Slopes Using Deep Neural Network.

Computer-Aided Cobb Measurement Based on Automatic Detection of Vertebral Slopes Using Deep Neural Network.

Objective: To develop a computer-aided method that reduces the variability of Cobb angle measurement for scoliosis assessment.

Methods: A deep neural network (DNN) was trained with vertebral patches extracted from spinal model radiographs. The Cobb angle of the spinal curve was calculated automatically from the vertebral slopes predicted by the DNN. Sixty-five in vivo radiographs and 40 model radiographs were analyzed. An experienced surgeon performed manual measurements on the aforementioned radiographs. Two examiners used both the proposed and the manual measurement methods to analyze the aforementioned radiographs.

Results: For model radiographs, the intraclass correlation coefficients were greater than 0.98, and the mean absolute differences were less than 3°. This indicates that the proposed system showed high repeatability for measurements of model radiographs. For the in vivo radiographs, the reliabilities were lower than those from the model radiographs, and the differences between the computer-aided measurement and the manual measurement by the surgeon were higher than 5°.

Conclusion: The variability of Cobb angle measurements can be reduced if the DNN system is trained with enough vertebral patches. Training data of in vivo radiographs must be included to improve the performance of DNN.

Significance: Vertebral slopes can be predicted by DNN. The computer-aided system can be used to perform automatic measurements of Cobb angle, which is used to make reliable and objective assessments of scoliosis.

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来源期刊
CiteScore
12.00
自引率
0.00%
发文量
11
审稿时长
20 weeks
期刊介绍: The International Journal of Biomedical Imaging is managed by a board of editors comprising internationally renowned active researchers. The journal is freely accessible online and also offered for purchase in print format. It employs a web-based review system to ensure swift turnaround times while maintaining high standards. In addition to regular issues, special issues are organized by guest editors. The subject areas covered include (but are not limited to): Digital radiography and tomosynthesis X-ray computed tomography (CT) Magnetic resonance imaging (MRI) Single photon emission computed tomography (SPECT) Positron emission tomography (PET) Ultrasound imaging Diffuse optical tomography, coherence, fluorescence, bioluminescence tomography, impedance tomography Neutron imaging for biomedical applications Magnetic and optical spectroscopy, and optical biopsy Optical, electron, scanning tunneling/atomic force microscopy Small animal imaging Functional, cellular, and molecular imaging Imaging assays for screening and molecular analysis Microarray image analysis and bioinformatics Emerging biomedical imaging techniques Imaging modality fusion Biomedical imaging instrumentation Biomedical image processing, pattern recognition, and analysis Biomedical image visualization, compression, transmission, and storage Imaging and modeling related to systems biology and systems biomedicine Applied mathematics, applied physics, and chemistry related to biomedical imaging Grid-enabling technology for biomedical imaging and informatics
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