HUANG Dongjin, TANG Pengbin, WANG Yin, et al., “Computer-Assisted Path Planning for Minimally Invasive Vascular Surgery,” Chinese Journal of Electronics, vol. 27, no. 6, pp. 1241-1249, 2018, doi: 10.1049/cje.2018.09.002
Citation: HUANG Dongjin, TANG Pengbin, WANG Yin, et al., “Computer-Assisted Path Planning for Minimally Invasive Vascular Surgery,” Chinese Journal of Electronics, vol. 27, no. 6, pp. 1241-1249, 2018, doi: 10.1049/cje.2018.09.002

Computer-Assisted Path Planning for Minimally Invasive Vascular Surgery

doi: 10.1049/cje.2018.09.002
Funds:  This work is supported by the National Natural Science Foundation of China (No.61402278), the Shanghai Natural Science Foundation of China (No.14ZR1415800), the Innovation Program of the Science and Technology Commission of Shanghai Municipality of China (No.16511101302), Research Program of Shanghai Engineering Research Center of Motion Picture Special Effects (No.16dz2251300), and Shanghai University Film Peak Discipline.
  • Received Date: 2017-06-19
  • Rev Recd Date: 2018-08-03
  • Publish Date: 2018-11-10
  • Path planning assisted by two-dimensional medical images is an essential part of minimally invasive diagnosis and treatment for cardiovascular diseases. Due to the complex background of angiography images and intricate vascular structure with multi-branch and stenoses, creating accurate pathways from angiography image is a challenge task. We present a new path planning methodology based on angiography medical images using the steady fluid dynamics. Our novel approach is useful in many medical applications, such as for computer-assisted medical images analysis and the follow-on image-guided interventions. A graph-cuts based energy function was applied to the vessel segmentation of angiography images in order to obtain boundary information. We have adopted Finite volume method (FVM) to simulate the Newtonian fluid inside the segmented blood vessels, and a set of isobars under the steady fluid condition are obtained by Meandering Triangles algorithm. The selected center points of isobars are organized to generate the directed vessels-tree, from which the vascular stenoses are automatically detected and the final surgical path is generated with branches. Our method can be used for quantitative path analysis, and we show experimental results to demonstrate that the versatility and applicability of the algorithm in obtaining single-pixel surgical path with good performance, high accuracy and less manual interventions, especially it is robust on complex vascular structures.
  • loading
  • A. Hernandez-Vela, C. Gatta, S. Escalera, et al., “Accurate coronary centerline extraction, caliber estimation, and catheter detection in angiographies”, IEEE Transactions on Information Technology in Biomedicine, Vol.16, No.6, pp.1332-1340, 2012.
    H. Blum, “Models for the perception of speech and visual form”, MIT Press, USA, pp.362-380, 1967.
    Z. Shoujun, Y. Jian, W. Yongtian, et al., “Automatic segmentation of coronary angiograms based on fuzzy inferring and probabilistic tracking”, Biomedical Engineering Online, Vol.9, No.40, pp.1-21, 2010.
    I. Cruz-Aceves, F. Oloumi, RM. Rangayyan, et al., “Automatic segmentation of coronary arteries using Gabor filters and thresholding based on multiobjective optimization”, Biomedical Signal Processing and Control, Vol.25, pp.76-85, 2016.
    K. Krissian, G. Malandain, N. Ayache, et al., “Model based detection of tubular structures in 3d images”. Computer Vision and Image Understanding, Vol.80, No.2, pp.130-171, 2000.
    J. Staal, M.D. Abràmoff, M. Niemeijer, et al., “Ridge-based vessel segmentation in color images of the retina”, IEEE Transactions on Medical Imaging, Vol.23, No.4, pp.501-509, 2004.
    O.K.C. Au, C.L. Tai, H.K. Chu, et al., “Skeleton extraction by mesh contraction”, ACM Transactions on Graphics (TOG), Vol.27, No.3, pp.1567-1573, 2008.
    S. Wang, J. Wu, M. Wei, et al., “Robust curve skeleton extraction for vascular structures”, Graphical Models, Vol.74, No.4, pp.109-120, 2012.
    J. Wu, H. Wang, P. Zhang, et al., “A preliminary real-time and realistic simulation environment for percutaneous coronary intervention”, BioMed Research International, Article ID 183157, 10 pages, 2015.
    M. Hassouna and A. Farag, “Multistencils fast marching methods: A highly accurate solution to the eikonal equation on cartesian domains”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.29, No.9, pp.1563-1574, 2007.
    B. Liu, A.C. Telea, J.B.T.M. Roerdink, et al., “Parallel centerline extraction on the GPU”, Computers & Graphics, Vol.41, No.1, pp.72-83, 2014.
    D. Jin, K.S. Iyer, C. Cheng, et al., “A robust and efficient curve skeletonization algorithm for tree-like objects using minimum cost paths”, Pattern Recognition Letters, Vol.76, pp.32-40,2015.
    N. Mayya and V.T. Rajan, “An efficient shape representation scheme using voronoi skeletons”, Pattern Recognition Letters, Vol.16, No.2, pp.147-160, 1995.
    J.F. Fan, J. Yang and Y.T. Wang, “A fast vascular centerline extraction method based on voronoi diagram”, Transaction of Beijing Institute of Technology, Vol.33, No.12, pp.1303-1308, 2013. (in Chinese)
    F. Yang, Z.G. Hou, S.H. Mi, et al., “Centerlines extraction for lumen model of human vasculature for computer-aided simulation of intravascular procedures”, Intelligent Control and Automation, pp.970-975, 2014.
    P. Long, H. Lu and A. Wang, “A novel unsupervised two-stage technique in color image segmentation”, Chinese Journal of Electronics, Vol.27, No.2, pp.405-412, 2018.
    A.P. Kiraly, J.P. Helferty, E.A. Hoffman, et al., “Three dimensional path planning for virtual bronchoscopy”, IEEE Transactions on Medical Imaging, Vol.23, No.11, pp.1365-1379, 2004.
    D.G. Kang and J.B. Ra, “A new path planning algorithm for maximizing visibility in computed tomography colonography”, IEEE Transactions on Medical Imaging, Vol.24, No.8, pp.957-968, 2005.
    Y. Xu, G. Liang, G. Hu, et al., “Quantification of coronary arterial stenoses in cta using fuzzy distance transform”, Computerized Medical Imaging and Graphics, Vol.36, No.1, pp.11-24, 2012.
    A. Eslami, A. Aboee, Z. Hodaei, et al., “Quantification of coronary arterial stenosis by inflating tubes in ct angiographic images”, Proc. MICCAI Workshop 3D Cardiovascular Imaging: A MICCAI Segmentation Challenge, 2012.
    K. Mori, J. Hasegawa, Y. Suenaga, et al., “Automated anatomical labeling of the bronchial branch and its application to the virtual bronchoscopy system”, IEEE Transactions on Medical Imaging, Vol.19, No.2, pp.103-114, 2000.
    J. Tschirren, G. Mclennan, K, Palcgyi, et al., “Matching and anatomical labeling of human airway tree”, IEEE Transactions on Medical Imaging, Vol.24, No.12, pp.1540-1547, 2005.
    R. Zwiggelaar, S.M. Astley, C.R. Boggis, et al., “Linear structures in mammographic images: Detection and classification”, IEEE Transactions on Medical Imaging, Vol.23, No.23, pp.1077-1086, 2004.
    P.K. Saha, Y. Xu, H. Duan, et al., “Volumetric topological analysis: A novel approach for trabecular bone classification on the continuum between plates and rods”, IEEE Transactions on Medical Imaging, Vol.29, No.29, pp.1821-1838, 2010.
    P. Li, S. Jiang, D. Liang, et al., “Modeling of path planning and needle steering with path tracking in anatomical soft tissues for minimally invasive surgery”, Medical Engineering & Physics, Vol.41, pp.35-45, 2017.
    L. Mei and Z. Ke, “The influence of arterial stenosis in bifurcate blood vessel on the blood flow”, Chinese Journal of Applied Mechanics, Vol.3, pp.417-421, 2013. (in Chinese)
  • 加载中


    通讯作者: 陈斌,
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (473) PDF downloads(242) Cited by()
    Proportional views


    DownLoad:  Full-Size Img  PowerPoint