Chaofan Du and Peter Xiaoping Liu


  1. [1] M.M. Mohamed, J. Gu, and J. Luo, Modular design ofneurosurgical robotic system, International Journal of Roboticsand Automation, 33(5), 2019, 542–551.
  2. [2] J. Chen and H. Lau, Policy gradient-based inverse kinematicsrefinement for tendon-driven serpentine surgical manipulator,International Journal of Robotics and Automation, 34(3), 2019,303–311.
  3. [3] K. Wang, H. Yang, W. Wang, and Z. Han, Force configuration ofa rigid-flexible gait rehabilitation robot, International Journalof Robotics and Automation, 33(6), 2018, 577–583.
  4. [4] G. Yin, X. Zhang, and J.C. Chen, An approach for sEMG-basedvariable damping control of lower limb rehabilitation robot,International Journal of Robotics and Automation, 35(3), 2020,171–180.
  5. [5] V. Azimirad, Y. Salekzamani, and M. Ahmadi, Roboticdiagnosis of trigger points, International Journal of Roboticsand Automation, 31(6), 2016, 446–452.
  6. [6] J. Seo, J. Cho, H. Woo, and Y. Lee, Development ofprototype system for robot-assisted ultrasound diagnosis,227Proc. International Conf. on Control Automation and Systems(ICCAS), Busan, 2015, 1285–1288.
  7. [7] J. An and S. Lee, Medical image analysis by robot kinematics,Key Engineering Materials, 326–328, 2006, 875–878.
  8. [8] S.E. Salcudean, H. Moradi, D.G. Black, and N. Navab, Robot-assisted medical imaging: A review, Proceedings of the IEEE,110(7), 2020, 951–967.
  9. [9] Z. Zhang, B. Rosa, O. Caravaca-Mora, P. Zanne, M.J. Gora, andF. Nageotte, Image-guided control of an endoscopic robot forOCT path scanning, IEEE Robotics and Automation Letters,6(3), 2021, 5881–5888.
  10. [10] X. Huang, J. Ren, G. Guiraudon, D. Boughner, and T.M.Peters, Rapid dynamic image registration of the beating heartfor diagnosis and surgical navigation, IEEE Transactions onMedical Imaging, 28(11), 2009, 1802–1814.
  11. [11] M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville,Y. Bengio, C. Pal, P.M. Jodoin, and H. Larochelle, Braintumor segmentation with deep neural networks, Medical ImageAnalysis, 35, 2016, 18–31.
  12. [12] M.I. Razzak, M. Imran, and G. Xu, Efficient brain tumorsegmentation with multiscale two-pathway-group conventionalneural networks, IEEE Journal of Biomedical and HealthInformatics, 23(5), 2019, 1911–1919.
  13. [13] S.S. Mohseni Salehi, D. Erdogmus, and A. Gholipour, Auto-context convolutional neural network (Auto-Net) for brainextraction in magnetic resonance imaging, IEEE Transactionson Medical Imaging, 36(11), 2017, 2319–2330.
  14. [14] E. Shelhamer, J. Long, and T. Darrell, Fully convolutionalnetworks for semantic segmentation, IEEE Transactions onPattern Analysis and Machine Intelligence, 39(4), 2017,640–651.
  15. [15] O. Ronneberger, P. Fischer, and T. Brox, U-Net: Convolutionalnetworks for biomedical image segmentation, Proc. Interna-tional Conf. on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Cham, 2015, 234–241.
  16. [16] M. Zahangir Alom, M. Hasan, C. Yakopcic, T.M. Taha, andV.K. Asari, Recurrent residual convolutional neural networkbased on U-Net (R2U-Net) for medical image segmentation,arXiv:1802.06955, 2018.
  17. [17] X. Xiao, S. Lian, Z. Luo, and S. Li, Weighted Res-UNet forhigh-quality retina vessel segmentation, Proc. InternationalConf. on Information Technology in Medicine and Education(ITME), Hangzhou, 2018, 327–331.
  18. [18] T. Tarasiewicz, M. Kawulok, and J. Nalepa, Lightweight U-Netsfor brain tumor segmentation, in Lecture notes in computerscience, (Cham: Springer, 2021), 3–14.
  19. [19] Y. Wang, Y. Cao, J. Li, H. Wu, S. Wang, X. Dong, and H. Yu,A lightweight hierarchical convolution network for brain tumorsegmentation, BMC Bioinformatics, 22(5), 2022, 636.
  20. [20] A. Paszke, A. Chaurasia, S. Kim, and E. Culurciello, ENet:A deep neural network architecture for real-time semanticsegmentation, arXiv:1804.02767, 2016.
  21. [21] J. Hu, L. Shen, and G. Sun, Squeeze-and-excitation networks,Proc. IEEE Conf. on Computer Vision and Pattern Recognition(CVPR), Salt Lake City, UT, 2018, 7132–7141.
  22. [22] F. Milletari, N. Navab, and S.-A. Ahmadi, V-Net: Fullyconvolutional neural networks for volumetric medical imagesegmentation, Proc. of 2016 fourth International Conf. on 3DVision (3DV), Stanford, CA, 2016, 565–571.
  23. [23] D. Lachinov, E. Vasiliev, and V. Turlapov, Glioma segmentationwith cascaded UNet, Proc. of the International MICCAIBrainlesion Workshop, Cham, 2019, 189–198.
  24. [24] G. Wang, W. Li, S. Ourselin, and T. Vercauteren, Automaticbrain tumor segmentation based on cascaded convolutionalneural networks with uncertainty estimation, Frontiers inComputational Neuroscience, 13, 2019, 56.
  25. [25] M. U. Saeed, G. Ali, W. Bin, S. H. Almotiri, M. A. AlGhamdi,A. Ali Nagra, K. Masood, and R. Ul Amin, RMU-Net: A novelresidual mobile U-Net model for brain tumor segmentationfrom MR images, Electronics, 10(16), 2021, 1962.
  26. [26] J. Huang, M. Zheng, and P.X. Liu, Automatic brain tumorsegmentation using 3D architecture based on ROI extraction,Proc. 2019 IEEE International Conf. on Robotics andBiomimetics (ROBIO), Dali, 2019, 36–40.
  27. [27] C. Zhou, S. Chen, C. Ding, and D. Tao, Learning contextualand attentive information for brain tumor segmentation, Proc.of the International MICCAI Brainlesion Workshop, Cham,2019, 497–507.
  28. [28] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C.Chen, MobileNetV2: Inverted residuals and linear bottlenecks,Proc. IEEE Conf. on Computer Vision and Pattern Recognition(CVPR), Salt Lake City, UT, 2018, 4510–4520.

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