应用神经网络修复颅骨表面缺损

S. Mishinov
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引用次数: 0

摘要

目的:评价基于卷积神经网络的颅骨缺损骨修复数字算法的敏感性、特异性和准确性。材料和方法。对78000个人工颅骨损伤的颅骨模型进行了6000次epoch的神经网络训练。对222例有颅骨缺损的DICOM系列ct患者进行评价。结果。敏感性、特异性和准确性分别为95.3%、85.5%和79.4%。为了找出颅骨重建效果不理想的原因,我们进行了大量的实验,并对三维模型进行了分步排序。颅骨缺损的错误检测多发生在面部骨骼区域。在排除带有工件的系列之后,指标的平均增长为2.6%。结论。该算法对颅骨模型骨缺损的正确判断(特异性)对表面精度影响最大。该算法的最大精度允许在三维建模环境中使用获得的表面而无需额外处理,在计算机断层扫描期间实现了一系列无伪影(83.5%),以及不延伸到颅底的缺陷(79.5%)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of a neural network to restore the lost surface of skull bones
Objective: to evaluate the sensitivity, specificity and accuracy of a digital algorithm based on convo-lutional neural networks to restore of bones of cranium defects. Material and methods. Neural network training was carried out as a result of 6,000 epochs on 78,000 variants of skull models with artificially generated skull injuries. The evaluation was performed on 222 DICOM series of patients computerized tomography with bones of cranium defects. Results. The indicators of sensitivity, specificity and accuracy were 95.3%, 85.5% and 79.4% respectively. A number of experiments were carried out with step-by-step sorting of three-dimensional models in order to find the reasons for the unsatisfactory skull reconstructing results. Incorrect detection of the skull defect most often occurred in the area of the facial skeleton. After excluding the series with artifacts, the average increase in metrics was 2.6%. Conclusion. Correct determination of the bone defect at the scull model (specificity) by the algorithm had the greatest impact on the surface accuracy. The maximum accuracy of the algorithm, which allows using the obtained surfaces without additional processing in a three-dimensional modeling environment, was achieved on series without the presence of artifacts during computed tomography (83.5%), as well as with defects that do not extend to the skull base (79.5%).
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