Automatic recognition of thalamic section in fetal two-dimensional ultrasound images based on convolutional neural network

DING Chun-xia1; QU Ruo-wei2; YIN Ying1

doi:10.12117/jccmi.2020.05.013

Journal of China Clinic Medical Imaging ›› 2020, Vol. 31 ›› Issue (5) : 358-362. DOI: 10.12117/jccmi.2020.05.013

Automatic recognition of thalamic section in fetal two-dimensional ultrasound images based on convolutional neural network

DING Chun-xia1, QU Ruo-wei2, YIN Ying1

Author information +

History +

Abstract

Objective: Automatic recognition and classification on fetal ultrasound image is significant to make doctors’ work efficiency. Method: Being different from traditional automatic classification method whose images should be segmented in detail, feature extracted manually and then classified, we proposed a deep convolutional neural network(CNN) based fetal thalamus plane ultrasound image recognition method. First, the images were pre-processed, such as image enhancement; then, we proposed an improved CNN algorithm. Result: This algorithm avoids the complex pre-processing of two-dimensional ultrasound image, and can input the original two-dimensional ultrasound image directly. It has strong capacity of adaption and generalization. The experimental results show that the recognition accuracy of this method can reach 94.81%. Conclusion: The proposed model provides a new reference for automatic medical image recognition technology.

Key words

Brain / Ultrasonography, prenatal

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

DING Chun-xia1, QU Ruo-wei2, YIN Ying1. Automatic recognition of thalamic section in fetal two-dimensional ultrasound images based on convolutional neural network[J]. Journal of China Clinic Medical Imaging. 2020, 31(5): 358-362 https://doi.org/10.12117/jccmi.2020.05.013

References

[1]黄艳. 超声产前筛查胎儿畸形的探讨分析[J]. 中国优生与遗传杂志，2008，16（6）：101-102.
[2]孙超，何立红，张平锋. 超声诊断鳃裂畸形[J]. 中国医学影像技术，2013，29（7）：1099-1102.
[3]程桂静，张波，杨太珠. 超声诊断胎儿Dandy-Walker畸形及其合并畸形[J]. 中国医学影像技术，2011，27（10）：2091-2094.
[4]欧阳春艳，马小燕，肖珍，等. 产前超声诊断重复肾畸形及误诊原因分析[J]. 中国医学影像技术，2011，27（10）：2102-2104.
[5]郭艳坤，丁春霞，刘凤霞. 超声弹性成像对乳腺病灶诊断效果的Logistic回归模型分析[J]. 山西医药杂志，2013，42（5）：535-536.
[6]丁春霞，刘凤霞，王海平. 全自动乳腺容积成像对乳腺病灶诊断效果的Logistic回归模型分析[J]. 肿瘤学杂志，2014，20（9）：720-723.
[7]Yu Z, Tan EL, Ni D, et al. A Deep Convolutional Neural Network Based Framework for Automatic Fetal Facial Standard Plane Recognition[J]. IEEE Journal of Biomedical and Health Informatics, 2018, 22(3): 874-885.
[8]Ni D, Yang X, Chen X, et al. Standard Plane Localization in Ultrasound by Radial Component Model and Selective Search[J]. Ultrasound Med Biol, 2014, 40(11): 2728-2742.
[9]吕康泰，徐倩君，查文，等. 超声诊断孕期宫腔内局限性隔膜样结构[J]. 中国医学影像技术，2012，28（2）：332-335.
[10]何钊群，赵亚平，张庆，等. 超声检查正常新生儿甲状腺容积[J]. 中国医学影像技术，2010，26（2）：396.
[11]Kermany DS, Goldbaum M, Cai W, et al. Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning[J]. Cell, 2018, 172(5): 1122-1131. e9.
[12]李彦冬，郝宗波，雷航. 卷积神经网络研究综述[J]. 计算机应用，2016，36（9）：2508-2515.
[13]卢宏涛，张秦川. 深度卷积神经网络在计算机视觉中的应用研究综述[J]. 数据采集与处理，2016，31（1）：1-17.
[14]Feng W, Jian C, Liu W, et al. Additive Margin Softmax for Face Verification[J]. IEEE Signal Processing Letters, 2018, 25(7): 926-930.
[15]Hubel DH, Wiesel TN. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex[J]. J Physiol, 1962, 160(1): 106-154.
[16]Szegedy C, Liu W, Jia Y, et al. Going Deeper with Convolutions[C]. International Conference on Computer Vision and Pattern Recogintion(CVPR), Columbus: IEEE, 2014: 1-9.
[17]Lecun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
[18]Krizhevsky A, Sutskever I, Hinton G. ImageNet Classification with Deep Convolutional Neural Networks[J]. Adv Neural Inform Proc Sys, 2012, 25(2): 1097-1105.
[19]Qi X, Gang P. SparseConnect: regularising CNNs on fully connected layers[J]. Electronics Letters, 2017, 53(18): 1246-1248.
[20]Zhang K, Zuo W, Chen Y, et al. Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising[J]. IEEE Transactions on Image Processing, 2016, 26(7): 3142-3155.
[21]Jin KH, Mccann MT, Froustey E, et al. Deep Convolutional Neural Network for Inverse Problems in Imaging[J]. IEEE Transactions on Image Processing, 2017, 26(9): 4509-4522.
[22]Shi L, Liu W, Zhang H, et al. A survey of GPU-based medical image computing techniques[J]. Quantitative Imaging in Medicine and Surgery, 2012, 2(3): 188-206.
[23]Russakovsky O, Deng J, Su H, et al. ImageNet Large Scale Visual Recognition Challenge[J]. Intern J Comput Vision, 2015, 115(3): 211-252.
[24]张红梅，卞正中，郭佑民，等. 感兴趣区域高效提取算法（英文）[J]. 软件学报，2005，16（1）：77-88.
[25]斯白露，高文，卢汉清，等. 基于感兴趣区域的图像检索方法[J]. 高技术通讯，2003，13（5）：13-18.
[26]Hao SB, Li Y, Li YM. An SVM-Based Prediction Method for Solving SAT Problems[J]. ACTA Electronica SINICA, 2019, 28(2): 246-252.
[27]Zhang C, Zhou Y, Guo J, et al. Research on classification method of high-dimensional class-imbalanced datasets based on SVM[J]. International Journal of Machine Learning & Cybernetics, 2019, 10: 1765-1778.