PDF(Pubmed)
Abstract：
OBJECTIVE: Segmentation of the femur in Dual-Energy X-ray (DXA) images poses challenges due to reduced contrast, noise, bone shape variations, and inconsistent X-ray beam penetration. In this study, we investigate the relationship between noise and certain deep learning (DL) techniques for semantic segmentation of the femur to enhance segmentation and bone mineral density (BMD) accuracy by incorporating noise reduction methods into DL models.
METHODS: Convolutional neural network (CNN)-based models were employed to segment femurs in DXA images and evaluate the effects of noise reduction filters on segmentation accuracy and their effect on BMD calculation. Various noise reduction techniques were integrated into DL-based models to enhance image quality before training. We assessed the performance of the fully convolutional neural network (FCNN) in comparison to noise reduction algorithms and manual segmentation methods.
RESULTS: Our study demonstrated that the FCNN outperformed noise reduction algorithms in enhancing segmentation accuracy and enabling precise calculation of BMD. The FCNN-based segmentation approach achieved a segmentation accuracy of 98.84% and a correlation coefficient of 0.9928 for BMD measurements, indicating its effectiveness in the clinical diagnosis of osteoporosis.
CONCLUSIONS: In conclusion, integrating noise reduction techniques into DL-based models significantly improves femur segmentation accuracy in DXA images. The FCNN model, in particular, shows promising results in enhancing BMD calculation and clinical diagnosis of osteoporosis. These findings highlight the potential of DL techniques in addressing segmentation challenges and improving diagnostic accuracy in medical imaging.
METHODS: Convolutional neural network (CNN)-based models were employed to segment femurs in DXA images and evaluate the effects of noise reduction filters on segmentation accuracy and their effect on BMD calculation. Various noise reduction techniques were integrated into DL-based models to enhance image quality before training. We assessed the performance of the fully convolutional neural network (FCNN) in comparison to noise reduction algorithms and manual segmentation methods.
RESULTS: Our study demonstrated that the FCNN outperformed noise reduction algorithms in enhancing segmentation accuracy and enabling precise calculation of BMD. The FCNN-based segmentation approach achieved a segmentation accuracy of 98.84% and a correlation coefficient of 0.9928 for BMD measurements, indicating its effectiveness in the clinical diagnosis of osteoporosis.
CONCLUSIONS: In conclusion, integrating noise reduction techniques into DL-based models significantly improves femur segmentation accuracy in DXA images. The FCNN model, in particular, shows promising results in enhancing BMD calculation and clinical diagnosis of osteoporosis. These findings highlight the potential of DL techniques in addressing segmentation challenges and improving diagnostic accuracy in medical imaging.
摘要：
目的：由于对比度降低，双能X射线（DXA）图像中股骨的分割提出了挑战，噪音,骨骼形状变化，和不一致的X射线束穿透。在这项研究中,我们研究了噪声与某些用于股骨语义分割的深度学习（DL）技术之间的关系，以通过将降噪方法纳入DL模型来提高分割和骨密度（BMD）准确性。
方法：采用基于卷积神经网络（CNN）的模型对DXA图像中的股骨进行分割，并评估降噪滤波器对分割精度的影响及其对BMD计算的影响。在训练之前，将各种降噪技术集成到基于DL的模型中以增强图像质量。与降噪算法和手动分割方法相比，我们评估了全卷积神经网络（FCNN）的性能。
结果：我们的研究表明，FCNN在提高分割精度和实现BMD精确计算方面优于降噪算法。基于FCNN的分割方法实现了98.84%的分割精度和0.9928的BMD测量相关系数,表明其在骨质疏松症的临床诊断中的有效性。
结论：结论：将降噪技术集成到基于DL的模型中，可以显着提高DXA图像中股骨分割的准确性。FCNN模型，特别是,在增强BMD计算和骨质疏松症的临床诊断方面显示出有希望的结果。这些发现凸显了DL技术在解决分割挑战和提高医学成像诊断准确性方面的潜力。
方法：采用基于卷积神经网络（CNN）的模型对DXA图像中的股骨进行分割，并评估降噪滤波器对分割精度的影响及其对BMD计算的影响。在训练之前，将各种降噪技术集成到基于DL的模型中以增强图像质量。与降噪算法和手动分割方法相比，我们评估了全卷积神经网络（FCNN）的性能。
结果：我们的研究表明，FCNN在提高分割精度和实现BMD精确计算方面优于降噪算法。基于FCNN的分割方法实现了98.84%的分割精度和0.9928的BMD测量相关系数,表明其在骨质疏松症的临床诊断中的有效性。
结论：结论：将降噪技术集成到基于DL的模型中，可以显着提高DXA图像中股骨分割的准确性。FCNN模型，特别是,在增强BMD计算和骨质疏松症的临床诊断方面显示出有希望的结果。这些发现凸显了DL技术在解决分割挑战和提高医学成像诊断准确性方面的潜力。
