Magnetic resonance image segmentation of articular synovium based on improved U-Net

doi:10.11772/j.issn.1001-9081.2020030390

Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (11): 3340-3345.DOI: 10.11772/j.issn.1001-9081.2020030390

• Virtual reality and multimedia computing • Previous Articles Next Articles

Magnetic resonance image segmentation of articular synovium based on improved U-Net

WEI Xiaona¹, XING Jiaqi², WANG Zhenyu¹, WANG Yingshan¹, SHI Jie³, ZHAO Di⁴, WANG Hongzhi¹

1. Shanghai Key Laboratory of Magnetic Resonance(East China Normal University), Shanghai 200062, China;
2. School of Acupuncture-Moxibustion and Tuina, Shanghai University of Traditional Chinese Medicine, Shanghai 200032, China;
3. Shanghai GuangHua Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai 200052, China;
4. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China

Received:2020-03-31 Revised:2020-05-26 Online:2020-11-10 Published:2020-06-03
Supported by:
This work is partially supported by the Enterprise Horizontal Project of Shanghai Niumag Electronic Technology Company Limited (2017KFR0107).

基于改进U-Net的关节滑膜磁共振图像的分割

魏小娜¹, 邢嘉祺², 王振宇¹, 王颖珊¹, 石洁³, 赵地⁴, 汪红志¹

1. 上海市磁共振重点实验室(华东师范大学), 上海 200062;
2. 上海市中医药大学针灸推拿学院, 上海 200032;
3. 上海市光华中西医结合医院, 上海 200052;
4. 中国科学院计算技术研究所, 北京 100190

通讯作者: 汪红志(1975-),男,湖北黄冈人,高级工程师,博士,主要研究方向:磁共振(成像)技术、医学影像分析;hzwang@phy.ecnu.edu.cn
作者简介:魏小娜(1990-),女,安徽宿州人,硕士研究生,主要研究方向:深度学习、医学图像处理;邢嘉祺(1996-),男,上海人,硕士研究生,主要研究方向:生物医学信号处理;王振宇(1996-),男,江西抚州人,硕士研究生,主要研究方向:深度学习、医学图像处理;王颖珊(1997-),女,浙江绍兴人,硕士研究生,主要研究方向:深度学习、医学图像处理;石洁(1976-),女,河北邯郸人,主任医师,硕士,主要研究方向:强脊类风湿关节炎的影像诊断;赵地(1978-),男,湖南长沙人,副教授,博士,主要研究方向:类脑计算
基金资助:
上海纽迈电子科技有限公司企业横向项目（2017KFR0107）。

Abstract

Abstract: In order to accurately diagnose the synovitis patient's condition, doctors mainly rely on manual labeling and outlining method to extract synovial hyperplasia areas in the Magnetic Resonance Image (MRI). This method is time-consuming and inefficient, has certain subjectivity and is of low utilization rate of image information. To solve this problem, a new articular synovium segmentation algorithm, named 2D ResU-net segmentation algorithm was proposed. Firstly, the two-layer residual block in the Residual Network (ResNet) was integrated into the U-Net to construct the 2D ResU-net. Secondly, the sample dataset was divided into training set and testing set, and data augmentation was performed to the training set. Finally, all the training samples after augmentation were applied to the training of the network model. In order to test the segmentation effect of the model, the tomographic images containing synovitis in the testing set were selected for segmentation test. The final average segmentation accuracy indexes are as follow:Dice Similarity Coefficient (DSC) of 69.98%, IOU (Intersection over Union) index of 79.90% and Volumetric Overlap Error (VOE)of 12.11%. Compared with U-Net algorithm, 2D ResU-net algorithm has the DSC increased by 10.72%, IOU index increased by 4.24% and VOE decreased by 11.57%. Experimental results show that this algorithm can achieve better segmentation effect of synovial hyperplasia areas in MRI images, and can assist doctors to make diagnosis of the disease condition in time.

Key words: synovitis, magnetic resonance image, medical image segmentation, data augmentation, U-Net

摘要： 为了准确诊断滑膜炎患者病情，医生主要依靠手工标注和勾画的方法来提取磁共振图像（MRI）中的滑膜增生区域，该方法耗时长、效率低，具有一定的主观性且图像信息利用率低。针对这一问题，提出了一种新的关节滑膜分割算法，即2D ResU-net分割算法。首先，将残差网络（ResNet）中的两层结构的残差块融入到U-Net中，构建2D ResU-net；然后，将样本数据集分为训练集和测试集，而后对训练集进行数据增广；最后，将增广后的所有训练样本用于网络模型的训练。为了检测模型的分割效果，选取测试集中含滑膜炎的断层图像进行分割测试，最终平均分割精度指标可达到：Dice相似系数（DSC）69.98%，交并比（IOU）指标79.90%，体积重叠误差（VOE）系数12.11%。与U-Net算法相比，2D ResU-net算法的DSC系数提升了10.72%，IOU指标升高了4.24%，VOE系数降低了11.57%。实验结果表明，该算法对于MRI图像中的滑膜增生区域可以实现较好的分割效果，能够辅助医生对病情做出及时诊断。

关键词: 滑膜炎, 磁共振图像, 医学图像分割, 数据增广, U-Net

CLC Number:

TP391.41

WEI Xiaona, XING Jiaqi, WANG Zhenyu, WANG Yingshan, SHI Jie, ZHAO Di, WANG Hongzhi. Magnetic resonance image segmentation of articular synovium based on improved U-Net[J]. Journal of Computer Applications, 2020, 40(11): 3340-3345.

魏小娜, 邢嘉祺, 王振宇, 王颖珊, 石洁, 赵地, 汪红志. 基于改进U-Net的关节滑膜磁共振图像的分割[J]. 计算机应用, 2020, 40(11): 3340-3345.

References

[1] 周海燕, 刘鹏程. MR在手腕部小关节病变的临床价值及研究进展[J]. 中国医学工程,2010,18(3):172-175.(ZHOU H Y,LIU P C. Clinical value and research progress of MR in wrist facet joint lesions[J]. China Medical Engineering,2010,18(3):172-175.)
[2] 马强. 类风湿关节炎的腕关节MRI及临床应用研究[D]. 太原:山西医科大学,2003:21-23.(MA Q. Clinical study on MRI of the wrist in rheumatoid arthritis[D]. Taiyuan:Shanxi Medical University,2003:21-23.)
[3] SCUTELLARI P N, ORZINCOLO C. Rheumatoid arthritis:sequences[J]. European Journal of Radiology,1998,27(S1):S31-S38.
[4] ALAMANOS Y,VOULGARI P V,DROSOS A A. Incidence and prevalence of rheumatoid arthritis,based on the 1987 American College of Rheumatology criteria:a systematic review[J]. Seminars in Arthritis and Rheumatism,2006,36(3):182-188.
[5] SUMA A B,SNEKHALATHA U,RAJALAKSHMI T. Automated thermal image segmentation of knee rheumatoid arthritis[C]//Proceedings of the 2016 International Conference on Communication and Signal Processing. Piscataway:IEEE,2016:535-539.
[6] 马进, 陈岷, 李获, 等. 中药联合抗风湿药治疗类风湿性关节炎活动期的临床观察[J]. 中国实验方剂学杂志,2014,20(5):192-196.(MA J,CHEN M,LI H,et al. Clinical observation of traditional Chinese medicinecombined with antirheumatic drug treatment of rheumatoid arthritis in active stage[J]. Chinese Journal of Experimental Traditional Medical Formulae,2014,20(5):192-196.)
[7] 周海燕. 类风湿性关节炎手腕部骨侵蚀MRI定量分析的临床价值[D]. 汕头:汕头大学,2011:2-3.(ZHOU H Y. Clinic values of MRI quantitative analyze in bone erosions in rheumatoid arthritis patients[D]. Shantou:Shantou University,2011:2-3.)
[8] AJEGANOVA S,HUIZINGA T. Sustained remission in rheumatoid arthritis:latest evidence and clinical considerations[J]. Therapeutic Advances in Musculoskeletal Disease,2017,9(10):249-262.
[9] 黄远彬, 吴小玲, 雷鸣, 等. Sharp-van der Heijde评分对类风湿关节炎的临床应用价值[J]. 临床医药实践,2017, 26(7):524-526. (HUANG Y B,WU X L,LEI M,et al. The clinical application value of sharp-van der Heijde score for rheumatoid arthritis[J]. Proceeding of Clinical Medicine,2017,26(7):524-526.)
[10] MCQUEEN F,LASSERE M,EDMONDS J,et al. OMERACT rheumatoid arthritis magnetic resonance imaging studies. Summary of OMERACT 6 MR imaging module[J]. The Journal of Rheumatology,2003,30(6):1387-1392.
[11] VAN STEENBERGEN H W,MANGNUS L,REIJNIERSE M,et al. Clinical factors,anticitrullinated peptide antibodies and MRI detected subclinical inflammation in relation to progression from clinically suspect arthralgia to arthritis[J]. Annals of the Rheumatic Diseases,2015,75(10):1824-1830.
[12] KLEYER A,KRIETER M,OLIVEIRA I,et al. High prevalence of tenosynovial inflammation before onset of rheumatoid arthritis and its link to progression to RA-acombined MRI/CT study[J]. Seminars in Arthritis and Rheumatism,2016,46(2):143-150.
[13] NIEUWENHUIS W P,VAN STEENBERGEN H W,MANGNUS L,et al. Evaluation of the diagnostic accuracy of hand and foot MRI for early rheumatoid arthritis[J]. Rheumatology,2017,56(8):1367-1377.
[14] AIZENBERG E, SHAMONIN D P, REIJNIERSE M, et al. Automatic quantification of tenosynovitis on MRI of the wrist in patients with early arthritis:a feasibility study[J]. European Radiology,2019,29(8):4477-4484.
[15] 谭利华. 类风湿关节炎:诊断标准与MRI的临床意义[C]//中华医学会放射学分会第13届全国磁共振学术大会暨海外华人磁共振2013年会/国际医学磁共振学会论坛. 杭州:出版者不详,2013:380-381.(TAN L H. Rheumatoid arthritis:diagnostic criteria and clinical significance of MRI[C]//Proceedings of the 13th National Magnetic Resonance Conference of Radiology Society of Chinese Medical Association and Overseas Chinese Annual Meeting of Magnetic Resonance 2013 and International Medical Magnetic Resonance Association Forum. Hangzhou:[s. n.],2013:380-381.)
[16] RONNEBERGER O, FISCHER P, BROX T. U-Net:convolutional networks for biomedical image segmentation[C]//Proceedings of the 2015 International Conference on Medical Image Computing and Computer-Assisted Intervention, LNCS 9351. Cham:Springer,2015:234-241.
[17] HE K,ZHANG X,REN S,et al. Deep residual learning for image recognition[C]//Proceeding of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2016:770-778.
[18] IOFFE S,SZEGEDY C. Batch normalization:accelerating deep network training by reducing internal covariate shift[C]//Proceeding of the 32nd International Conference on Machine Learning. New York:JMLR.org,2015:448-456.
[19] MILLETARI F, NAVAB N, AHMADI S A. V-Net:fully convolutional neural networks for volumetric medical image segmentation[C]//Proceeding of the 4th International Conference on 3D Vision. Piscataway:IEEE,2016:565-571.
[20] RAHMAN M A,WANG Y. Optimizing intersection-over-union in deep neural networks for image segmentation[C]//Proceedings of the 2016 International Symposium on Visual Computing,LNCS 10072. Cham:Springer,2016:234-244.

Magnetic resonance image segmentation of articular synovium based on improved U-Net

基于改进U-Net的关节滑膜磁共振图像的分割

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	CAO Yuhong, XU Hai, LIU Sun'ao, WANG Zixiao, LI Hongliang. Review of deep learning-based medical image segmentation [J]. Journal of Computer Applications, 2021, 41(8): 2273-2287.
[2]	JIA Chengxun, LAI Hua, YU Zhengtao, WEN Yonghua, YU Zhiqiang. Chinese-Vietnamese pseudo-parallel corpus generation based on monolingual language model [J]. Journal of Computer Applications, 2021, 41(6): 1652-1658.
[3]	HUANG Li, LU Long. Segmentation of ischemic stroke lesion based on long-distance dependency encoding and deep residual U-Net [J]. Journal of Computer Applications, 2021, 41(6): 1820-1827.
[4]	LU Xinwei, YU Pengfei, LI Haiyan, LI Hongsong, DING Wenqian. Weakly supervised fine-grained image classification algorithm based on attention-attention bilinear pooling [J]. Journal of Computer Applications, 2021, 41(5): 1319-1325.
[5]	GAN Lan, SHEN Hongfei, WANG Yao, ZHANG Yuejin. Data augmentation method based on improved deep convolutional generative adversarial networks [J]. Journal of Computer Applications, 2021, 41(5): 1305-1313.
[6]	HUO Shoujun, HAO Yan, SHI Huiyu, DONG Yanqing, CAO Rui. Pattern recognition of motor imagery EEG based on deep convolutional network [J]. Journal of Computer Applications, 2021, 41(4): 1042-1048.
[7]	DONG Yang, PAN Haiwei, CUI Qianna, BIAN Xiaofei, TENG Teng, WANG Bangju. Few-shot segmentation method for multi-modal magnetic resonance images of brain tumor [J]. Journal of Computer Applications, 2021, 41(4): 1049-1054.
[8]	CHEN Li, WANG Hongyuan, ZHANG Yunpeng, CAO Liang, YIN Yuchang. Video-based person re-identification method by jointing evenly sampling-random erasing and global temporal feature pooling [J]. Journal of Computer Applications, 2021, 41(1): 164-169.
[9]	GAO Haijun, ZENG Xiangyin, PAN Dazhi, ZHENG Bochuan. Rectal tumor segmentation method based on improved U-Net model [J]. Journal of Computer Applications, 2020, 40(8): 2392-2397.
[10]	SHI Lukui, MA Hongqi, ZHANG Chaozong, FAN Shiyan. Detection method of pulmonary nodules based on improved residual structure [J]. Journal of Computer Applications, 2020, 40(7): 2110-2116.
[11]	MA Jinlin, WEI Meng, MA Ziping. Pulmonary nodule segmentation method based on deep transfer learning [J]. Journal of Computer Applications, 2020, 40(7): 2117-2125.
[12]	CHEN Foji, ZHU Feng, WU Qingxiao, HAO Yingming, WANG Ende. Infrared image data augmentation based on generative adversarial network [J]. Journal of Computer Applications, 2020, 40(7): 2084-2088.
[13]	CHENG Guangtao, GONG Jiachang, LI Jian. Smoke recognition method based on dense convolutional neural network [J]. Journal of Computer Applications, 2020, 40(5): 1465-1469.
[14]	WANG Yan, HE Hongke. Improved fuzzy c-means MRI segmentation based on neighborhood information [J]. Journal of Computer Applications, 2020, 40(4): 1196-1201.
[15]	FAN Wei, DUAN Bokun, HUANG Rui, LIU Ting, ZHANG Ning. Interactive augmentation method for aircraft engine borescope inspection images based on style transfer [J]. Journal of Computer Applications, 2020, 40(12): 3631-3636.