Intra- and inter-operator reproducibility of automated cloud-based carotid lumen diameter ultrasound measurement

Background: Common carotid artery lumen diameter (LD) ultrasound measurement systems are either manual or semi-automated and lack reproducibility and variability studies. This pilot study presents an automated and cloud-based LD measurements software system (AtheroCloud) and evaluates its: (i) intra/inter-operator reproducibility and (ii) intra/inter-observer variability. Methods: 100 patients (83 M, mean age: 68 ± 11 years), IRB approved, consisted of L/R CCA artery (200 ultrasound images), acquired using a 7.5-MHz linear transducer. The intra/inter-operator reproducibility was verified using three operator's readings. Near-wall and far carotid wall borders were manually traced by two observers for intra/inter-observer variability analysis. Results: The mean coefficient of correlation (CC) for intra- and inter-operator reproducibility between all the three automated reading pairs were: 0.99 (P < 0.0001) and 0.97 (P < 0.0001), respectively. The mean CC for intra- and inter-observer variability between both the manual reading pairs were 0.98 (P < 0.0001) and 0.98 (P < 0.0001), respectively. The Figure-of-Merit between the mean of the three automated readings against the four manuals were 98.32%, 99.50%, 98.94% and 98.49%, respectively. Conclusions: The AtheroCloud LD measurement system showed high intra/inter-operator reproducibility hence can be adapted for vascular screening mode or pharmaceutical clinical trial mode

Relationship between automated coronary calcium volumes and a set of manual coronary lumen volume, vessel volume and atheroma volume in japanese diabetic cohort

Introduction: A high degree of correlation exists between Coronary Artery Diseases (CAD) and calcification of the vessel wall. For Percutaneous Coronary Interventional (PCI) planning, it is essential to have an exact understanding of the extent to which calcium volume is correlated to the lumen, vessel, and atheroma volume regions in the coronary artery, which is unclear in recent studies. Aim: Four automated Coronary Calcium Volume (aCCV) measurement methods threshold, Fuzzy c-Means (FCM), K-means, and Hidden Markov Random Field (HMRF) and its correlation with three manual (experts) coronary parameters namely: Coronary Vessel Volume (mCVV), Coronary Lumen Volume (mCLV), and Coronary Atheroma Volume (mCAV), was determined in a Japanese diabetic cohort. Materials and Methods: Intravascular Ultrasound (IVUS) image dataset from 19 patients (around 40,090 frames) was collected using 40 MHz IVUS catheter (Atlantis® SR Pro, Boston Scientific®, pullback speed of 0.5 mm/sec). The methodology consisted of automatically computing the calcium volume in the entire IVUS coronary videos using FCM, K-means, and HMRF based pixel classification and comparing it against the previously published threshold-based method. The Coefficient of Correlation (CC) was then established between the four aCCV and three manually (experts) coronary parameters: mCVV, mCLV, and mCAV computed using iMAP software Boston Scientific®. Statistical tests (Two-tailed paired Student t-test, Wilcoxon signed rank test, Mann-Whitney test, Chi-square test, and Kolmogorov-Smirnov KS-test) were performed to demonstrate consistency, reliability, and accuracy of the proposed work. Results: Correlation coefficient of: (a) automated threshold-based volume; (b) automated FCM based volume; (c) automated K-means based volume; and (d) automated HMRF based volume and corresponding three manually (expertâ\u80\u99s) coronary parameters (mCLV, mCVV, mCAV) were: (0.51, 0.40, 0.48), (0.52, 0.38, 0.49), (0.56, 0.45, 0.52), and (0.57, 0.42, 0.56), respectively. The CC between age and haemoglobin was 0.50. Conclusion: Automated coronary volume measurement using HMRF method is more accurate compared to threshold, FCM, and K-means-based method, since it is more strongly correlated with three expertâ\u80\u99s readings

Calcium detection, its quantification, and grayscale morphology-based risk stratification using machine learning in multimodality big data coronary and carotid scans: A review

Purpose of review: Atherosclerosis is the leading cause of cardiovascular disease (CVD) and stroke. Typically, atherosclerotic calcium is found during the mature stage of the atherosclerosis disease. It is therefore often a challenge to identify and quantify the calcium. This is due to the presence of multiple components of plaque buildup in the arterial walls. The American College of Cardiology/American Heart Association guidelines point to the importance of calcium in the coronary and carotid arteries and further recommend its quantification for the prevention of heart disease. It is therefore essential to stratify the CVD risk of the patient into low- and high-risk bins. Recent finding: Calcium formation in the artery walls is multifocal in nature with sizes at the micrometer level. Thus, its detection requires high-resolution imaging. Clinical experience has shown that even though optical coherence tomography offers better resolution, intravascular ultrasound still remains an important imaging modality for coronary wall imaging. For a computer-based analysis system to be complete, it must be scientifically and clinically validated. This study presents a state-of-the-art review (condensation of 152 publications after examining 200 articles) covering the methods for calcium detection and its quantification for coronary and carotid arteries, the pros and cons of these methods, and the risk stratification strategies. The review also presents different kinds of statistical models and gold standard solutions for the evaluation of software systems useful for calcium detection and quantification. Finally, the review concludes with a possible vision for designing the next-generation system for better clinical outcomes

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 40 MHz IVUS catheter (Atlantis® SR Pro, Boston Scientific®, pullback speed of 0.5 mm/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

Wall-based measurement features provides an improved IVUS coronary artery risk assessment when fused with plaque texture-based features during machine learning paradigm

Background Planning of percutaneous interventional procedures involves a pre-screening and risk stratification of the coronary artery disease. Current screening tools use stand-alone plaque texture-based features and therefore lack the ability to stratify the risk. Method This IRB approved study presents a novel strategy for coronary artery disease risk stratification using an amalgamation of IVUS plaque texture-based and wall-based measurement features. Due to common genetic plaque makeup, carotid plaque burden was chosen as a gold standard for risk labels during training-phase of machine learning (ML) paradigm. Cross-validation protocol was adopted to compute the accuracy of the ML framework. A set of 59 plaque texture-based features was padded with six wall-based measurement features to show the improvement in stratification accuracy. The ML system was executed using principle component analysis-based framework for dimensionality reduction and uses support vector machine classifier for training and testing-phases. Results The ML system produced a stratification accuracy of 91.28%, demonstrating an improvement of 5.69% when wall-based measurement features were combined with plaque texture-based features. The fused system showed an improvement in mean sensitivity, specificity, positive predictive value, and area under the curve by: 6.39%, 4.59%, 3.31% and 5.48%, respectively when compared to the stand-alone system. While meeting the stability criteria of 5%, the ML system also showed a high average feature retaining power and mean reliability of 89.32% and 98.24%, respectively. Conclusions The ML system showed an improvement in risk stratification accuracy when the wall-based measurement features were fused with the plaque texture-based features

Web-based accurate measurements of carotid lumen diameter and stenosis severity: An ultrasound-based clinical tool for stroke risk assessment during multicenter clinical trials

Background This pilot study presents a completely automated, novel, smart, cloud-based, point-of-care system for (a) carotid lumen diameter (LD); (b) stenosis severity index (SSI) and (c) total lumen area (TLA) measurement using B-mode ultrasound. The proposed system was (i) validated against manual reading taken by the Neurologist and (ii) benchmarked against the commercially available system. Method One hundred patients (73 M/27 F, mean age: 68 ± 11 years), institutional review board approved, written informed consent, consisted of left/right common carotid artery (200 ultrasound scans) were acquired using a 7.5-MHz linear transducer. Results The measured mean LD for left and right carotids were (in mm): (i) for proposed system (6.49 ± 1.77, 6.66 ± 1.70); and (ii) for manual (6.29 ± 1.79, 6.45 ± 1.63), respectively and coefficient of correlation between cloud-based automated against manual were 0.98 (P < 0.0001) and 0.99 (P < 0.0001), respectively. The corresponding TLA error, Precision-of-Merit, and Figure-of-Merit when measured against the manual were: 4.56 ± 3.54%, 96.18 ± 3.21%, and 96.85%, respectively. The AUC for the receiving operating characteristics for the cloud-based system was: 1.0. Four statistical tests such as: Two-tailed z-test, Mann-Whitney test, Kolmogorov-Smirnov (KS) and one-way ANOVA were performed to demonstrate consistency and reliability. Conclusions The proposed system is reliable, accurate, fast, completely automated, anytime-anywhere solution for multi-center clinical trials and routine vascular screening

A new method for IVUS-based coronary artery disease risk stratification: A link between coronary & carotid ultrasound plaque burdens

Interventional cardiologists have a deep interest in risk stratification prior to stenting and percutaneous coronary intervention (PCI) procedures. Intravascular ultrasound (IVUS) is most commonly adapted for screening, but current tools lack the ability for risk stratification based on grayscale plaque morphology. Our hypothesis is based on the genetic makeup of the atherosclerosis disease, that there is evidence of a link between coronary atherosclerosis disease and carotid plaque built up. This novel idea is explored in this study for coronary risk assessment and its classification of patients between high risk and low risk.This paper presents a strategy for coronary risk assessment by combining the IVUS grayscale plaque morphology and carotid B-mode ultrasound carotid intima-media thickness (cIMT) - a marker of subclinical atherosclerosis. Support vector machine (SVM) learning paradigm is adapted for risk stratification, where both the learning and testing phases use tissue characteristics derived from six feature combinational spaces, which are then used by the SVM classifier with five different kernels sets. These six feature combinational spaces are designed using 56 novel feature sets. K-fold cross validation protocol with 10 trials per fold is used for optimization of best SVM-kernel and best feature combination set.IRB approved coronary IVUS and carotid B-mode ultrasound were jointly collected on 15 patients (2 days apart) via: (a) 40. MHz catheter utilizing iMap (Boston Scientific, Marlborough, MA, USA) with 2865 frames per patient (42,975 frames) and (b) linear probe B-mode carotid ultrasound (Toshiba scanner, Japan). Using the above protocol, the system shows the classification accuracy of 94.95% and AUC of 0.95 using optimized feature combination. This is the first system of its kind for risk stratification as a screening tool to prevent excessive cost burden and better patients' cardiovascular disease management, while validating our two hypotheses

PCA-based polling strategy in machine learning framework for coronary artery disease risk assessment in intravascular ultrasound: A link between carotid and coronary grayscale plaque morphology

Background and objective: Percutaneous coronary interventional procedures need advance planning prior to stenting or an endarterectomy. Cardiologists use intravascular ultrasound (IVUS) for screening, risk assessment and stratification of coronary artery disease (CAD). We hypothesize that plaque components are vulnerable to rupture due to plaque progression. Currently, there are no standard grayscale IVUS tools for risk assessment of plaque rupture. This paper presents a novel strategy for risk stratification based on plaque morphology embedded with principal component analysis (PCA) for plaque feature dimensionality reduction and dominant feature selection technique. The risk assessment utilizes 56 grayscale coronary features in a machine learning framework while linking information from carotid and coronary plaque burdens due to their common genetic makeup. Method: This system consists of a machine learning paradigm which uses a support vector machine (SVM) combined with PCA for optimal and dominant coronary artery morphological feature extraction. Carotid artery proven intima-media thickness (cIMT) biomarker is adapted as a gold standard during the training phase of the machine learning system. For the performance evaluation, K-fold cross validation protocol is adapted with 20 trials per fold. For choosing the dominant features out of the 56 grayscale features, a polling strategy of PCA is adapted where the original value of the features is unaltered. Different protocols are designed for establishing the stability and reliability criteria of the coronary risk assessment system (cRAS). Results: Using the PCA-based machine learning paradigm and cross-validation protocol, a classification accuracy of 98.43% (AUC 0.98) with K = 10 folds using an SVM radial basis function (RBF) kernel was achieved. A reliability index of 97.32% and machine learning stability criteria of 5% were met for the cRAS. Conclusions: This is the first Computer aided design (CADx) system of its kind that is able to demonstrate the ability of coronary risk assessment and stratification while demonstrating a successful design of the machine learning system based on our assumptions

Five multiresolution-based calcium volume measurement techniques from coronary IVUS videos: A comparative approach

Background and objective Fast intravascular ultrasound (IVUS) video processing is required for calcium volume computation during the planning phase of percutaneous coronary interventional (PCI) procedures. Nonlinear multiresolution techniques are generally applied to improve the processing time by down-sampling the video frames. Methods This paper presents four different segmentation methods for calcium volume measurement, namely Threshold-based, Fuzzy c-Means (FCM), K-means, and Hidden Markov Random Field (HMRF) embedded with five different kinds of multiresolution techniques (bilinear, bicubic, wavelet, Lanczos, and Gaussian pyramid). This leads to 20 different kinds of combinations. IVUS image data sets consisting of 38,760 IVUS frames taken from 19 patients were collected using 40 MHz IVUS catheter (Atlantis® SR Pro, Boston Scientific®, pullback speed of 0.5 mm/sec.). The performance of these 20 systems is compared with and without multiresolution using the following metrics: (a) computational time; (b) calcium volume; (c) image quality degradation ratio; and (d) quality assessment ratio. Results Among the four segmentation methods embedded with five kinds of multiresolution techniques, FCM segmentation combined with wavelet-based multiresolution gave the best performance. FCM and wavelet experienced the highest percentage mean improvement in computational time of 77.15% and 74.07%, respectively. Wavelet interpolation experiences the highest mean precision-of-merit (PoM) of 94.06 ± 3.64% and 81.34 ± 16.29% as compared to other multiresolution techniques for volume level and frame level respectively. Wavelet multiresolution technique also experiences the highest Jaccard Index and Dice Similarity of 0.7 and 0.8, respectively. Multiresolution is a nonlinear operation which introduces bias and thus degrades the image. The proposed system also provides a bias correction approach to enrich the system, giving a better mean calcium volume similarity for all the multiresolution-based segmentation methods. After including the bias correction, bicubic interpolation gives the largest increase in mean calcium volume similarity of 4.13% compared to the rest of the multiresolution techniques. The system is automated and can be adapted in clinical settings. Conclusions We demonstrated the time improvement in calcium volume computation without compromising the quality of IVUS image. Among the 20 different combinations of multiresolution with calcium volume segmentation methods, the FCM embedded with wavelet-based multiresolution gave the best performance

Reliable and accurate calcium volume measurement in coronary artery using intravascular ultrasound videos

Quantitative assessment of calcified atherosclerotic volume within the coronary artery wall is vital for cardiac interventional procedures. The goal of this study is to automatically measure the calcium volume, given the borders of coronary vessel wall for all the frames of the intravascular ultrasound (IVUS) video. Three soft computing fuzzy classification techniques were adapted namely Fuzzy c-Means (FCM), K-means, and Hidden Markov Random Field (HMRF) for automated segmentation of calcium regions and volume computation. These methods were benchmarked against previously developed threshold-based method. IVUS image data sets (around 30,600 IVUS frames) from 15 patients were collected using 40 MHz IVUS catheter (Atlantis® SR Pro, Boston Scientific®, pullback speed of 0.5 mm/s). Calcium mean volume for FCM, K-means, HMRF and threshold-based method were 37.84 ± 17.38 mm3, 27.79 ± 10.94 mm3, 46.44 ± 19.13 mm3 and 35.92 ± 16.44 mm3 respectively. Cross-correlation, Jaccard Index and Dice Similarity were highest between FCM and threshold-based method: 0.99, 0.92 ± 0.02 and 0.95 + 0.02 respectively. Student’s t-test, z-test and Wilcoxon-test are also performed to demonstrate consistency, reliability and accuracy of the results. Given the vessel wall region, the system reliably and automatically measures the calcium volume in IVUS videos. Further, we validated our system against a trained expert using scoring: K-means showed the best performance with an accuracy of 92.80 %. Out procedure and protocol is along the line with method previously published clinically