PDF(Pubmed)
Abstract：
Improvement of image quality in radiology, including the maxillofacial region, is important for diagnosis by enhancing the visual perception of the original image. One of the most used modification methods is sharpening, in which simultaneously with the improvement, due to edge enhancement, several artifacts appear. These might lead to misdiagnosis and, as a consequence, to improper treatment. The purpose of this study was to prove the feasibility and effectiveness of automatic sharpening detection based on neural networks.
The in-house created dataset contained 4290 X-ray slices from different datasets of cone beam computed tomography images were taken on 2 different devices: Ortophos 3D SL (Sirona Dental Systems GmbH, Bensheim, Germany) and Planmeca ProMax 3D (Planmeca, Helsinki, Finland). The selected slices were modified using the sharpening filter available in the software RadiAnt Dicom Viewer software (Medixant, Poland), version 5.5. The neural network known as \"ResNet-50\" was used, which has been previously trained on the ImageNet dataset. The input images and their corresponding sharpening maps were used to train the network. For the implementation, Keras with Tensorflow backend was used. The model was trained using NVIDIA GeForce GTX 1080 Ti GPU. Receiver Operating Characteristic (ROC) analysis was performed to calculate the detection accuracy using MedCalc Statistical Software version 14.8.1 (MedCalc Software Ltd, Ostend, Belgium). The study was approved by the Ethical Committee.
For the test, 1200 different images with the filter and without modification were used. An analysis of the detection of three different levels of sharpening (1, 2, 3) showed sensitivity of 53%, 93.33%, 93% and specificity of 72.33%, 84%, 85.33%, respectively with an accuracy of 62.17%, 88.67% and 89% (p < 0.0001). The ROC analysis in all tests showed an Area Under Curve (AUC) different from 0.5 (null hypothesis).
This study showed a high performance in automatic sharpening detection of radiological images based on neural network technology. Further investigation of these capabilities, including their application to different types of radiological images, will significantly improve the level of diagnosis and appropriate treatment.
The in-house created dataset contained 4290 X-ray slices from different datasets of cone beam computed tomography images were taken on 2 different devices: Ortophos 3D SL (Sirona Dental Systems GmbH, Bensheim, Germany) and Planmeca ProMax 3D (Planmeca, Helsinki, Finland). The selected slices were modified using the sharpening filter available in the software RadiAnt Dicom Viewer software (Medixant, Poland), version 5.5. The neural network known as \"ResNet-50\" was used, which has been previously trained on the ImageNet dataset. The input images and their corresponding sharpening maps were used to train the network. For the implementation, Keras with Tensorflow backend was used. The model was trained using NVIDIA GeForce GTX 1080 Ti GPU. Receiver Operating Characteristic (ROC) analysis was performed to calculate the detection accuracy using MedCalc Statistical Software version 14.8.1 (MedCalc Software Ltd, Ostend, Belgium). The study was approved by the Ethical Committee.
For the test, 1200 different images with the filter and without modification were used. An analysis of the detection of three different levels of sharpening (1, 2, 3) showed sensitivity of 53%, 93.33%, 93% and specificity of 72.33%, 84%, 85.33%, respectively with an accuracy of 62.17%, 88.67% and 89% (p < 0.0001). The ROC analysis in all tests showed an Area Under Curve (AUC) different from 0.5 (null hypothesis).
This study showed a high performance in automatic sharpening detection of radiological images based on neural network technology. Further investigation of these capabilities, including their application to different types of radiological images, will significantly improve the level of diagnosis and appropriate treatment.
摘要：
提高放射学中的图像质量，包括颌面部区域，通过增强原始图像的视觉感知对诊断很重要。最常用的修改方法之一是锐化，在改进的同时，由于边缘增强，几件文物出现。这些可能会导致误诊，因此，不适当的治疗。本研究的目的是证明基于神经网络的自动锐化检测的可行性和有效性。
内部创建的数据集包含来自锥束计算机断层扫描图像不同数据集的4290个X射线切片，这些X射线切片是在2种不同的设备上拍摄的：Ortophos3DSL（SironaDentalSystemsGmbH，Bensheim,德国）和PlanmecaProMax3D（Planmeca，赫尔辛基,芬兰)。使用RadiAntDicomViewer软件（Medixant，波兰),版本5.5。使用了被称为“ResNet-50”的神经网络，之前在ImageNet数据集上进行过训练。输入图像及其相应的锐化图用于训练网络。对于实现，使用带有Tensorflow后端的Keras。该模型使用NVIDIAGeForceGTX1080TiGPU进行训练。使用MedCalc统计软件14.8.1版(MedCalcSoftwareLtd,奥斯坦德,比利时)。这项研究得到了伦理委员会的批准。
对于测试，使用了1200个不同的带有滤波器且没有修改的图像。对三种不同锐化水平（1，2，3）的检测进行的分析显示灵敏度为53％，93.33%,93%，特异性72.33%，84%,85.33%,精度分别为62.17%，88.67%和89%(p<0.0001)。所有测试中的ROC分析显示曲线下面积(AUC)不同于0.5(零假设)。
这项研究显示了基于神经网络技术的放射图像自动锐化检测的高性能。进一步调查这些能力，包括它们在不同类型的放射图像中的应用，将显著提高诊断水平和适当治疗。
内部创建的数据集包含来自锥束计算机断层扫描图像不同数据集的4290个X射线切片，这些X射线切片是在2种不同的设备上拍摄的：Ortophos3DSL（SironaDentalSystemsGmbH，Bensheim,德国）和PlanmecaProMax3D（Planmeca，赫尔辛基,芬兰)。使用RadiAntDicomViewer软件（Medixant，波兰),版本5.5。使用了被称为“ResNet-50”的神经网络，之前在ImageNet数据集上进行过训练。输入图像及其相应的锐化图用于训练网络。对于实现，使用带有Tensorflow后端的Keras。该模型使用NVIDIAGeForceGTX1080TiGPU进行训练。使用MedCalc统计软件14.8.1版(MedCalcSoftwareLtd,奥斯坦德,比利时)。这项研究得到了伦理委员会的批准。
对于测试，使用了1200个不同的带有滤波器且没有修改的图像。对三种不同锐化水平（1，2，3）的检测进行的分析显示灵敏度为53％，93.33%,93%，特异性72.33%，84%,85.33%,精度分别为62.17%，88.67%和89%(p<0.0001)。所有测试中的ROC分析显示曲线下面积(AUC)不同于0.5(零假设)。
这项研究显示了基于神经网络技术的放射图像自动锐化检测的高性能。进一步调查这些能力，包括它们在不同类型的放射图像中的应用，将显著提高诊断水平和适当治疗。
