Well-balanced system for coronary calcium detection and volume measurement in a low resolution intravascular ultrasound videos

BACKGROUND Accurate and fast quantitative assessment of calcium volume is required during the planning of percutaneous coronary interventions procedures. Low resolution in intravascular ultrasound (IVUS) coronary videos poses a threat to calcium detection causing over-estimation in volume measurement. We introduce a correction block that counter-balances the bias introduced during the calcium detection process. METHOD Nineteen patients image dataset (around 40,090 frames), IRB approved, were collected using 40MHz IVUS catheter (Atlantis® SR Pro, Boston Scientific®, pullback speed of 0.5mm/sec). A new set of 20 generalized and well-balanced systems each consisting of three stages: (i) calcium detection, (ii) calibration and (iii) measurement, while ensuring accuracy of four soft classifiers (Threshold, FCM, K-means and HMRF) and workflow speed using five multiresolution techniques (bilinear, bicubic, wavelet, Lanczos, Gaussian Pyramid) were designed. Results of the three calcium detection methods were benchmarked against the Threshold-based method. RESULTS All 20 well-balanced systems with calibration block show superior performance. Using calibration block, FCM versus Threshold-based method shows the highest cross-correlation 0.99 (P<0.0001), Jaccard index 0.984±0.013 (P<0.0001), and Dice similarity 0.992±0.007 (P<0.0001). The corresponding area under the curve for four calcium detection techniques is: 1.0, 1.0, 0.97 and 0.93, respectively. The mean overall performance improvement is 38.54% and when adapting calibration block. The mean workflow speed improvement is 62.14% when adapting multiresolution paradigm. Three clinical tests shows consistency, reliability, and stability of our well-balanced system. CONCLUSIONS A well-balanced system with a combination of Threshold embedded with Lanczos multiresolution was optimal and can be useable in clinical settings.

[1]  Zhou Wang,et al.  Complex Wavelet Structural Similarity: A New Image Similarity Index , 2009, IEEE Transactions on Image Processing.

[2]  Jasjit S. Suri,et al.  Atherosclerosis disease management , 2011 .

[3]  Quan Wang,et al.  HMRF-EM-image: Implementation of the Hidden Markov Random Field Model and its Expectation-Maximization Algorithm , 2012, ArXiv.

[4]  Stan Z. Li,et al.  Markov Random Field Models in Computer Vision , 1994, ECCV.

[5]  Filippo Molinari,et al.  Shape‐Based Approach for Coronary Calcium Lesion Volume Measurement on Intravascular Ultrasound Imaging and Its Association With Carotid Intima‐Media Thickness , 2015, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.

[6]  Hamid Jafarkhani,et al.  A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI , 2015, Medical Image Anal..

[7]  Mark D. Huffman,et al.  Executive Summary: Heart Disease and Stroke Statistics—2015 Update A Report From the American Heart Association , 2011, Circulation.

[8]  M. F. Fuller,et al.  Practical Nonparametric Statistics; Nonparametric Statistical Inference , 1973 .

[9]  D. A. Bell,et al.  Applied Statistics , 1953, Nature.

[10]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[11]  Ken Turkowski,et al.  Filters for common resampling tasks , 1990 .

[12]  Jerry L Prince,et al.  Current methods in medical image segmentation. , 2000, Annual review of biomedical engineering.

[13]  Oriol Pujol Vila,et al.  Generalized multi-scale stacked sequential learning for multi-class classification , 2015 .

[14]  Jasjit S. Suri,et al.  Multi-Modality Atherosclerosis Imaging and Diagnosis , 2013 .

[15]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[16]  Nassir Navab,et al.  A State-of-the-Art Review on Segmentation Algorithms in Intravascular Ultrasound (IVUS) Images , 2012, IEEE Transactions on Information Technology in Biomedicine.

[18]  R. Ross,et al.  Cell biology of atherosclerosis. , 1995, Annual review of physiology.

[19]  Carl de Boor,et al.  A Practical Guide to Splines , 1978, Applied Mathematical Sciences.

[20]  G Rose ABC of vascular diseases. Epidemiology of atherosclerosis. , 1991, BMJ.

[21]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Paul Schoenhagen,et al.  Understanding coronary artery disease: tomographic imaging with intravascular ultrasound , 2002, Heart.

[23]  Robert M. Haralick,et al.  Greedy Algorithm for Error Correction in Automatically Produced Boundaries from Low Contrast Ventriculograms , 2000, Pattern Analysis & Applications.

[24]  Azriel Rosenfeld,et al.  Computer Vision , 1988, Adv. Comput..

[25]  Petia Radeva,et al.  Reliable and Accurate Calcium Volume Measurement in Coronary Artery Using Intravascular Ultrasound Videos , 2016, Journal of Medical Systems.

[26]  D. Mozaffarian,et al.  Executive summary: heart disease and stroke statistics--2012 update: a report from the American Heart Association. , 2012, Circulation.

[27]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  W. Press,et al.  Numerical Recipes: The Art of Scientific Computing , 1987 .

[29]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[30]  Sameer Singh,et al.  Advanced Algorithmic Approaches to Medical Image Segmentation , 2002, Advances in Computer Vision and Pattern Recognition.

[31]  J. Suri,et al.  Link between automated coronary calcium volumes from intravascular ultrasound to automated carotid IMT from B-mode ultrasound in coronary artery disease population. , 2014, International angiology : a journal of the International Union of Angiology.

[32]  Petia Radeva,et al.  Five multiresolution-based calcium volume measurement techniques from coronary IVUS videos: A comparative approach , 2016, Comput. Methods Programs Biomed..

[33]  Michael G. Strintzis,et al.  A novel active contour model for fully automated segmentation of intravascular ultrasound images: In vivo validation in human coronary arteries , 2007, Comput. Biol. Medicine.

[34]  Jasjit S. Suri,et al.  Computer Vision, Pattern Recognition and Image Processing in Left Ventricle Segmentation: The Last 50 Years , 2000, Pattern Analysis & Applications.

[35]  J. Suri,et al.  Advanced algorithmic approaches to medical image segmentation: state-of-the-art application in cardiology, neurology, mammography and pathology , 2001 .

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

[37]  Tae Jin Lee,et al.  DICOM-based intravascular ultrasound signal intensity analysis: an Echoplaque Medical Imaging Bench study , 2014, Coronary artery disease.

[38]  Edward H. Adelson,et al.  IMAGE DATA COMPRESSION WITH THE LAPLACIAN PYRAMID , 1981 .

[39]  Tadashi Araki,et al.  Visualization of coronary plaque in arterial remodeling using a new 40‐MHz intravascular ultrasound imaging system , 2013, Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions.

[40]  Nilanjan Dey,et al.  A comparative approach of four different image registration techniques for quantitative assessment of coronary artery calcium lesions using intravascular ultrasound , 2015, Comput. Methods Programs Biomed..

[41]  William H. Press,et al.  Numerical recipes : the art of scientific computing : FORTRAN version , 1989 .

[42]  N J Weissman,et al.  Pathologic validation of a new method to quantify coronary calcific deposits in vivo using intravascular ultrasound. , 2000, The American journal of cardiology.

[43]  P. Libby,et al.  Progress and challenges in translating the biology of atherosclerosis , 2011, Nature.

[44]  N. Otsu A threshold selection method from gray level histograms , 1979 .