基于深度学习的椎间孔狭窄自动多分级研究
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摘要
椎间孔狭窄症的术前定性分级诊断对临床医生治疗策略的制定和患者健康恢复至关重要,但目前该方面临床上仍然存在很多问题,并且缺乏相关的研究和行之有效的方法用于辅助临床医生诊断。因此,为提高计算机辅助椎间孔狭窄症诊断准确率以及医生工作效率,本文提出一种基于深度学习的椎间孔狭窄图像自动分级算法。从人体矢状切脊柱核磁共振图像中提取脊柱椎间孔图像,并做图像预处理;设计一种监督式深度卷积神经网络模型,用于实现脊柱椎间孔图像数据集的自动多分级;利用迁移学习方法,解决深度学习算法在小样本数据集上的过拟合问题。实验结果表明,本文算法在脊柱椎间孔图像数据集上的分类精确度可达到87.5%以上,且其具有良好的鲁棒性和泛化能力。
Preoperative qualitative diagnosis of intervertebral foraminal stenosis is essential for the formulation of clinician treatment strategies and patients' health recovery. However, there are still many clinical challenges in this aspect and a lack of relevant research and proven methods to assist clinicians in diagnosis. Therefore, a deep learning-based automatic classification algorithm is proposed in this study to improve the diagnosis accuracy and the efficiency. First,we extracted the spinal foramen images from the sagittal spine MRI image, and then these images were preprocessed.Second, a supervised deep convolutional neural network model was designed to achieve automatic multi-classification for the datasets of the intervertebral foraminal stenosis. Finally, we used the transfer learning to optimize the overfitting problem of the deep learning algorithm in the small sample dataset. The experimental results show that the classification accuracy of this algorithm on the dataset of spinal foramen was 87.5%, and it has good robustness and generalization performance.
Preoperative qualitative diagnosis of intervertebral foraminal stenosis is essential for the formulation of clinician treatment strategies and patients' health recovery. However, there are still many clinical challenges in this aspect and a lack of relevant research and proven methods to assist clinicians in diagnosis. Therefore, a deep learning-based automatic classification algorithm is proposed in this study to improve the diagnosis accuracy and the efficiency. First,we extracted the spinal foramen images from the sagittal spine MRI image, and then these images were preprocessed.Second, a supervised deep convolutional neural network model was designed to achieve automatic multi-classification for the datasets of the intervertebral foraminal stenosis. Finally, we used the transfer learning to optimize the overfitting problem of the deep learning algorithm in the small sample dataset. The experimental results show that the classification accuracy of this algorithm on the dataset of spinal foramen was 87.5%, and it has good robustness and generalization performance.
引文
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[8]GHOSHA S,ALOMARI R S,CHAUDHARY V,et al.Automatic lumbar vertebra segmentation from clinical CT for wedge compression fracture diagnosis[C]//Proceedings of the SPIE 7963,Medical Imaging 201 1:Computer-Aided Diagnosis.Lake Buena Vista(Orlando),United States,2011:796303,DOI:10.1117/12.878055.
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[10]KLINDER T,WOLZ R,LORENZ C,et al.Spine segmentation using articulated shape models[C]//Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention.New York,USA,2008:227-234.
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[12]VERBIEST H.Results of surgical treatment of idiopathic developmental stenosis of the lumbar vertebral canal.A review of twenty-seven years'experience[J].The journal of bone and joint surgery,1977,59(2):181-188.
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[14]LITJENS G,KOOI T,BEJNORDI B E,et al.A survey on deep learning in medical image analysis[J].Medical image analysis,2017,42(9):60-88.
[15]WANG Xiaosong,PENG Yifan,LU Le,et al.ChestXRay8:Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases[C]//Proceeding of 201 7IEEE Conference on Computer Vision and Pattern Recognition.Honolulu,USA,2017:3462-3471.
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[17]BENGIO Y,DELALLEAU O.On the expressive power of deep architectures[C]//Proceedings of the 22nd International Conference on Algorithmic Learning Theory.Espoo,Finland,2011:18-36.
[18]PAN S J,YANG Qiang.A survey on transfer learning[J].IEEE Transactions on Knowledge and Data Engineering,2010,22(10):1345-1359.
[19]WILDERMUTH S,ZANETTI M,DUEWELL S,et al.Magnetic resonance imaging and magnetic resonance myelography in the presurgical diagnosis of lumbar foraminal stenosis[J].Spine,2007,32(8):896-903.
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[22]GUO Zhenhua,ZHANG Lei,ZHANG D.A completed modeling of local binary pattern operator for texture classification[J].IEEE transactions on image processing,2010,19(6):1657-1663.
[23]HONG Huichao,ZHENG Lixin,PAN Shuwan.Fast com-putational technique for gray-level co-occurrence matrix based on graphic s process unit in biomedical engineering applications[J].Journal of medic al imaging and health informatics,2018,8(2):309-312.
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[25]SAHAN S,POLAT K,KODAZ H,et al.A new hybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosis[J].Computers in biology and medicine,2007,37(3):415-423.
[26]WONG P K,GAO Xianghui,WONG K I,et al.Online extreme learning machine based modeling and optimization for point-by-point engine calibration[J].Neurocomputing,2018,277:187-197.
[27]CORTES C, VAPNIK V.Support-vector networks[J].Machine learning,1995,20(3):273-297.
[28]BREIMAN L.Random forests[J].Machine Learning,2001,45(1):5-32.