Automatic segmentation of cross-sectional coronary arterial images

A novel fully automated graph cut method to segment IVUS and OCT images.Shape priors to impose global and local shape constraints.Adaptive cost function based on data driven artifact classification.Tracking integrated into graph segmentation to efficiently impose temporal consistency. We present a novel approach to segment coronary cross-sectional images acquired using catheterization imaging techniques, i.e. intra-vascular ultrasound (IVUS) and optical coherence tomography (OCT). The proposed approach combines cross-sectional segmentation with longitudinal tracking in order to tackle various forms of imaging artifacts and to achieve consistent segmentation. A node-weighted directed graph is constructed on two consecutive cross-sectional frames with embedded shape constraints within individual cross-sections or frames and between consecutive frames. The intra-frame constraints are derived from a set of training samples and are embedded in both graph construction and its cost function. The inter-frame constraints are imposed by tracking the borders of interest across multiple frames. The coronary images are transformed from Cartesian coordinates to polar coordinates. Graph partition can then be formulated as searching an optimal interface in the node-weighted directed graph without user initialization. It also allows efficient parametrization of the border using radial basis function (RBF) and thus reduces the tracking of a large number of border points to a very few RBF centers. Moreover, we carry out supervised column-wise tissue classification in order to automatically optimize the feature selection. Instead of empirically assigning weights to different feature detectors, we dynamically and automatically adapt those weighting depending on the tissue compositions in each individual column of pixels. The proposed approach is applied to IVUS and OCT images. Both qualitative and quantitative results show superior performance of the proposed method compared to a number of alternative segmentation techniques.

[1]  Seyed Kamaledin Setarehdan,et al.  Automatic media-adventitia IVUS image segmentation based on sparse representation framework and dynamic directional active contour model , 2017, Comput. Biol. Medicine.

[2]  E. Gerardo Mendizabal-Ruiz,et al.  Segmentation of the luminal border in intravascular ultrasound B-mode images using a probabilistic approach , 2013, Medical Image Anal..

[3]  Qinye Yin,et al.  Automatic Lumen Segmentation in Intravascular Optical Coherence Tomography Images Using Level Set , 2017, Comput. Math. Methods Medicine.

[4]  Milan Sonka,et al.  Graph-Based IVUS Segmentation With Efficient Computer-Aided Refinement , 2013, IEEE Transactions on Medical Imaging.

[5]  Heye Zhang,et al.  Automated Framework for Detecting Lumen and Media-Adventitia Borders in Intravascular Ultrasound Images. , 2015, Ultrasound in medicine & biology.

[6]  Daniel Cremers,et al.  Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation , 2006, International Journal of Computer Vision.

[7]  Xun Liang,et al.  An Effective Method of Pruning Support Vector Machine Classifiers , 2010, IEEE Transactions on Neural Networks.

[8]  Xiaodong Wu,et al.  Optimal Net Surface Problems with Applications , 2002, ICALP.

[9]  Xiaodong Wu,et al.  Simultaneous searching of globally optimal interacting surfaces with shape priors , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Zhenghui Hu,et al.  An artificial neural network method for lumen and media-adventitia border detection in IVUS , 2017, Comput. Medical Imaging Graph..

[11]  E. Gerardo Mendizabal-Ruiz,et al.  Computerized Medical Imaging and Graphics , 2022 .

[12]  D. Vince,et al.  Evaluation of three-dimensional segmentation algorithms for the identification of luminal and medial-adventitial borders in intravascular ultrasound images , 2000, IEEE Transactions on Medical Imaging.

[13]  Andrew Blake,et al.  Random Forest Classification for Automatic Delineation of Myocardium in Real-Time 3D Echocardiography , 2009, FIMH.

[14]  Xiaodong Wu,et al.  Intraretinal Layer Segmentation of Macular Optical Coherence Tomography Images Using Optimal 3-D Graph Search , 2008, IEEE Transactions on Medical Imaging.

[15]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[16]  Kun Huang,et al.  Automatic detection of stent struts with thick neointimal growth in intravascular optical coherence tomography image sequences. , 2011, Physics in medicine and biology.

[17]  Peter Kovesi,et al.  Symmetry and Asymmetry from Local Phase , 1997 .

[18]  James F. O'Brien,et al.  Modelling with implicit surfaces that interpolate , 2005, SIGGRAPH Courses.

[19]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[20]  Gökhan BakIr,et al.  Fast Binary and Multi-Output Reduced Set Selection , 2004 .

[21]  Xianghua Xie,et al.  Automatic IVUS media-adventitia border extraction using double interface graph cut segmentation , 2011, 2011 18th IEEE International Conference on Image Processing.

[22]  Edward H. Adelson,et al.  The Design and Use of Steerable Filters , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Hiroshi Ishikawa,et al.  Exact Optimization for Markov Random Fields with Convex Priors , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Petia Radeva,et al.  Automatic Detection of Bioabsorbable Coronary Stents in IVUS Images Using a Cascade of Classifiers , 2010, IEEE Transactions on Information Technology in Biomedicine.

[25]  J. Alison Noble,et al.  2D+T acoustic boundary detection in echocardiography , 2000, Medical Image Anal..

[26]  Gareth Funka-Lea,et al.  Graph Cuts and Efficient N-D Image Segmentation , 2006, International Journal of Computer Vision.

[27]  Milan Sonka,et al.  Segmentation of intravascular ultrasound images: a knowledge-based approach , 1995, IEEE Trans. Medical Imaging.

[28]  Ioannis Kompatsiaris,et al.  Image analysis techniques for automated IVUS contour detection. , 2008, Ultrasound in medicine & biology.

[29]  W. Desmet,et al.  Automatic segmentation of in-vivo intra-coronary optical coherence tomography images to assess stent strut apposition and coverage , 2012, The International Journal of Cardiovascular Imaging.

[30]  B. S. Manjunath,et al.  Shape prior segmentation of multiple objects with graph cuts , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[31]  P. Fitzgerald,et al.  Automated contour detection for high-frequency intravascular ultrasound imaging: a technique with blood noise reduction for edge enhancement. , 2000, Ultrasound in medicine & biology.

[32]  Daniel Rueckert,et al.  Automatical vessel wall detection in intravascular coronary OCT , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[33]  Milan Sonka,et al.  Plaque development, vessel curvature, and wall shear stress in coronary arteries assessed by X-ray angiography and intravascular ultrasound , 2006, Medical Image Anal..

[34]  Guy Cloutier,et al.  Segmentation method of intravascular ultrasound images of human coronary arteries , 2014, Comput. Medical Imaging Graph..

[35]  Claude Kauffmann,et al.  In Vivo Supervised Analysis of Stent Reendothelialization From Optical Coherence Tomography , 2010, IEEE Transactions on Medical Imaging.

[36]  Rich Caruana,et al.  An empirical comparison of supervised learning algorithms , 2006, ICML.

[37]  Gözde B. Ünal,et al.  A New 3-D Automated Computational Method to Evaluate In-Stent Neointimal Hyperplasia in In-Vivo Intravascular Optical Coherence Tomography Pullbacks , 2009, MICCAI.

[38]  Andrew Zisserman,et al.  Image Classification using Random Forests and Ferns , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[39]  Gozde Unal,et al.  Stent implant follow-up in intravascular optical coherence tomography images , 2010, The International Journal of Cardiovascular Imaging.

[40]  Olga Veksler,et al.  Star Shape Prior for Graph-Cut Image Segmentation , 2008, ECCV.

[41]  Xianghua Xie,et al.  Combining region-based and imprecise boundary-based cues for interactive medical image segmentation. , 2014, International journal for numerical methods in biomedical engineering.

[42]  M Yoshizawa,et al.  Detection and quantification of calcifications in intravascular ultrasound images by automatic thresholding. , 2008, Ultrasound in medicine & biology.

[43]  Tong Fang,et al.  Shape-Driven Segmentation of the Arterial Wall in Intravascular Ultrasound Images , 2008, IEEE Transactions on Information Technology in Biomedicine.

[44]  Xianghua Xie,et al.  Shape Prior Model for Media-Adventitia Border Segmentation in IVUS Using Graph Cut , 2012, MCV.

[45]  Stavros Tsantis,et al.  Automatic vessel lumen segmentation and stent strut detection in intravascular optical coherence tomography. , 2011, Medical physics.

[46]  Vladimir Kolmogorov,et al.  An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision , 2001, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[47]  Tao Zhang,et al.  Interactive graph cut based segmentation with shape priors , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[48]  Thiagalingam Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation , 2001 .

[49]  Xiaodong Wu,et al.  Optimal Surface Segmentation in Volumetric Images-A Graph-Theoretic Approach , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[50]  Yogesh Rathi,et al.  Graph Cut Segmentation with Nonlinear Shape Priors , 2007, 2007 IEEE International Conference on Image Processing.

[51]  Dimitrios I. Fotiadis,et al.  A Novel Semiautomated Atherosclerotic Plaque Characterization Method Using Grayscale Intravascular Ultrasound Images: Comparison With Virtual Histology , 2012, IEEE Transactions on Information Technology in Biomedicine.

[52]  Xianghua Xie,et al.  Minimum S-Excess Graph for Segmenting and Tracking Multiple Borders with HMM , 2015, MICCAI.

[53]  Hiram G. Bezerra,et al.  Plaque and thrombus evaluation by optical coherence tomography , 2011, The International Journal of Cardiovascular Imaging.

[54]  D. Simon.  Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches , 2006 .

[55]  Jean Meunier,et al.  Intravascular Ultrasound Image Segmentation: A Fast-Marching Method , 2003, MICCAI.