Guidewire Tracking Based on Regional Proposal Network and Residual Structure
LIU Shiqi1,2, SUN Xiaobo1, XIE Xiaoliang2, HOU Zengguang2
1.School of Automation, Harbin University of Science and Tech-nology, Harbin 150080 2. State Key Laboratory of Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190
Abstract：X-ray image navigation is a breakthrough of improving the accuracy and safety of robotic interventional surgery. A method based on region proposal network, residual structure and canny edge detection is proposed in this paper. It is specifically designed for guidewire segmentation framework. In the image calibration, multi-scale marking strategies are adopted to enable detection networks to learn accurate features. In the image augmentation, a multi-filter fusion strategy is employed to increase the recognizability of the guidewire and improve the tracking accuracy and the system robustness. The experiment is conducted on 22 sets of X-ray video sequences. Experimental results demonstrate the superiority of the proposed algorithm in terms of speed, accuracy and robustness.
[1] CHANG P L, ROLLS A, DE PRAETERE H, et al. Robust Catheter and Guidewire Tracking Using B-Spline Tube Model and Pixel-Wise Posteriors. IEEE Robotics and Automation Letters, 2017, 1(1): 303-308. [2] HEIBEL T H, GLOCKER B, GROHER M, et al. Discrete Tracking of Parametrized Curves // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2009: 1754-1761. [3] HONNORAT N, VAILLANT R, PARAGIOS N. Graph-Based Geometric-Iconic Guide-Wire Tracking // Proc of the 14th International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin, Germany: Springer, 2011: 9-16. [4] WANG L, XIE X L, GAO Z J, et al. Guide-Wire Detecting Using a Modified Cascade Classifier in Interventional Radiology // Proc of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Washington, USA: IEEE, 2016: 1240-1243. [5] WANG L, XIE X L, BIAN G B, et al. Guide-Wire Detection Using Region Proposal Network for X-Ray Image-Guided Navigation // Proc of the International Joint Conference on Neural Network. Wa-shington, USA: IEEE, 2017: 3169-3175. [6] REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2015, 39(6): 1137-1149. [7] FAZLALI H R, KARIMI N, SOROUSHMEHR S, et al. Vessel Region Detection in Coronary X-Ray Angiogram // Proc of the IEEE Conference on Image Processing. Washington, USA: IEEE, 2015: 1493-1497. [8] VANDINI A, GLOCKER B, HAMADY M, et al. Robust Guidewire Tracking under Large Deformations Combining Segment-Like Features(SEGlets). Medical Image Analysis, 2017, 38: 150-164. [9] HE K M, ZHANG X Y, REN S Q, et al. Deep Residual Learning for Image Recognition // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2016: 770-778. [10] CANNY J. A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(6): 679-698. [11] REN S Q, HE K M, GIRSHICK R, et al. Object Detection Networks on Convolutional Feature Maps. IEEE Transactions on Pa-ttern Analysis and Machine Intelligence, 2016, 39(7): 1476-1481. [12] SHRIVASTAVA A, GUPTA A, GIRSHICK R. Training Region-Based Object Detectors with Online Hard Example Mining // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2016: 761-769. [13] HE K M, ZHANG X Y, REN S Q, et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on Imagenet Classification // Proc of the IEEE Conference on Computer Vision and Pa-ttern Recognition. Washington, USA: IEEE, 2015: 1026-1034. [14] KINGMA D P, BA J. Adam: A Method for Stochastic Optimization[C/OL]. [2018-08-15]. https://arxiv.org/pdf/1803.09574.pdf. [15] DUCHI J, HAZAN E, SINGER Y. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. Journal of Machine Learning Research, 2011, 12: 2121-2159. [16] WANG P, LIAO W S, CHEN T, et al. Graph Based Interactive Detection of Curve Structures in 2D Fluoroscopy // Proc of the International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin, Germany: Springer, 2010: 269-277. [17] HEIBEL H, GLOCKER B, GROHER M, et al. Interventional Tool Tracking Using Discrete Optimization. IEEE Transactions on Medical Imaging, 2013, 32(3): 544-555. [18] SMEULDERS A W M, CHU D M, CUCCHIARA R, et al. Visual Tracking: An Experimental Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(7): 1442-1468.
2019, Vol. 32 
