Fully automatic plaque segmentation in 3-D carotid ultrasound images

State-of-the-art CT and MR imaging and assessment of atherosclerotic carotid artery disease: standardization of scanning protocols and measurements-a consensus document by the European Society of Cardiovascular Radiology (ESCR)

The European Society of Cardiovascular Radiology (ESCR) is the European specialist society of cardiac and vascular imaging. This society's highest priority is the continuous improvement, development, and standardization of education, training, and best medical practice, based on experience and evidence. The present intra-society consensus is based on the existing scientific evidence and on the individual experience of the members of the ESCR writing group on carotid diseases, the members of the ESCR guidelines committee, and the members of the executive committee of the ESCR. The recommendations published herein reflect the evidence-based society opinion of ESCR. We have produced a twin-papers consensus, indicated through the documents as respectively "Part I" and "Part II." The first document (Part I) begins with a discussion of features, role, indications, and evidence for CT and MR imaging-based diagnosis of carotid artery disease for risk stratification and prediction of stroke (Section I). It then provides an extensive overview and insight into imaging-derived biomarkers and their potential use in risk stratification (Section II). Finally, detailed recommendations about optimized imaging technique and imaging strategies are summarized (Section III). The second part of this consensus paper (Part II) is focused on structured reporting of carotid imaging studies with CT/MR. KEY POINTS: • CT and MR imaging-based evaluation of carotid artery disease provides essential information for risk stratification and prediction of stroke. • Imaging-derived biomarkers and their potential use in risk stratification are evolving; their correct interpretation and use in clinical practice must be well-understood. • A correct imaging strategy and scan protocol will produce the best possible results for disease evaluation.

Method for Carotid Artery 3-D Ultrasound Image Segmentation Based on CSWin Transformer

Precise segmentation of carotid artery (CA) structure is an important prerequisite for the medical assessment and detection of carotid plaques. For automatic segmentation of the media-adventitia boundary (MAB) and lumen-intima boundary (LIB) in 3-D ultrasound images of the CA, a U-shaped CSWin transformer (U-CSWT) is proposed. Both the encoder and decoder of the U-CSWT are composed of hierarchical CSWT modules, which can capture rich global context information in the 3-D image. Experiments were performed on a 3-D ultrasound image data set of the CA, and the results indicate that the U-CSWT performs better than other convolutional neural network (CNN)-based and CNN-transformer hybrid methods. The model yields Dice coefficients of 94.6 ± 3.0% and 90.8 ± 5.1% for the MAB and LIB in the common carotid artery (CCA) and 92.9 ± 4.9% and 89.6 ± 6.2% for MAB and LIB in the bifurcation, respectively. Our U-CSWT is expected to become an effective method for automatic segmentation of 3-D ultrasound images of CA.

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.

Three-dimensional ultrasound assessment of effects of therapies on carotid atherosclerosis using vessel wall thickness maps

We present a new method for assessing the effects of therapies on atherosclerosis, by measuring the weighted average of carotid vessel-wall-plus-plaque thickness change (ΔVWT¯_Weighted) in 120 patients randomized to pomegranate juice/extract versus placebo. Three-dimensional ultrasound images were acquired at baseline and one year after. Three-dimensional VWT maps were reconstructed and then projected onto a carotid template to obtain two-dimensional VWT maps. Anatomic correspondence on the two-dimensional VWT maps was optimized to reduce misalignment for the same subject and across subjects. A weight was computed at each point on the two-dimensional VWT map to highlight anatomic locations likely to exhibit plaque progression/regression, resulting in ΔVWT¯_Weighted for each subject. The weighted average of VWT-Change measured from the two-dimensional VWT maps with correspondence alignment (ΔVWT¯_Weighted,MDL) detected a significant difference between the pomegranate and placebo groups (P = 0.008). This method improves the cost-effectiveness of proof-of-concept studies involving new therapies for atherosclerosis.

Three-dimensional ultrasound imaging: An effective method to detect the effect of moderate intensity statin treatment in slowing carotid plaque progression

PURPOSE

We aimed to evaluate the feasibility of three-dimensional ultrasound imaging (3DUS) in assessing the therapeutic effect of moderate-intensity statin therapy on carotid atherosclerotic plaques.

METHODS

Patients with carotid plaques were recruited to the study from January 2016 to September 2018, and were divided into two groups based on whether or not they were taking statins. All participants underwent 3DUS of their carotid plaques at baseline, then 3 months and 2 years after initial examination. The changes of the carotid plaques were compared between the two groups.

RESULTS

Were included 97 patients (57 males and 40 females), 65.26 ± 9.53 year-old with 67 into the statin group and 30 in the control group. The baseline levels of total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) were lower in the statin group than in the control group (3.79 ± 0.78 mmol/L vs 4.50 ± 1.12 mmol/L; 2.01 ± 0.62 mmol/L vs 2.58 ± 0.91 mmol/L, P < .05). There was no significant difference in the change of total plaque volume (TPV) detected by 3D-US between the statin (median [interquartile range]: 0 [-30-20] mm³ ) and the control group (0 [-22.5-25] mm³ ) at 3 months. Over 2 years, the TPV increased faster in the control group (+70 [25-150] mm³ ), than in the statin group (15 [-57.5-90) mm³ , P < .05).

CONCLUSIONS

3DUS can be an effective tool to observe the development of carotid plaques and the effect of statin treatment.

Fractal dimension based carotid plaque characterization from three-dimensional ultrasound images

Irregularity of the plaque surface associated with previous plaque rupture plays an important role in the risk estimation of stroke caused by carotid atherosclerotic lesions. Thus, the aim of this study is to develop and validate novel vulnerability biomarkers from three-dimensional ultrasound (3DUS) images by analyzing the surface morphological characteristics of carotid plaque using fractal geometry features. In the experiments, a total of 38 3DUS plaque images were obtained from two groups of patients treated with 80 mg of atorvastatin or placebo daily for 3 months respectively. Two types of 3D fractal dimensions (FDs) were used to describe the smoothness of plaque surface morphology and the roughness from intensity of 3DUS images. Student's t test showed that the two fractal features were effective for detecting the statin-related changes in carotid atherosclerosis with p < 0.00023 and p < 0.0113 respectively. It was concluded that the 3D FD measurements were effective for analyzing carotid plaque characteristics and especially effective for evaluating the impact of atorvastatin treatment. Graphical abstract ᅟ.

Carotid plaque segmentation from three-dimensional ultrasound images by direct three-dimensional sparse field level-set optimization

Total plaque volume (TPV) measured from 3D carotid ultrasound has been shown to be able to predict cardiovascular events and is sensitive in detecting treatment effects. Manual plaque segmentation was performed in previous studies to quantify TPV, but is tedious, requires long training times and is prone to observer variability. This article introduces the first 3D direct volume-based level-set algorithm to segment plaques from 3D carotid ultrasound images. The plaque surfaces were first initialized based on the lumen and outer wall boundaries generated by a previously described semi-automatic algorithm and then deformed by a direct three-dimensional sparse field level-set algorithm, which enforced the longitudinal continuity of the segmented plaque surfaces. This is a marked advantage as compared to a previously proposed 2D slice-by-slice plaque segmentation method. In plaque boundary initialization, the previous technique performed a search on lines connecting corresponding point pairs of the outer wall and lumen boundaries. A limitation of this initialization strategy was that an inaccurate initial plaque boundary would be generated if the plaque was not enclosed entirely by the wall and lumen boundaries. A mechanism is proposed to extend the search range in order to capture the entire plaque if the outer wall boundary lies on a weak edge in the 3D ultrasound image. The proposed method was compared with the previously described 2D slice-by-slice plaque segmentation method in 26 three-dimensional carotid ultrasound images containing 27 plaques with volumes ranging from 12.5 to 450.0 mm³. The manually segmented plaque boundaries serve as the surrogate gold standard. Segmentation accuracy was quantified by volume-, area- and distance-based metrics, including absolute plaque volume difference (|ΔPV|), Dice similarity coefficient (DSC), mean and maximum absolute distance (MAD and MAXD). The proposed direct 3D plaque segmentation algorithm was associated with a significantly lower |ΔPV|, MAD and MAXD, and a significantly higher DSC compared to the previously described slice-by-slice algorithm (|ΔPV|:p=0.012, DSC: p=2.1×10^-4, MAD: p=1.3×10^-4, MAXD: p=5.2×10^-4). The proposed 3D volume-based algorithm required 72±22 s to segment a plaque, which is 40% lower than the 2D slice-by-slice algorithm (114±18 s). The proposed automatic plaque segmentation method generates accurate and reproducible boundaries efficiently and will allow for streamlining plaque quantification based on 3D ultrasound images.

Three-Dimensional Echographic Evaluation of Carotid Artery Disease

The introduction of three-dimensional echography (3D echo) in vascular field is not recent, but it still remains a seldom-used technique because of the costs of ultrasound probe and the need of dedicated laboratories. Therefore, despite significant prognostic implications, the high diagnostic accuracy in plaque definition, and the relative ease of use, 3D echo in vascular field is a niche technique. The purpose of this review is mainly clinical and intends to demonstrate the potential strength of a 3D approach, including technical aspects, in order to present to clinicians and imagers the appealing aspects of a noninvasive and radiation-free methodology with relevant diagnostic and prognostic correlates in the assessment of carotid atherosclerosis. A comprehensive literature search (since 1990s to date) using the PubMed, MEDLINE, and Cochrane libraries databases has been conducted. Articles written in English have been assessed, including reviews, clinical trials, meta-analyses, and interventional/observational studies. Manual cross-referencing was also performed, and relevant references from selected articles were reviewed. The search was limited to studies conducted in humans. Search terms, retrieved also with PubMed Advanced search and AND/OR Boolean operators (mainly in title and abstract), included three-dimensional, echo, stroke/transient ischemic attack, predictors, carotid, imaging, and biomarkers.

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.

Automated localization and segmentation techniques for B-mode ultrasound images: A review

B-mode ultrasound imaging is used extensively in medicine. Hence, there is a need to have efficient segmentation tools to aid in computer-aided diagnosis, image-guided interventions, and therapy. This paper presents a comprehensive review on automated localization and segmentation techniques for B-mode ultrasound images. The paper first describes the general characteristics of B-mode ultrasound images. Then insight on the localization and segmentation of tissues is provided, both in the case in which the organ/tissue localization provides the final segmentation and in the case in which a two-step segmentation process is needed, due to the desired boundaries being too fine to locate from within the entire ultrasound frame. Subsequenly, examples of some main techniques found in literature are shown, including but not limited to shape priors, superpixel and classification, local pixel statistics, active contours, edge-tracking, dynamic programming, and data mining. Ten selected applications (abdomen/kidney, breast, cardiology, thyroid, liver, vascular, musculoskeletal, obstetrics, gynecology, prostate) are then investigated in depth, and the performances of a few specific applications are compared. In conclusion, future perspectives for B-mode based segmentation, such as the integration of RF information, the employment of higher frequency probes when possible, the focus on completely automatic algorithms, and the increase in available data are discussed.

Multimodality ultrasound imaging in stroke: current concepts and future focus

INTRODUCTION

Stroke is a leading cause of disability and mortality worldwide. Ultrasound is a real-time imaging technique that is inexpensive, portable, non-invasive, and safe, with high diagnostic accuracy. Ultrasonic imaging can provide useful direct and indirect information about the characteristics of various vessels in the both intracranial and extracranial segments. Areas covered: In this review, we will discuss multimodal applications of ultrasonic imaging in stroke prevention and management including checking carotid intima-media thickness progression, evaluating the plaque morphology, calibrating the degree of stenosis, detecting the presence of patent foramen ovale, monitoring microembolization, and screening for stroke risk in patients with sickle cell disease. We present the conventional ultrasonography as well as the novel ultrasound techniques including gray scale median, 3-dementional ultrasound, elastography, intravascular ultrasound, and contrast-enhanced ultrasound. Expert commentary: Ultrasonography is a non-invasive, low-cost, safe, fast, and real-time imaging technology for stroke risk assessment. Each modality has its own advantage as well as limitation. Future research should be focused on developing new technologies that can improve the quality of imaging and accuracy of diagnosis.

Three-Dimensional Ultrasound of Carotid Plaque

Measurement of plaque burden is different from measurement of carotid intima-media thickness (IMT). Carotid total plaque area is a stronger predictor of cardiovascular risk than IMT, and in contrast to progression of IMT, which does not predict cardiovascular events, progression of total plaque area and total plaque volume strongly predict cardiovascular events. Measurement of plaque burden is useful in genetic research, and in evaluation of new therapies for atherosclerosis. Perhaps more importantly, it can be used for management of patients. A strategy called "treating arteries instead of treating risk factors" markedly reduces risk among patients with asymptomatic carotid stenosis.

Secondary Stroke Prevention: Improving Diagnosis and Management with Newer Technologies

Treatment of hypertension, diabetes, high cholesterol, smoking cessation, and healthy lifestyle have all contributed to the decline in the incidence of vascular disease over the last several decades. Patients who suffer an acute stroke are at a high risk for recurrence. Introduction of newer technologies and their wider use allows for better identification of patients in whom the risk of recurrence following an acute stroke may be very high. Traditionally, the major focus for diagnosis and management has focused on patient history, examination, imaging for carotid stenosis/occlusion, and detection of AF and paroxysmal AF (PAF) with 24-48 h cardiac monitoring. This review focuses on the usefulness of three newer investigative tools that are becoming widely available and lead to better prevention. Continuous ambulatory blood pressure measurements for 24 h or longer and 3D Doppler measures of the carotid arteries provide key useful information on the state of vascular health and enhance our ability to monitor the response to preventive therapies. Furthermore, the detection of PAF can be significantly improved with prolonged cardiac monitoring for 3 weeks or longer, enabling the initiation of appropriate prevention therapy. This review will focus on the potential impact and importance of these emerging technologies on the prevention of recurrent stroke in high-risk patients.

Plaque Assessment in the Management of Patients with Asymptomatic Carotid Stenosis

The continued occurrence of stroke despite advances in medical therapy for asymptomatic carotid stenosis (ACS) strongly indicates that individual response to medical therapy may vary widely. This article reviews the literature that identifies MR imaging and ultrasound plaque features which are seen in patients at increased risk of future cardiovascular events. Imaging can identify plaque phenotype that is the most amendable to intensive medical therapy. There is also good evidence that plaque imaging can measure the individual response to medical therapy and the lack of response identifies a high-risk group of ACS patients.

Standard plane localization in ultrasound by radial component model and selective search

Acquisition of the standard plane is crucial for medical ultrasound diagnosis. However, this process requires substantial experience and a thorough knowledge of human anatomy. Therefore it is very challenging for novices and even time consuming for experienced examiners. We proposed a hierarchical, supervised learning framework for automatically detecting the standard plane from consecutive 2-D ultrasound images. We tested this technique by developing a system that localizes the fetal abdominal standard plane from ultrasound video by detecting three key anatomical structures: the stomach bubble, umbilical vein and spine. We first proposed a novel radial component-based model to describe the geometric constraints of these key anatomical structures. We then introduced a novel selective search method which exploits the vessel probability algorithm to produce probable locations for the spine and umbilical vein. Next, using component classifiers trained by random forests, we detected the key anatomical structures at their probable locations within the regions constrained by the radial component-based model. Finally, a second-level classifier combined the results from the component detection to identify an ultrasound image as either a "fetal abdominal standard plane" or a "non- fetal abdominal standard plane." Experimental results on 223 fetal abdomen videos showed that the detection accuracy of our method was as high as 85.6% and significantly outperformed both the full abdomen and the separate anatomy detection methods without geometric constraints. The experimental results demonstrated that our system shows great promise for application to clinical practice.