Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (6A): 95-100.doi: 10.11896/jsjkx.200700067

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Mediastinal Lymph Node Segmentation Algorithm Based on Multi-level Features and Global Context

XU Shao-wei1,2, QIN Pin-le1,2, ZENG Jian-chao1,2, ZHAO Zhi-kai3, GAO Yuan1,2, WANG Li-fang1,2   

  1. 1 School of Data Science,North University of China,Taiyuan 030051,China
    2 Shanxi Medical Imaging and Data Analysis Engineering Research Center,North University of China,Taiyuan 030051,China
    3 First Hospital of Shanxi Medical University,Taiyuan 030001,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:XU Shao-wei,born in 1995,M.S.candidate,is a member of China Computer Federation.His main research interests include computer vision,medical image and machine learning.
    ZENG Jian-chao,born in 1963,Ph.D,is a member of China Computer Federation.His main research interests include medical image and maintenance decision of complex system.
  • Supported by:
    Construction Project of Engineering Technology Research Center of Shanxi Province(201805D121008).

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Abstract: Aiming at the problems of large mediastinal lymph node scale difference,unbalanced positive and negative samples,and easy to confuse soft tissue and lung tumors,a novel multi-level feature and global context segmentation network for mediastinal lymph node segmentation is proposed.In order to solve the problem that the positive and negative samples of the mediastinal lymph nodes are not balanced and are similar to the mediastinal organs and soft tissues,the mediastinal space is extracted through medical a priori to artificially enhance the attention to the location of the mediastinal lymph nodes.In order to solve the problem that the enlarged mediastinal lymph nodes are similar to lung tumors and the lymph nodes appear regionally dispersed,a global context module is designed.By calculating the global context dependence,the network's ability to classify lymph nodes and background is greatly enhanced.In order to solve the large differences in mediastinal lymph node scale,a feature fusion module was designed to greatly enhance the accuracy of segmentation of small lymph nodes by the network.Experiments show that the proposed method achieves an accuracy rate of 76.92%,a recall rate of 79.65%,and a dice score of 76.08% in the mediastinal lymph node segmentation task.The accuracy rate,recall rate,and dice score are significantly better than other algorithms currently used for mediastinal lymph nodessegmentation.

Key words: 3D convolutional neural network, 3D medical image, Attention mechanism, Computer-aided diagnosis, Mediastinal lymph node segmentation

CLC Number: 

  • TP391.41
[1] RUSCH V W,ASAMURA H,WATANABE H,et al.TheIASLC lung cancer staging project:a proposal for a new international lymph node map in the forthcoming seventh edition of the TNM classification for lung cancer[J].Journal of thoracic oncology,2009,4(5):568-577.
[2] ZHANG L J.Consensus and controversy of mediastinal lymph node dissection in diagnosis and treatment of lung cancer[J].Chinese Journal of Lung Cancer,2018,21(3):176-179.
[3] TERÁN M D,BROCK M V.Staging lymph node metastasesfrom lung cancer in the mediastinum[J].Journal of Thoracic Disease,2014,6(3):230.
[4] LaCKEY A,DONINGTON J S.Surgical management of lungcancer[J].Seminars in Interventional Radiology,2013,30(2):133.
[5] ODA H,ROTH H R,BHATIA K K,et al.Dense volumetric detection and segmentation of mediastinal lymph nodes in chest CT images[C]//Medical Imaging 2018:Computer-Aided Diagnosis.International Society for Optics and Photonics,2018:1057502.
[6] RONNEBERGER O,FISCHER P,BROX T.U-net:Convolu-tional networks for biomedical image segmentation[C]//International Conference on Medical image computing and computer-assisted intervention.Cham:Springer,2015:234-241.
[7] BOUGET D,JRGENSEN A,KISS G,et al.Semantic segmentation and detection of mediastinal lymph nodes and anatomical structures in CT data for lung cancer staging[J].International Journal of Computer Assisted Radiology and Surgery,2019,14(2):1-10.
[8] CHEN H,WANG X,HUANG Y,et al.Harnessing 2d networksand 3d features for automated pancreas segmentation from volumetric ct images[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention.Cham:Springer,2019:339-347.
[9] LIU W,ANGUELOV D,ERHAN D,et al.SSD:single shotmultibox detector[C]//Computer Vision-ECCV 2016 14th European Conference.2016:21-37.
[10] LIN T Y,DOLLAR,PIOTR,et al.Feature Pyramid Networks for Object Detection[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017).Honolulu,HI,USA,2017:936-944.
[11] SHEN W,QIN P,ZENG J.An Indoor crowd detection network framework based on feature aggregation module and hybrid attention selection module[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops.2019.
[12] WANG X,GIRSHICK R,GUPTA A,et al.Non-local NeuralNetworks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:7794-7803.
[13] CAO Y,XU J,LIN S,et al.Gcnet:Non-local networks meet squeeze-excitation networks and beyond[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops.2019.
[14] SUTSKEVER I,VINYALS O,LE O V.Sequence to Sequence Learning with Neural Networks[C]//Advances in Neural Information Processing Systems 27:Annual Conference on Neural Information Processing Systems 2014.Montreal,Quebec,Canada,2014:3104-3112.
[15] HE K,ZHANG X,REN S,et al.Deep residual learning forimage recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778.
[16] WANG Z,ZOU N,SHEN D,et al.Non-local U-Nets for Biomedical Image Segmentation[C]//Thirty-Fourth AAAI Confe-rence on Artificial Intelligence.2020.
[17] ZHOU Z,SIDDIQUEE M M R,TAJBAKHSH N,et al.Unet++:A nested u-net architecture for medical image segmentation[M]//Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support.Cham:Springer,2018:3-11.
[18] ISENSEE F,JÄGER P F,KOHL S A A,et al.Automated design of deep learning methods for biomedical image segmentation[J].arXiv:1904.08128,2019.
[19] HE K,GKIOXARI G,DOLLÁR P P,et al.Mask r-cnn[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:2961-2969.
[20] CAI Z W,FAN Q F,FERIS R S,et al.A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection[C]//Computer Vision ECCV 2016 14th European Conference.2016:354-370.
[21] ZHOU P,NI B B,GENG C.Scale-Transferrable Object Detection[C]//2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018).Salt Lake City,UT,USA,2018:528-537.
[22] LIN T Y,GOYAL P,GIRSHICK R,et al.Focal Loss for Dense Object Detection[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2017,PP(99):2999-3007.
[23] LI X,WANG W,HU X,et al.Selective kernel networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2019:510-519.
[24] HU J,SHEN L,SUN G.Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:7132-7141.
[25] ISENSEE F,JAEGER P,WASSERTHAL J,et al.batchgenerators-a python framework for data augmentation[J].Zenodo.https://doi.org/10.5281/zenodo,2020,3632567.
[26] MILLETARI F,NAVAB N,AHMADI S A.V-net:Fully convolutional neural networks for volumetric medical image segmentation[C]//2016 Fourth International Conference on 3D Vision (3DV).IEEE,2016:565-571.
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