Automatic detection of the intima-media thickness in ultrasound images of the common carotid artery using neural networks

Quantifying Carotid Stenosis: History, Current Applications, Limitations, and Potential: How Imaging Is Changing the Scenario

Carotid artery stenosis is a major cause of morbidity and mortality. The journey to understanding carotid disease has developed over time and radiology has a pivotal role in diagnosis, risk stratification and therapeutic management. This paper reviews the history of diagnostic imaging in carotid disease, its evolution towards its current applications in the clinical and research fields, and the potential of new technologies to aid clinicians in identifying the disease and tailoring medical and surgical treatment.

Development of a Three-Dimensional Carotid Ultrasound Image Segmentation Workflow for Improved Efficiency, Reproducibility and Accuracy in Measuring Vessel Wall and Plaque Volume and Thickness

Automated segmentation of carotid lumen-intima boundary (LIB) and media-adventitia boundary (MAB) by deep convolutional neural networks (CNN) from three-dimensional ultrasound (3DUS) images has made assessment and monitoring of carotid atherosclerosis more efficient than manual segmentation. However, training of CNN still requires manual segmentation of LIB and MAB. Therefore, there is a need to improve the efficiency of manual segmentation and develop strategies to improve segmentation accuracy by the CNN for serial monitoring of carotid atherosclerosis. One strategy to reduce segmentation time is to increase the interslice distance (ISD) between segmented axial slices of a 3DUS image while maintaining the segmentation reliability. We, for the first time, investigated the effect of ISD on the reproducibility of MAB and LIB segmentations. The intra-observer reproducibility of LIB and MAB segmentations at ISDs of 1 mm and 2 mm was not statistically significantly different, whereas the reproducibility at ISD = 3 mm was statistically lower. Therefore, we conclude that segmentation with an ISD of 2 mm provides sufficient reliability for CNN training. We further proposed training the CNN by the baseline images of the entire cohort of patients for automatic segmentation of the follow-up images acquired for the same cohort. We validated that segmentation with this time-based partitioning approach is more accurate than that produced by patient-based partitioning, especially at the carotid bifurcation. This study forms the basis for an efficient, reproducible, and accurate 3DUS workflow for serial monitoring of carotid atherosclerosis useful in risk stratification of cardiovascular events and in evaluating the efficacy of new treatments.

A Dual-Stream Centerline-Guided Network for Segmentation of the Common and Internal Carotid Arteries From 3D Ultrasound Images

Segmentation of the carotid section encompassing the common carotid artery (CCA), the bifurcation and the internal carotid artery (ICA) from three-dimensional ultrasound (3DUS) is required to measure the vessel wall volume (VWV) and localized vessel-wall-plus-plaque thickness (VWT), shown to be sensitive to treatment effect. We proposed an approach to combine a centerline extraction network (CHG-Net) and a dual-stream centerline-guided network (DSCG-Net) to segment the lumen-intima (LIB) and media-adventitia boundaries (MAB) from 3DUS images. Correct arterial location is essential for successful segmentation of the carotid section encompassing the bifurcation. We addressed this challenge by using the arterial centerline to enhance the localization accuracy of the segmentation network. The CHG-Net was developed to generate a heatmap indicating high probability regions for the centerline location, which was then integrated with the 3DUS image by the DSCG-Net to generate the MAB and LIB. The DSCG-Net includes a scale-based and a spatial attention mechanism to fuse multi-level features extracted by the encoder, and a centerline heatmap reconstruction side-branch connected to the end of the encoder to increase the generalization ability of the network. Experiments involving 224 3DUS volumes produce a Dice similarity coefficient (DSC) of 95.8±1.9% and 92.3±5.4% for CCA MAB and LIB, respectively, and 93.2±4.4% and 89.0±10.0% for ICA MAB and LIB, respectively. Our approach outperformed four state-of-the-art 3D CNN models, even after their performances were boosted by centerline guidance. The efficiency afforded by the framework would allow it to be incorporated into the clinical workflow for improved quantification of plaque change.

Carotid Vessel-Wall-Volume Ultrasound Measurement via a UNet++ Ensemble Algorithm Trained on Small Data Sets

Vessel wall volume (VWV) is a 3-D ultrasound measurement for the assessment of therapy in patients with carotid atherosclerosis. Deep learning can be used to segment the media-adventitia boundary (MAB) and lumen-intima boundary (LIB) and to quantify VWV automatically; however, it typically requires large training data sets with expert manual segmentation, which are difficult to obtain. In this study, a UNet++ ensemble approach was developed for automated VWV measurement, trained on five small data sets (n = 30 participants) and tested on 100 participants with clinically diagnosed coronary artery disease enrolled in a multicenter CAIN trial. The Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), Pearson correlation coefficient (r), Bland-Altman plots and coefficient of variation (CoV) were used to evaluate algorithm segmentation accuracy, agreement and reproducibility. The UNet++ ensemble yielded DSCs of 91.07%-91.56% and 87.53%-89.44% and ASSDs of 0.10-0.11 mm and 0.33-0.39 mm for the MAB and LIB, respectively; the algorithm VWV measurements were correlated (r = 0.763-0.795, p < 0.001) with manual segmentations, and the CoV for VWV was 8.89%. In addition, the UNet++ ensemble trained on 30 participants achieved a performance similar to that of U-Net and Voxel-FCN trained on 150 participants. These results suggest that our approach could provide accurate and reproducible carotid VWV measurements using relatively small training data sets, supporting deep learning applications for monitoring atherosclerosis progression in research and clinical trials.

NAG-Net: Nested attention-guided learning for segmentation of carotid lumen-intima interface and media-adventitia interface

Cardiovascular diseases (CVD), as the leading cause of death in the world, poses a serious threat to human health. The segmentation of carotid Lumen-intima interface (LII) and Media-adventitia interface (MAI) is a prerequisite for measuring intima-media thickness (IMT), which is of great significance for early screening and prevention of CVD. Despite recent advances, existing methods still fail to incorporate task-related clinical domain knowledge and require complex post-processing steps to obtain fine contours of LII and MAI. In this paper, a nested attention-guided deep learning model (named NAG-Net) is proposed for accurate segmentation of LII and MAI. The NAG-Net consists of two nested sub-networks, the Intima-Media Region Segmentation Network (IMRSN) and the LII and MAI Segmentation Network (LII-MAISN). It innovatively incorporates task-related clinical domain knowledge through the visual attention map generated by IMRSN, enabling LII-MAISN to focus more on the clinician's visual focus region under the same task during segmentation. Moreover, the segmentation results can directly obtain fine contours of LII and MAI through simple refinement without complicated post-processing steps. To further improve the feature extraction ability of the model and reduce the impact of data scarcity, the strategy of transfer learning is also adopted to apply the pretrained weights of VGG-16. In addition, a channel attention-based encoder feature fusion block (EFFB-ATT) is specially designed to achieve efficient representation of useful features extracted by two parallel encoders in LII-MAISN. Extensive experimental results have demonstrated that our proposed NAG-Net outperformed other state-of-the-art methods and achieved the highest performance on all evaluation metrics.

Ultrasound carotid plaque segmentation via image reconstruction-based self-supervised learning with limited training labels

Carotid total plaque area (TPA) is an important contributing measurement to the evaluation of stroke risk. Deep learning provides an efficient method for ultrasound carotid plaque segmentation and TPA quantification. However, high performance of deep learning requires datasets with many labeled images for training, which is very labor-intensive. Thus, we propose an image reconstruction-based self-supervised learning algorithm (IR-SSL) for carotid plaque segmentation when few labeled images are available. IR-SSL consists of pre-trained and downstream segmentation tasks. The pre-trained task learns region-wise representations with local consistency by reconstructing plaque images from randomly partitioned and disordered images. The pre-trained model is then transferred to the segmentation network as the initial parameters in the downstream task. IR-SSL was implemented with two networks, UNet++ and U-Net, and evaluated on two independent datasets of 510 carotid ultrasound images from 144 subjects at SPARC (London, Canada) and 638 images from 479 subjects at Zhongnan hospital (Wuhan, China). Compared to the baseline networks, IR-SSL improved the segmentation performance when trained on few labeled images (n = 10, 30, 50 and 100 subjects). For 44 SPARC subjects, IR-SSL yielded Dice-similarity-coefficients (DSC) of 80.14-88.84%, and algorithm TPAs were strongly correlated (r=0.962-0.993, p < 0.001) with manual results. The models trained on the SPARC images but applied to the Zhongnan dataset without retraining achieved DSCs of 80.61-88.18% and strong correlation with manual segmentation (r=0.852-0.978, p < 0.001). These results suggest that IR-SSL could improve deep learning when trained on small labeled datasets, making it useful for monitoring carotid plaque progression/regression in clinical use and trials.

Learning-based initialization for correntropy-based level sets to segment atherosclerotic plaque in ultrasound images

Carotid artery atherosclerosis is a significant cause of stroke. Ultrasound imaging has been widely used in the diagnosis of atherosclerosis. Therefore, segmenting the atherosclerotic carotid plaque in an ultrasound image is an important task. Accurate plaque segmentation is helpful for the measurement of carotid plaque burden. This study proposes an automatic method for atherosclerotic plaque segmentation by using correntropy-based level sets (CLS) with learning-based initialization. We introduce the CLS model, containing the point-based local bias-field corrected image fitting method and correntropy-based distance measurement, to overcome the limitations of the ultrasound images. A supervised learning algorithm is employed to solve the automatic initialization problem of the variational methods. The proposed atherosclerotic plaque segmentation method is validated on 29 carotid ultrasound images, obtaining a Dice ratio of 90.6 ± 1.9% and an overlap index of 83.6 ± 3.2%. Moreover, by comparing the standard deviation of each evaluation index, it can be found that the proposed method is more robust for segmenting the atherosclerotic plaque. Our work shows that our proposed method can be more helpful than other variational models for measuring the carotid plaque burden.

HRU-Net: A Transfer Learning Method for Carotid Artery Plaque Segmentation in Ultrasound Images

Carotid artery stenotic plaque segmentation in ultrasound images is a crucial means for the analysis of plaque components and vulnerability. However, segmentation of severe stenotic plaques remains a challenging task because of the heterogeneities of inter-plaques and intra-plaques, and obscure boundaries of plaques. In this paper, we propose an automated HRU-Net transfer learning method for segmenting carotid plaques, using the limited images. The HRU-Net is based on the U-Net encoder−decoder paradigm, and cross-domain knowledge is transferred for plaque segmentation by fine-tuning the pretrained ResNet-50. Moreover, a cropped-blood-vessel image augmentation is customized for the plaque position constraint during training only. Moreover, hybrid atrous convolutions (HACs) are designed to derive diverse long-range dependences for refined plaque segmentation that are used on high-level semantic layers to exploit the implicit discrimination features. The experiments are performed on 115 images; Firstly, the 10-fold cross-validation, using 40 images with severe stenosis plaques, shows that the proposed method outperforms some of the state-of-the-art CNN-based methods on Dice, IoU, Acc, and modified Hausdorff distance (MHD) metrics; the improvements on metrics of Dice and MHD are statistically significant (p < 0.05). Furthermore, our HRU-Net transfer learning method shows fine generalization performance on 75 new images with varying degrees of plaque stenosis, and it may be used as an alternative for automatic noisy plaque segmentation in carotid ultrasound images clinically.

Measurement of Intima-Media Thickness Depending on Intima Media Complex Segmentation by Deep Neural Networks

Intima Media Thickness (IMT) of the carotid artery is an important marker indicating the sign of cardiovascular disease. Automated measurement of IMT requires segmentation of intima media complex (IMC).Traditional methods which use shape, color and texture for classification have poor generalization capability. This paper proposes two models- the pipeline model and the end-to-end model using Convolutional Neural Networks (CNN) and auto encoder–decoder network respectively. CNN architecture is implemented and tested by varying the number of convolutional layer, size of the kernel as well as the number of kernels. Auto encoder–decoder performs pixel wise classification using two interconnected pathways for identifying the boundary of lumen-intima (LI) and media adventitia (MA). This helps in reconstruction of the segmented portion for measurement of IMT. Both methods are tested using a dataset of 550 subjects. The results clearly indicate that end-to-end model has an edge over the pipeline model exhibiting lesser deviation between the automated measurement and the measurement made by the radiologist. The pipeline model however has better segmentation accuracy when the size of the image used for training is small. The convolutional neural network with auto encoder–decoder proves robust through sparse representation, and faster learning with better generalization. Also, the experimental setup is analyzed by interconnecting Tensor flow simulated result with Raspberry PI and the outcomes are analyzed.

Segmentation of common and internal carotid arteries from 3D ultrasound images based on adaptive triple loss

PURPOSE

Vessel wall volume (VWV) and localized vessel-wall-plus-plaque thickness (VWT) measured from three-dimensional (3D) ultrasound (US) carotid images are sensitive to anti-atherosclerotic effects of medical/dietary treatments. VWV and VWT measurements require the lumen-intima (LIB) and media-adventitia boundaries (MAB) at the common and internal carotid arteries (CCA and ICA). However, most existing segmentation techniques were capable of segmenting the CCA only. An approach capable of segmenting the MAB and LIB from the CCA and ICA was required to accelerate VWV and VWT quantification.

METHODS

Segmentation for CCA and ICA was performed independently using the proposed two-channel U-Net, which was driven by a novel loss function known as the adaptive triple Dice loss (ADTL) function. The training set was augmented by interpolating manual segmentation along the longitudinal direction, thereby taking continuity of the artery into account. A test-time augmentation (TTA) approach was applied, in which segmentation was performed three times based on the input axial images and its flipped versions; the final segmentation was generated by pixel-wise majority voting.

RESULTS

Experiments involving 224 3DUS volumes produce a Dice similarity coefficient (DSC) of 95.1% ± 4.1% and 91.6% ± 6.6% for the MAB and LIB, in the CCA, respectively, and 94.2% ± 3.3% and 89.0% ± 8.1% for the MAB and LIB, in the ICA, respectively. TTA and ATDL independently contributed to a statistically significant improvement to all boundaries except the LIB in ICA.

CONCLUSIONS

The proposed two-channel U-Net with ADTL and TTA can segment the CCA and ICA accurately and efficiently from the 3DUS volume. Our approach has the potential to accelerate the transition of 3DUS measurements of carotid atherosclerosis to clinical research.

TEE image quality improvement with our devised probe cover

OBJECTIVE(S)

Our hypothesis was that our devised transesophageal echocardiography probe cover with the capacity for pinpoint suction would improve image quality.

DESIGN

Prospective cohort study.

SETTING

Single tertiary medical center.

PARTICIPANTS

Patients undergoing surgery requiring intraoperative transesophageal echocardiography.

INTERVENTIONS

Suctioning with inserted orogastric tube.

MEASUREMENTS AND MAIN RESULTS

Changes in image quality with suctioning were assessed by 2 methods. In method #1, investigators categorized the quality of all acquired images on a numeric scale based on each investigator's impression (1: very poor, 2: poor, 3: acceptable, 4: good, and 5: very good). In method #2, the reproducibility of the left ventricular fraction area change (LV FAC) was assessed, assuming that improved transgastric midpapillary short-axis view image quality would yield better LV FAC reproducibility. With method #1, for midesophageal views, 26.5%, 70.5%, and 3.0% of images showed improved, the same, and worsened image quality, respectively. For transgastric views, 55.3%, 43.3%, and 1.4% showed improved, the same, and worsened image quality, respectively. For deep transgastric views, 60.0%, 38.0%, and 2.0% showed improved, the same, and worsened image quality, respectively. With method #2, the presuction group had an ICC of 0.942 (95% CI: 0.91, 0.965). The postsuction group had an ICC of 0.988 (95% CI: 0.981, 0.993).

CONCLUSIONS

Our investigation validates the potential image quality improvement withour devised TEE probe cover. However, its clinical validity needs to be confirmed by further studies.

A Review on Joint Carotid Intima-Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework

Cardiovascular diseases (CVDs) are the top ten leading causes of death worldwide. Atherosclerosis disease in the arteries is the main cause of the CVD, leading to myocardial infarction and stroke. The two primary image-based phenotypes used for monitoring the atherosclerosis burden is carotid intima-media thickness (cIMT) and plaque area (PA). Earlier segmentation and measurement methods were based on ad hoc conventional and semi-automated digital imaging solutions, which are unreliable, tedious, slow, and not robust. This study reviews the modern and automated methods such as artificial intelligence (AI)-based. Machine learning (ML) and deep learning (DL) can provide automated techniques in the detection and measurement of cIMT and PA from carotid vascular images. Both ML and DL techniques are examples of supervised learning, i.e., learn from "ground truth" images and transformation of test images that are not part of the training. This review summarizes (1) the evolution and impact of the fast-changing AI technology on cIMT/PA measurement, (2) the mathematical representations of ML/DL methods, and (3) segmentation approaches for cIMT/PA regions in carotid scans based for (a) region-of-interest detection and (b) lumen-intima and media-adventitia interface detection using ML/DL frameworks. AI-based methods for cIMT/PA segmentation have emerged for CVD/stroke risk monitoring and may expand to the recommended parameters for atherosclerosis assessment by carotid ultrasound.

Estimating Arterial Wall Deformations from Automatic Key-Point Detection and Matching

Assessing arterial-wall motion and deformations may reveal pathologic alterations in biomechanical properties of the parietal tissues and, thus, contribute to the detection of vascular disease onset. Ultrasound image sequences allow the observation of this motion and many methods have been developed to estimate temporal changes in artery diameter and wall thickness and to track 2-D displacements of selected points. Some methods enable the assessment of shearing or stretching within the wall, but none of them can estimate all these deformations simultaneously. The method herein proposed was devised to simultaneously estimate translation, compression, stretching and shearing of the arterial wall in ultrasound B-mode image sequences representing the carotid artery longitudinal section. Salient blob-like patterns, called key points, are automatically detected in each frame and matched between successive frames. A robust estimator based on an affine transformation model is then used to assess frame-to-frame motion explaining at best the key-point matches and to reject outliers. Realistic simulated image sequences were used to evaluate the accuracy and robustness of the method against ground truth. The method was also visually assessed on clinical image sequences, for which true deformations are unknown.

APRIL: Anatomical prior-guided reinforcement learning for accurate carotid lumen diameter and intima-media thickness measurement

Carotid artery lumen diameter (CALD) and carotid artery intima-media thickness (CIMT) are essential factors for estimating the risk of many cardiovascular diseases. The automatic measurement of them in ultrasound (US) images is an efficient assisting diagnostic procedure. Despite the advances, existing methods still suffer the issue of low measuring accuracy and poor prediction stability, mainly due to the following disadvantages: (1) ignore anatomical prior and prone to give anatomically inaccurate estimation; (2) require carefully designed post-processing, which may introduce more estimation errors; (3) rely on massive pixel-wise annotations during training; (4) can not estimate the uncertainty of the predictions. In this study, we propose the Anatomical Prior-guided ReInforcement Learning model (APRIL), which innovatively formulate the measurement of CALD & CIMT as an RL problem and dynamically incorporate anatomical prior (AP) into the system through a novel reward. With the guidance of AP, the designed keypoints in APRIL can avoid various anatomy impossible mis-locations, and accurately measure the CALD & CIMT based on their corresponding locations. Moreover, this formulation significantly reduces human annotation effort by only using several keypoints and can help to eliminate the extra post-processing steps. Further, we introduce an uncertainty module for measuring the prediction variance, which can guide us to adaptively rectify the estimation of those frames with considerable uncertainty. Experiments on a challenging carotid US dataset show that APRIL can achieve MAE (in pixel/mm) of 3.02±2.23 / 0.18±0.13 for CALD, and 0.96±0.70 / 0.06±0.04 for CIMT, which significantly surpass popular approaches that use more annotations.

Fully Automatic Measurement of Intima-Media Thickness in Ultrasound Images of the Common Carotid Artery Based on Improved Otsu's Method and Adaptive Wind Driven Optimization

The intima media thickness (IMT) of the common carotid artery (CCA) can be used to predict the risk of atherosclerosis. Many image segmentation techniques have been used for IMT measurement. However, severe noise in the ultrasound image can lead to erroneous segmentation results. To improve the robustness to noise, a fully automatic method, based on an improved Otsu's method and an adaptive wind-driven optimization technique, is proposed for estimating the IMT (denoted as "improved Otsu-AWDO"). First, an advanced despeckling filter, i.e., " Nagare's filter" is used to address the speckle noise in the carotid ultrasound images. Next, an improved fuzzy contrast method (IFC) is used to enhance the region of the intima media complex (IMC) in the blurred filtered images. Then, a new method is used for automatic extraction of the region of interest (ROI). Finally, the lumen intima interface and media adventitia interface are segmented from the IMC using improved Otsu-AWDO. Then, 156 B-mode longitudinal carotid ultrasound images of six different datasets are used to evaluate the performance of the automatic measurements. The results indicate that the absolute error of proposed method is only 10.1 ± 9.6 (mean ± std in μm). Moreover, the proposed method has a correlation coefficient as high as 0.9922, and a bias as low as 0.0007. From comparison with previous methods, we can conclude that the proposed method has strong robustness and can provide accurate IMT estimations.

Segmentation of the Common Carotid Intima-Media Complex in Ultrasound Images Using 2-D Continuous Max-Flow and Stacked Sparse Auto-encoder

The intima-media thickness (IMT) of a common carotid artery in an ultrasound image is considered an important indicator of the onset of atherosclerosis. However, it is challenging to segment the intima-media complex (IMC) directly in ultrasound images. This study proposes a fully automatic method to segment the IMC on longitudinal B-mode ultrasound images. Our method consists of two stages: (i) extraction of the region of interest with a continuous max-flow algorithm and region-of-interest reconstruction using a stacked sparse auto-encoder model, and (ii) IMC segmentation using a trained random forest classifier. The proposed method has been tested on three databases from three different imaging centres, comprising a total of 228 ultrasound images of the common carotid artery. On the three databases, our method yields mean absolute errors of 0.028 ± 0.016 mm, 0.579 ± 0.288 pixel and 0.582 ± 0.341 pixel; polyline distance (PD) measures of 0.026 ± 0.017 mm, 0.657 ± 0.275 pixel and 0.731 ± 0:282 pixel; Hausdorff distance measures of 0.249 ± 0.101 mm, 4.760 ± 1.085 pixels and 5.825 ± 2.059 pixels; and correlation coefficients of 95.19%, 93.79%, and 98.96%, respectively. These results indicate that the proposed method performs well in segmentation of the IMC and measurement of the IMT.

Obtaining the sGAG distribution profile in articular cartilage color images

The articular cartilage tissue is an essential component of joints as it reduces the friction between the two bones. Its load-bearing properties depend mostly on proteoglycan distribution, which can be analyzed through the study of the presence of sulfated glycosaminoglycan (sGAG). Currently, sGAG distribution in articular cartilage is not completely known; it is calculated by means of laboratory tests that imply the inherent inaccuracy of a manual procedure. This paper presents an easy-to-use desktop software application for obtaining the sGAG distribution profile in tissue. This app uses color images of stained cartilage tissues taken under a microscope, so researchers at the Trinity Centre for Bioengineering (Dublin, Ireland) can understand the qualitative distribution of sGAG with depth in the studied tissues.

Effects of high-intensity interval training on vascular endothelial function and vascular wall thickness in breast cancer patients receiving anthracycline-based chemotherapy: a randomized pilot study

PURPOSE

The purpose of this study was to determine the effects of an 8-week high-intensity interval training (HIIT) intervention on vascular endothelial function, measured as brachial artery flow-mediated dilation (baFMD), and vascular wall thickness measured by carotid intima media thickness (cIMT) in breast cancer patients undergoing anthracycline-based chemotherapy.

METHODS

Thirty women were randomized to either HIIT or non-exercise control groups (CON). The HIIT group participated in an 8-week HIIT intervention occurring three times per week on a cycle ergometer. The CON group was offered the HIIT intervention after 8 weeks. baFMD was measured from the brachial artery diameter at baseline (D0) and 1 min after cuff deflation (D1); percent change was calculated by measuring brachial artery diameter after cuff deflation relative to the baseline [baFMD = (D1 - D0)/D0 × 100]. The cIMT was obtained from the posterior wall of common carotid artery 10 mm below the carotid bulb. Paired t test and repeated measures ANCOVA were performed to assess changes in baFMD and cIMT.

RESULTS

At baseline, the HIIT (n = 15) and CON (n = 15) groups did not differ by age (46.9 ± 9.8 years), BMI (31.0 ± 7.5 kg/m2), and blood pressure (123.4 ± 16.8/72.3.9 ± 5.6 mmHg). Post-exercise, baFMD significantly increased [4.3; 95% confidence interval (CI): (1.5, 7.0), p = 0.005] in HIIT versus CON group. cIMT did not significantly change [0.003, 95% CI - 0.004, 0.009), p = 0.40] in HIIT group, while IMT significantly increased from baseline to post-intervention (0.009, 95% CI 0.004, 0.010, p = 0.003) in CON group.

CONCLUSION

This study may suggest that HIIT improved vascular endothelial function and maintained wall thickness in breast cancer patients undergoing anthracycline-based chemotherapy.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT02454777.

Robust spatial fuzzy GMM based MRI segmentation and carotid artery plaque detection in ultrasound images

BACKGROUND AND OBJECTIVE

In medical image analysis for disease diagnosis, segmentation is one of the challenging tasks. Owing to the inherited degradations in MRI improper segments are produced. Segmentation process is an important step in brain tissue analysis. Moreover, an early detection of plaque in carotid artery using ultrasound images may prevent serious brain strokes. Unfortunately, low quality and noisy ultrasound images are still challenges for accurate segmentation. The objective of this research is to develop a robust segmentation approach for medical images such as brain MRI and carotid artery ultrasound images.

METHODS

In this paper, a novel approach is proposed to address the segmentation challenges of medical images. The proposed approach employed fuzzy intelligence and Gaussian mixture model (GMM). It comprises two phases; firstly, incorporating spatial fuzzy c-means in GMM by exploiting statistical, texture, and wavelet image features. During model development, GMM parameters are estimated in presence of noise by EM algorithm iteratively. Utilizing these parameters, brain MRI images are segmented. In next phase, developed approach is applied to solve a real problem of carotid artery plaque detection using ultrasound images. The dataset of real patients annotated by radiologists has been obtained from Radiology Department, Shifa International Hospital Islamabad, Pakistan. For this, intima-media-thickness values are computed from the proposed segmentation followed by support vector machines for plaque classification (normal/abnormal).

RESULTS

The obtained segmentation has been evaluated on standard brain MRI dataset and offers high segmentation accuracy of 99.2%. The proposed approach outperforms in term of segmentation performance range of 3-9% as compared to the state of the art approaches on brain MRI. Furthermore, the proposed approach shows robustness to various levels of Gaussian and Rician image noises. On carotid artery dataset, we have obtained high plaque detection rate in terms of accuracy, sensitivity, specificity, and F-score values of 98.8%, 99.3%, 98.0%, and 97.5% respectively.

CONCLUSIONS

The proposed approach segments both modalities with high precision and shows robustness at Gaussian and Rician noise levels. Results for brain MRI and ultrasound images indicate its effectiveness and can be used as second opinion in addition to the radiologists. The developed approach is straightforward, efficient, and reproducible. It may benefit to improve the clinical evaluation of the disease in both asymptomatic and symptomatic individuals.

Effects of high-intensity interval training on vascular function in breast cancer survivors undergoing anthracycline chemotherapy: design of a pilot study

INTRODUCTION

Cardiovascular disease (CVD) mortality is higher among breast cancer survivors (BCS) who receive chemotherapy compared with those not receiving chemotherapy. Anthracycline chemotherapy is of particular concern due to anthracycline-related impairment of vascular endothelial cells and dysregulation of the extracellular matrix. One strategy proven to offset these impairments is a form of exercise known as high-intensity interval training (HIIT). HIIT improves endothelial function in non-cancer populations by decreasing oxidative stress, the main contributor to anthracycline-induced vascular dysfunction. The purpose of this pilot study is to assess the feasibility of an 8-week HIIT, as well as the HIIT effects on endothelial function and extracellular matrix remodelling, in BCS undergoing anthracycline chemotherapy.

METHODS AND ANALYSIS

Thirty BCS are randomised to either HIIT, an 8-week HIIT intervention occurring three times per week (seven alternating bouts of 90% of peak power output followed by 10% peak power output), or delayed group (DEL). Feasibility of HIIT is assessed by (1) the percentage of completed exercise sessions and (2) the number of minutes of exercise completed over the course of the study. Vascular function is assessed using brachial artery flow-mediated dilation and carotid intima media thickness. Extracellular matrix remodelling is assessed by the level of matrix metalloproteinases in the plasma. A repeated-measures analysis of covariance model will be performed with group (HIIT and DEL group) and time (pre/post assessment) as independent factors. We hypothesise that HIIT will be feasible in BCS undergoing anthracycline chemotherapy, and that HIIT will improve endothelial function and extracellular matrix remodelling, compared with the DEL group. Success of this study will provide evidence of feasibility and efficacy to support a larger definitive trial which will impact cancer survivorship by decreasing anthracycline-induced vascular dysfunction, thereby benefiting cardiovascular markers that are related to CVD risk.

ETHICS AND DISSEMINATION

This trial was approved by the University of Southern California Institutional Review Board (HS-15-00227).

TRIAL REGISTRATION NUMBER

NCT02454777; Pre-results.

An integrated method for atherosclerotic carotid plaque segmentation in ultrasound image

BACKGROUND AND OBJECTIVE

Carotid artery atherosclerosis is an important cause of stroke. Ultrasound imaging has been widely used in the diagnosis of atherosclerosis. Therefore, segmenting atherosclerotic carotid plaque in ultrasound image is an important task. Accurate plaque segmentation is helpful for the measurement of carotid plaque burden. In this paper, we propose and evaluate a novel learning-based integrated framework for plaque segmentation.

METHODS

In our study, four different classification algorithms, along with the auto-context iterative algorithm, were employed to effectively integrate features from ultrasound images and later also the iteratively estimated and refined probability maps together for pixel-wise classification. The four classification algorithms were support vector machine with linear kernel, support vector machine with radial basis function kernel, AdaBoost and random forest. The plaque segmentation was implemented in the generated probability map. The performance of the four different learning-based plaque segmentation methods was tested on 29 B-mode ultrasound images. The evaluation indices for our proposed methods were consisted of sensitivity, specificity, Dice similarity coefficient, overlap index, error of area, absolute error of area, point-to-point distance, and Hausdorff point-to-point distance, along with the area under the ROC curve.

RESULTS

The segmentation method integrated the random forest and an auto-context model obtained the best results (sensitivity 80.4 ± 8.4%, specificity 96.5 ± 2.0%, Dice similarity coefficient 81.0 ± 4.1%, overlap index 68.3 ± 5.8%, error of area -1.02 ± 18.3%, absolute error of area 14.7 ± 10.9%, point-to-point distance 0.34 ± 0.10 mm, Hausdorff point-to-point distance 1.75 ± 1.02 mm, and area under the ROC curve 0.897), which were almost the best, compared with that from the existed methods.

CONCLUSIONS

Our proposed learning-based integrated framework investigated in this study could be useful for atherosclerotic carotid plaque segmentation, which will be helpful for the measurement of carotid plaque burden.

A Review on Atherosclerotic Biology, Wall Stiffness, Physics of Elasticity, and Its Ultrasound-Based Measurement

Functional and structural changes in the common carotid artery are biomarkers for cardiovascular risk. Current methods for measuring functional changes include pulse wave velocity, compliance, distensibility, strain, stress, stiffness, and elasticity derived from arterial waveforms. The review is focused on the ultrasound-based carotid artery elasticity and stiffness measurements covering the physics of elasticity and linking it to biological evolution of arterial stiffness. The paper also presents evolution of plaque with a focus on the pathophysiologic cascade leading to arterial hardening. Using the concept of strain, and image-based elasticity, the paper then reviews the lumen diameter and carotid intima-media thickness measurements in combined temporal and spatial domains. Finally, the review presents the factors which influence the understanding of atherosclerotic disease formation and cardiovascular risk including arterial stiffness, tissue morphological characteristics, and image-based elasticity measurement.

Automated detection of coarctation of aorta in neonates from two-dimensional echocardiograms

Coarctation of aorta (CoA) is a critical congenital heart defect (CCHD) that requires accurate and immediate diagnosis and treatment. Current newborn screening methods to detect CoA lack both in sensitivity and specificity, and when suspected in a newborn, it must be confirmed using specialized imaging and expert diagnosis, both of which are usually unavailable at tertiary birthing centers. We explore the feasibility of applying machine learning methods to reliably determine the presence of this difficult-to-diagnose cardiac abnormality from ultrasound image data. We propose a framework that uses deep learning-based machine learning methods for fully automated detection of CoA from two-dimensional ultrasound clinical data acquired in the parasternal long axis view, the apical four chamber view, and the suprasternal notch view. On a validation set consisting of 26 CoA and 64 normal patients our algorithm achieved a total error rate of 12.9% (11.5% false-negative error and 13.6% false-positive error) when combining decisions of classifiers over three standard echocardiographic view planes. This compares favorably with published results that combine clinical assessments with pulse oximetry to detect CoA (71% sensitivity).

A Fully-Automatic Method to Segment the Carotid Artery Layers in Ultrasound Imaging: Application to Quantify the Compression-Decompression Pattern of the Intima-Media Complex During the Cardiac Cycle

The aim of this study was to introduce and evaluate a contour segmentation method to extract the interfaces of the intima-media complex in carotid B-mode ultrasound images. The method was applied to assess the temporal variation of intima-media thickness during the cardiac cycle. The main methodological contribution of the proposed approach is the introduction of an augmented dimension to process 2-D images in a 3-D space. The third dimension, which is added to the two spatial dimensions of the image, corresponds to the tentative local thickness of the intima-media complex. The method is based on a dynamic programming scheme that runs in a 3-D space generated with a shape-adapted filter bank. The optimal solution corresponds to a single medial axis representation that fully describes the two anatomical interfaces of the arterial wall. The method is fully automatic and does not require any input from the user. The method was trained on 60 subjects and validated on 184 other subjects from six different cohorts and four different medical centers. The arterial wall was successfully segmented in all analyzed images (average pixel size = 57 ± 20 mm), with average segmentation errors of 47 ± 70 mm for the lumen-intima interface, 55 ± 68 mm for the media-adventitia interface and 66 ± 90 mm for the intima-media thickness. The amplitude of the temporal variations in IMT during the cardiac cycle was significantly higher in the diseased population than in healthy volunteers (106 ± 48 vs. 86 ± 34 mm, p = 0.001). The introduced framework is a promising approach to investigate an emerging functional parameter of the arterial wall by assessing the cyclic compression-decompression pattern of the tissues.

Ultrasound intima-media thickness measurement of the carotid artery using ant colony optimization combined with a curvelet-based orientation-selective filter

PURPOSE

Automatic measurement of the intima-media thickness (IMT) from ultrasound carotid images is an important task in clinical diagnosis. Many computer-based techniques for IMT measurement have been proposed to overcome the limits of manual segmentation. However, the robustness of the algorithms would be influenced by the inherent speckle noise of ultrasound image. This paper proposed a curvelet guided ant colony optimization (CGACO) strategy that could achieve satisfied accuracy for IMT measurement with improved robustness to noise.

METHODS

The curvelet-based orientation-selective (CBOS) filter was first introduced for speckle removal and edge enhancement. Different from conventional methods, CBOS filter processes the curvelet coefficients by orientations rather than by magnitude. Then, a specially designed two-leg ant colony optimization technique, combined with Otsu thresholding and Sobel edge detector, was proposed as a novel segmentation method to extract the media-adventitia (MA) and the lumen-intima (LI) boundaries. Finally, a coupled snake model was employed to further smooth the contours of MA and LI.

RESULTS

In addition to 224 carotid artery images acquired from 34 participants, simulated speckled images with nine levels of noise were also included in the database. The mean absolute distance errors of CGACO for LI interface tracings, MA interface tracings, and IMT measurements were 0.030 ± 0.027, 0.039 ± 0.036, and 0.041 ± 0.036 mm, respectively. Besides, CGACO had a correlation coefficient as high as 0.992 and a bias as low as -0.008. All these measures were comparable to or better than a previous technique and the manual segmentation. On the other hand, CGACO had the highest success rate of 98.7% in the segmentation of real data. It also maintained a much higher success rate in the segmentation of simulated images with different levels of speckle noise.

CONCLUSIONS

The proposed technique showed accurate IMT measurement results. Furthermore, benefiting from the CBOS filter, the robustness to noise of the algorithm was substantially improved. Therefore, CGACO could provide a reliable way to segment the carotid artery from ultrasound images and could be used in clinical practice of IMT measurement, particularly in early atherosclerotic stages.

An improved approach for accurate and efficient measurement of common carotid artery intima-media thickness in ultrasound images

The intima-media thickness (IMT) of common carotid artery (CCA) can serve as an important indicator for the assessment of cardiovascular diseases (CVDs). In this paper an improved approach for automatic IMT measurement with low complexity and high accuracy is presented. 100 ultrasound images from 100 patients were tested with the proposed approach. The ground truth (GT) of the IMT was manually measured for six times and averaged, while the automatic segmented (AS) IMT was computed by the algorithm proposed in this paper. The mean difference ± standard deviation between AS and GT IMT is 0.0231 ± 0.0348 mm, and the correlation coefficient between them is 0.9629. The computational time is 0.3223 s per image with MATLAB under Windows XP on an Intel Core 2 Duo CPU E7500 @2.93 GHz. The proposed algorithm has the potential to achieve real-time measurement under Visual Studio.