Image analysis techniques for automated IVUS contour detection

A State-Of-The-Art Review on Coronary Artery Border Segmentation Algorithms for Intravascular Ultrasound (IVUS) Images

Intravascular Ultrasound images (IVUS) is a useful guide for medical practitioners to identify the vascular status of coronary arteries in human beings. IVUS is a unique intracoronary imaging modality that is used as an adjunct to angioplasty to view vessel structures using a catheter with high resolutions. Segmentation of IVUS images has always remained a challenging task due to various impediments, for example, similar tissue components, vessel structures, and artifacts imposed during the acquisition process. Many researchers have applied various techniques to develop standard methods of image interpretation, however, the ultimate goal is still elusive to most researchers. This challenge was presented at the MICCAI- Computing and Visualization for (Intra)Vascular Imaging (CVII) workshop in 2011. This paper presents a major review of recently reported work in the field, with a detailed analysis of various segmentation techniques applied in IVUS, and highlights the directions for future research. The findings recommend a reference database with a larger number of samples acquired at varied transducer frequencies with special consideration towards complex lesions, suitable validation metrics, and ground-truth definition as a standard against which to compare new and current algorithms.

Fully automated lumen and vessel contour segmentation in intravascular ultrasound datasets

Segmentation of lumen and vessel contours in intravascular ultrasound (IVUS) pullbacks is an arduous and time-consuming task, which demands adequately trained human resources. In the present study, we propose a machine learning approach to automatically extract lumen and vessel boundaries from IVUS datasets. The proposed approach relies on the concatenation of a deep neural network to deliver a preliminary segmentation, followed by a Gaussian process (GP) regressor to construct the final lumen and vessel contours. A multi-frame convolutional neural network (MFCNN) exploits adjacency information present in longitudinally neighboring IVUS frames, while the GP regression method filters high-dimensional noise, delivering a consistent representation of the contours. Overall, 160 IVUS pullbacks (63 patients) from the IBIS-4 study (Integrated Biomarkers and Imaging Study-4, Trial NCT00962416), were used in the present work. The MFCNN algorithm was trained with 100 IVUS pullbacks (8427 manually segmented frames), was validated with 30 IVUS pullbacks (2583 manually segmented frames) and was blindly tested with 30 IVUS pullbacks (2425 manually segmented frames). Image and contour metrics were used to characterize model performance by comparing ground truth (GT) and machine learning (ML) contours. Median values (interquartile range, IQR) of the Jaccard index for lumen and vessel were 0.913, [0.882,0.935] and 0.940, [0.917,0.957], respectively. Median values (IQR) of the Hausdorff distance for lumen and vessel were 0.196mm, [0.146,0.275]mm and 0.163mm, [0.122,0.234]mm, respectively. Also, the mean value of lumen area predictions, and limits of agreement were -0.19mm², [1.1,-1.5]mm², while the mean value and limits of agreement of plaque burden were 0.0022, [0.082,-0.078]. The results obtained with the model developed in this work allow us to conclude that the proposed machine learning approach delivers accurate segmentations in terms of image metrics, contour metrics and clinically relevant variables, enabling its use in clinical routine by mitigating the costs involved in the manual management of IVUS datasets.

Vessel membrane segmentation and calcification location in intravascular ultrasound images using a region detector and an effective selection strategy

BACKGROUND AND OBJECTIVE

Segmenting vessel membranes and locating the calcific region in intravascular ultrasound (IVUS) images aid physicians in the diagnosis of atherosclerosis. However, the manual extraction of the media adventitia (MA)/lumen border and calcification location are cumbersome due to the excessive number of IVUS frames. Moreover, most existing (semi-)automatic detection methods cannot achieve both vessel membrane extraction and calcification location simultaneously, and they are unable to detect vessel membranes in IVUS frames from different acquisition systems.

METHOD

A fully automatic approach is proposed based on extremal regions and a flexible selection strategy to extract vessel membranes in different IVUS frames and locate the calcific region in high-frequency ones. Three main steps are included in the algorithm. First, a region detector is employed to extract extremal regions from an IVUS image. Then, according to the selection strategy, a part of the extracted regions is selected. At the same time, the calcification is located according to its special acoustic properties. Next, approximate MA and lumen border segmentation is achieved based on the selected extremal regions and the located calcification in polar coordinates. Finally, the final segmentation results are obtained by smoothing the approximate values.

RESULT

To demonstrate the feasibility of the method, it was evaluated based on a standard public dataset. Furthermore, to quantitatively evaluate the segmentation performance, the Hausdorff distance (HD), Jaccard measure (JM) and percentage of area difference (PAD) were used. The results show that a mean HD of 1.13/1.21 mm, a mean JM of 0.83/0.77 and a mean PAD of 0.11/0.23 are achieved for MA/lumen border detection in 77 40-MHz IVUS images. For MA/lumen border extraction in 435 20-MHz IVUS frames, the average HD, JM and PAD values are 0.47/0.28 mm, 0.84/0.89 and 0.13/0.10, respectively. In addition, the approach successfully achieves calcification location in 40-MHz IVUS frames. In comparison with other published methods, the method proposed in this study is competitive.

CONCLUSION

According to these results, our strategy can extract MA/lumen borders in different IVUS frames and effectively locate calcification in high-frequency IVUS frames.

Using Convolutional Neural Networks for Classification of Bifurcation Regions in IVOCT Images

Optical Coherence Tomography (OCT) technology enabled the experts to analyze coronary lesions from high-resolution intravascular images. Studies have shown the relationship between bifurcation regions and a higher occurrence of wall thickening and lesions in these areas. Some level of automation could benefit experts, since examining pullback frames is a laborious and time-consuming task. Although Convolutional Neural Networks (CNN) have shown promising results in classification tasks of medical images, we did not identify the use of CNN's in IVOCT images to classify bifurcation regions in the literature. In this work, we evaluated a CNN architecture in the bifurcation classification task trained with IVOCT images from 9 pullbacks from 9 different patients. We used data augmentation to balance the dataset, due to the low amount of bifurcation-labeled frames. Our classification results are comparable to other works in the literature, presenting better result in AUC (99.70%).

Radial-Biased Tracking Method to Assess Tissue Displacement in Intravascular Ultrasound Sequences: A Phantom Framework Evaluation

OBJECTIVES

We created and evaluated a pixel-tracking method capable of accurately identify the displacement of tissue in intravascular ultrasound (IVUS) images.

METHODS

Our proposed pixel-tracking method assessed the horizontal and vertical displacement of tissue from a numerical phantom of IVUS sequences. The proposed tracking method is based on a block-matching framework, comparing 2 distinct frames within a selected region by normalized cross-correlation. Our method, specialized for IVUS applications, reduced the tracking area by implementing a limiting radius and a radial bias during the search.

RESULTS

The method was evaluated by using 54 numerical phantom image sequences from 9 distinct arterial models, resulting in different arteries with atherosclerotic plaques under a range of pressures. The ground truth reference coordinates of the tracked tissue were extracted from each numerical phantom sequence. Our results were compared to 8 other methods present in the literature. The mean absolute tracking errors ± SD for our method were 15.56 ± 19.46 and 13.04 ± 13.82 μm for the horizontal and vertical directions, respectively, between 2 subsequent frames, and 162.58 ± 305.93 and 102.22 ± 130.61 μm from lower to higher pressures in the range of 6 frames (n = 42,036).

CONCLUSIONS

Our application-specific pixel-tracking method showed promising results and no statistically significant tracking error (P = .954), comparable to state-of-the-art methods present in the literature. Application-specific tracking methods have advantages over general methods by turning tissue-specific behavior into a directional bias in the tracking algorithm.

A New Approach to Border Irregularity Assessment with Application in Skin Pathology

The border irregularity assessment of tissue structures is an important step in medical diagnostics (e.g., in dermatoscopy, pathology, and cardiology). The diagnostic criteria based on the degree of uniformity and symmetry of border irregularities are particularly vital in dermatopathology, to distinguish between benign and malignant skin lesions. We propose a new method for the segmentation of individual border projections and measuring their morphometry. It is based mainly on analyzing the curvature of the object’s border to identify endpoints of projection bases, and on analyzing object’s skeleton in the graph representation to identify bases of projections and their location along the object’s main axis. The proposed segmentation method has been tested on 25 skin whole slide images of common melanocytic lesions. In total, 825 out of 992 (83%) manually segmented retes (projections of epidermis) were detected correctly and the Jaccard similarity coefficient for the task of detecting retes was 0.798. Experimental results verified the effectiveness of the proposed approach. Our method is particularly well suited for assessing the border irregularity of human epidermis and thus could help develop computer-aided diagnostic algorithms for skin cancer detection.

Patient-Specific Modeling of Stented Coronary Arteries Reconstructed from Optical Coherence Tomography: Towards a Widespread Clinical Use of Fluid Dynamics Analyses

The recent widespread application of optical coherence tomography (OCT) in interventional cardiology has improved patient-specific modeling of stented coronary arteries for the investigation of local hemodynamics. In this review, the workflow for the creation of fluid dynamics models of stented coronary arteries from OCT images is presented. The algorithms for lumen contours and stent strut detection from OCT as well as the reconstruction methods of stented geometries are discussed. Furthermore, the state of the art of studies that investigate the hemodynamics of OCT-based stented coronary artery geometries is reported. Although those studies analyzed few patient-specific cases, the application of the current reconstruction methods of stented geometries to large populations is possible. However, the improvement of these methods and the reduction of the time needed for the entire modeling process are crucial for a widespread clinical use of the OCT-based models and future in silico clinical trials.

ARCOCT: Automatic detection of lumen border in intravascular OCT images

BACKGROUND AND OBJECTIVE

Intravascular optical coherence tomography (OCT) is an invaluable tool for the detection of pathological features on the arterial wall and the investigation of post-stenting complications. Computational lumen border detection in OCT images is highly advantageous, since it may support rapid morphometric analysis. However, automatic detection is very challenging, since OCT images typically include various artifacts that impact image clarity, including features such as side branches and intraluminal blood presence. This paper presents ARCOCT, a segmentation method for fully-automatic detection of lumen border in OCT images.

METHODS

ARCOCT relies on multiple, consecutive processing steps, accounting for image preparation, contour extraction and refinement. In particular, for contour extraction ARCOCT employs the transformation of OCT images based on physical characteristics such as reflectivity and absorption of the tissue and, for contour refinement, local regression using weighted linear least squares and a 2nd degree polynomial model is employed to achieve artifact and small-branch correction as well as smoothness of the artery mesh. Our major focus was to achieve accurate contour delineation in the various types of OCT images, i.e., even in challenging cases with branches and artifacts.

RESULTS

ARCOCT has been assessed in a dataset of 1812 images (308 from stented and 1504 from native segments) obtained from 20 patients. ARCOCT was compared against ground-truth manual segmentation performed by experts on the basis of various geometric features (e.g. area, perimeter, radius, diameter, centroid, etc.) and closed contour matching indicators (the Dice index, the Hausdorff distance and the undirected average distance), using standard statistical analysis methods. The proposed method was proven very efficient and close to the ground-truth, exhibiting non statistically-significant differences for most of the examined metrics.

CONCLUSIONS

ARCOCT allows accurate and fully-automated lumen border detection in OCT images.

Segmenting breast cancerous regions in thermal images using fuzzy active contours

Breast cancer is the main cause of death among young women in developing countries. The human body temperature carries critical medical information related to the overall body status. Abnormal rise in total and regional body temperature is a natural symptom in diagnosing many diseases. Thermal imaging (Thermography) utilizes infrared beams which are fast, non-invasive, and non-contact and the output created images by this technique are flexible and useful to monitor the temperature of the human body. In some clinical studies and biopsy tests, it is necessary for the clinician to know the extent of the cancerous area. In such cases, the thermal image is very useful. In the same line, to detect the cancerous tissue core, thermal imaging is beneficial. This paper presents a fully automated approach to detect the thermal edge and core of the cancerous area in thermography images. In order to evaluate the proposed method, 60 patients with an average age of 44/9 were chosen. These cases were suspected of breast tissue disease. These patients referred to Tehran Imam Khomeini Imaging Center. Clinical examinations such as ultrasound, biopsy, questionnaire, and eventually thermography were done precisely on these individuals. Finally, the proposed model is applied for segmenting the proved abnormal area in thermal images. The proposed model is based on a fuzzy active contour designed by fuzzy logic. The presented method can segment cancerous tissue areas from its borders in thermal images of the breast area. In order to evaluate the proposed algorithm, Hausdorff and mean distance between manual and automatic method were used. Estimation of distance was conducted to accurately separate the thermal core and edge. Hausdorff distance between the proposed and the manual method for thermal core and edge was 0.4719 ± 0.4389, 0.3171 ± 0.1056 mm respectively, and the average distance between the proposed and the manual method for core and thermal edge was 0.0845 ± 0.0619, 0.0710 ± 0.0381 mm respectively. Furthermore, the sensitivity in recognizing the thermal pattern in breast tissue masses is 85 % and its accuracy is 91.98 %.A thermal imaging system has been proposed that is able to recognize abnormal breast tissue masses. This system utilizes fuzzy active contours to extract the abnormal regions automatically.

Automatic lumen contour detection in intravascular OCT images using Otsu binarization and intensity curve

This paper proposes an automatic method for the detection of lumen contours in intravascular OCT images with guide wire shadow artifacts. This algorithm is divided into five main procedures: pre-processing, an Otsu binarization approach, an intensity curve approach, a lumen contour position correction, and image reconstruction and contour extraction. The 30 IVOCT images from six anonymous patients were used to verify this method and we obtained 99.2% sensitivity and 99.7% specificity with this algorithm.

Quantification of coronary artery Stenosis by Area Stenosis from cardiac CT angiography

Non-invasive cardiac computed tomography angiography (CTA) is widely used to assess coronary artery stenosis and give clinical decision-making support to clinicians. The severity of stenosis lesion is commonly graded by a range of percent Diameter Stenosis (DS), which can introduce false positive diagnoses or over-estimation, triggering unnecessary further procedures. In this paper, a system and the associate methods to quantify stenosis by the percent Area Stenosis (AS) from cardiac CTA is presented. In the process, coronary artery tree is segmented and the centerline is extracted by Hessian filtering and the minimal path method. After a serial of 2D cross-sectional artery images along the artery centerline are obtained, lumen areas are segmented by ellipse-fitting with deformable models, and consequently to compute the lesion's AS. Experimental results on 5 CTA data sets show that compared to DS, AS better correlates to the reference standard for stenosis quantification, suggesting the efficacy of the proposed system.

Automated Framework for Detecting Lumen and Media-Adventitia Borders in Intravascular Ultrasound Images

An automated framework for detecting lumen and media-adventitia borders in intravascular ultrasound images was developed on the basis of an adaptive region-growing method and an unsupervised clustering method. To demonstrate the capability of the framework, linear regression, Bland-Altman analysis and distance analysis were used to quantitatively investigate the correlation, agreement and spatial distance, respectively, between our detected borders and manually traced borders in 337 intravascular ultrasound images in vivo acquired from six patients. The results of these investigations revealed good correlation (r = 0.99), good agreement (>96.82% of results within the 95% confidence interval) and small average distance errors (lumen border: 0.08 mm, media-adventitia border: 0.10 mm) between the borders generated by the automated framework and the manual tracing method. The proposed framework was found to be effective in detecting lumen and media-adventitia borders in intravascular ultrasound images, indicating its potential for use in routine studies of vascular disease.

Computer Vision Techniques for Transcatheter Intervention

Minimally invasive transcatheter technologies have demonstrated substantial promise for the diagnosis and the treatment of cardiovascular diseases. For example, transcatheter aortic valve implantation is an alternative to aortic valve replacement for the treatment of severe aortic stenosis, and transcatheter atrial fibrillation ablation is widely used for the treatment and the cure of atrial fibrillation. In addition, catheter-based intravascular ultrasound and optical coherence tomography imaging of coronary arteries provides important information about the coronary lumen, wall, and plaque characteristics. Qualitative and quantitative analysis of these cross-sectional image data will be beneficial to the evaluation and the treatment of coronary artery diseases such as atherosclerosis. In all the phases (preoperative, intraoperative, and postoperative) during the transcatheter intervention procedure, computer vision techniques (e.g., image segmentation and motion tracking) have been largely applied in the field to accomplish tasks like annulus measurement, valve selection, catheter placement control, and vessel centerline extraction. This provides beneficial guidance for the clinicians in surgical planning, disease diagnosis, and treatment assessment. In this paper, we present a systematical review on these state-of-the-art methods. We aim to give a comprehensive overview for researchers in the area of computer vision on the subject of transcatheter intervention. Research in medical computing is multi-disciplinary due to its nature, and hence, it is important to understand the application domain, clinical background, and imaging modality, so that methods and quantitative measurements derived from analyzing the imaging data are appropriate and meaningful. We thus provide an overview on the background information of the transcatheter intervention procedures, as well as a review of the computer vision techniques and methodologies applied in this area.

Shape-based approach for coronary calcium lesion volume measurement on intravascular ultrasound imaging and its association with carotid intima-media thickness

OBJECTIVES

Coronary calcification plays an important role in diagnostic classification of lesion subsets. According to histopathologic studies, vulnerable atherosclerotic plaque contains calcified deposits, and there can be considerable variation in the extent and degree of calcification. Intravascular ultrasound (IVUS) has demonstrated its role in imaging coronary arteries, thereby displaying calcium lesions. The aim of this work was to develop a fully automated system for detection, area and volume measurement, and characterization of the largest calcium deposits in coronary arteries. Furthermore, we demonstrate the correlation between the coronary calcium IVUS volume and the neurologic risk biomarker B-mode carotid intima-media thickness (IMT).

METHODS

Our system automatically detects the frames with calcium, identifies the largest calcium region, and performs shape-based volume measurements. The carotid IMT is measured by using AtheroEdge software (AtheroPoint, LLC) on B-mode ultrasound imaging.

RESULTS

Our database consists of low-contrast IVUS videos and corresponding B-mode images from 100 patients. Our experiments showed that the correlation between calcium volumes and carotid IMT was higher for the left carotid artery compared to the right carotid artery (r = 0.066 for the left carotid artery and 0.121 for the right carotid artery). We obtained 97% accuracy for automated calcium detection compared against the scoring given by our expert radiologists. Furthermore, we benchmarked shape-based volume measurement against the conventional method, which used integration of regions and showed a correlation of 84%.

CONCLUSIONS

Since carotid IMT is an independent prognostic factor for myocardial infarction, and calcium lesions are correlated with stroke risk, we believe that this automated system for calcium volume measurement could be useful for assessing patients' cardiovascular risk.

Fluorescence lifetime imaging and intravascular ultrasound: co-registration study using ex vivo human coronaries

Fluorescence lifetime imaging (FLIM) has demonstrated potential for robust assessment of atherosclerotic plaques biochemical composition and for complementing conventional intravascular ultrasound (IVUS), which provides information on plaque morphology. The success of such a bi-modal imaging modality depends on accurate segmentation of the IVUS images and proper angular registration between these two modalities. This paper reports a novel IVUS segmentation methodology addressing this issue. The image preprocessing consisted of denoising, using the Wiener filter, followed by image smoothing, implemented through the application of the alternating sequential filter on the edge separability metric images. Extraction of the lumen/intima and media/adventitia boundaries was achieved by tracing the gray-scale peaks over the A-lines of the IVUS preprocessed images. Cubic spline interpolation, in both cross-sectional and longitudinal directions, ensured boundary smoothness and continuity. The detection of the guide-wire artifact in both modalities is used for angular registration. Intraluminal studies were conducted in 13 ex vivo segments of human coronaries. The IVUS segmentation accuracy was assessed against independent manual tracings, providing 91.82% sensitivity and 97.55% specificity. The proposed methodology makes the bi-modal FLIM and IVUS approach feasible for comprehensive intravascular diagnosis by providing co-registered biochemical and morphological information of atherosclerotic plaques.

Clinical validation of an algorithm for rapid and accurate automated segmentation of intracoronary optical coherence tomography images

OBJECTIVES

The analysis of intracoronary optical coherence tomography (OCT) images is based on manual identification of the lumen contours and relevant structures. However, manual image segmentation is a cumbersome and time-consuming process, subject to significant intra- and inter-observer variability. This study aims to present and validate a fully-automated method for segmentation of intracoronary OCT images.

METHODS

We studied 20 coronary arteries (mean length=39.7±10.0 mm) from 20 patients who underwent a clinically-indicated cardiac catheterization. The OCT images (n=1812) were segmented manually, as well as with a fully-automated approach. A semi-automated variation of the fully-automated algorithm was also applied. Using certain lumen size and lumen shape characteristics, the fully- and semi-automated segmentation algorithms were validated over manual segmentation, which was considered as the gold standard.

RESULTS

Linear regression and Bland-Altman analysis demonstrated that both the fully-automated and semi-automated segmentation had a very high agreement with the manual segmentation, with the semi-automated approach being slightly more accurate than the fully-automated method. The fully-automated and semi-automated OCT segmentation reduced the analysis time by more than 97% and 86%, respectively, compared to manual segmentation.

CONCLUSIONS

In the current work we validated a fully-automated OCT segmentation algorithm, as well as a semi-automated variation of it in an extensive "real-life" dataset of OCT images. The study showed that our algorithm can perform rapid and reliable segmentation of OCT images.

Standardized evaluation methodology and reference database for evaluating IVUS image segmentation

This paper describes an evaluation framework that allows a standardized and quantitative comparison of IVUS lumen and media segmentation algorithms. This framework has been introduced at the MICCAI 2011 Computing and Visualization for (Intra)Vascular Imaging (CVII) workshop, comparing the results of eight teams that participated. We describe the available data-base comprising of multi-center, multi-vendor and multi-frequency IVUS datasets, their acquisition, the creation of the reference standard and the evaluation measures. The approaches address segmentation of the lumen, the media, or both borders; semi- or fully-automatic operation; and 2-D vs. 3-D methodology. Three performance measures for quantitative analysis have been proposed. The results of the evaluation indicate that segmentation of the vessel lumen and media is possible with an accuracy that is comparable to manual annotation when semi-automatic methods are used, as well as encouraging results can be obtained also in case of fully-automatic segmentation. The analysis performed in this paper also highlights the challenges in IVUS segmentation that remains to be solved.

IVUSAngio tool: a publicly available software for fast and accurate 3D reconstruction of coronary arteries

There is an ongoing research and clinical interest in the development of reliable and easily accessible software for the 3D reconstruction of coronary arteries. In this work, we present the architecture and validation of IVUSAngio Tool, an application which performs fast and accurate 3D reconstruction of the coronary arteries by using intravascular ultrasound (IVUS) and biplane angiography data. The 3D reconstruction is based on the fusion of the detected arterial boundaries in IVUS images with the 3D IVUS catheter path derived from the biplane angiography. The IVUSAngio Tool suite integrates all the intermediate processing and computational steps and provides a user-friendly interface. It also offers additional functionality, such as automatic selection of the end-diastolic IVUS images, semi-automatic and automatic IVUS segmentation, vascular morphometric measurements, graphical visualization of the 3D model and export in a format compatible with other computer-aided design applications. Our software was applied and validated in 31 human coronary arteries yielding quite promising results. Collectively, the use of IVUSAngio Tool significantly reduces the total processing time for 3D coronary reconstruction. IVUSAngio Tool is distributed as free software, publicly available to download and use.

Automatic lumen segmentation in IVOCT images using binary morphological reconstruction

Background

Atherosclerosis causes millions of deaths, annually yielding billions in expenses round the world. Intravascular Optical Coherence Tomography (IVOCT) is a medical imaging modality, which displays high resolution images of coronary cross-section. Nonetheless, quantitative information can only be obtained with segmentation; consequently, more adequate diagnostics, therapies and interventions can be provided. Since it is a relatively new modality, many different segmentation methods, available in the literature for other modalities, could be successfully applied to IVOCT images, improving accuracies and uses.

Method

An automatic lumen segmentation approach, based on Wavelet Transform and Mathematical Morphology, is presented. The methodology is divided into three main parts. First, the preprocessing stage attenuates and enhances undesirable and important information, respectively. Second, in the feature extraction block, wavelet is associated with an adapted version of Otsu threshold; hence, tissue information is discriminated and binarized. Finally, binary morphological reconstruction improves the binary information and constructs the binary lumen object.

Results

The evaluation was carried out by segmenting 290 challenging images from human and pig coronaries, and rabbit iliac arteries; the outcomes were compared with the gold standards made by experts. The resultant accuracy was obtained: True Positive (%) = 99.29 ± 2.96, False Positive (%) = 3.69 ± 2.88, False Negative (%) = 0.71 ± 2.96, Max False Positive Distance (mm) = 0.1 ± 0.07, Max False Negative Distance (mm) = 0.06 ± 0.1.

Conclusions

In conclusion, by segmenting a number of IVOCT images with various features, the proposed technique showed to be robust and more accurate than published studies; in addition, the method is completely automatic, providing a new tool for IVOCT segmentation.

Realistic IVUS image generation in different intraluminal pressures

Intravascular ultrasound (IVUS) phantoms are important to calibrate and evaluate many IVUS imaging processing tasks. However, phantom generation is never the primary focus of related works; hence, it cannot be well covered, and is usually based on more than one platform, which may not be accessible to investigators. Therefore, we present a framework for creating representative IVUS phantoms, for different intraluminal pressures, based on the finite element method and Field II. First, a coronary cross-section model is selected. Second, the coronary regions are identified to apply the properties. Third, the corresponding mesh is generated. Fourth, the intraluminal force is applied and the deformation computed. Finally, the speckle noise is incorporated. The framework was tested taking into account IVUS contrast, noise and strains. The outcomes are in line with related studies and expected values. Moreover, the framework toolbox is freely accessible and fully implemented in a single platform.

Endothelial shear stress in the evolution of coronary atherosclerotic plaque and vascular remodelling: current understanding and remaining questions

The heterogeneity of plaque formation, the vascular remodelling response to plaque formation, and the consequent phenotype of plaque instability attest to the extraordinarily complex pathobiology of plaque development and progression, culminating in different clinical coronary syndromes. Atherosclerotic plaques predominantly form in regions of low endothelial shear stress (ESS), whereas regions of moderate/physiological and high ESS are generally protected. Low ESS-induced compensatory expansive remodelling plays an important role in preserving lumen dimensions during plaque progression, but when the expansive remodelling becomes excessive promotes continued influx of lipids into the vessel wall, vulnerable plaque formation and potential precipitation of an acute coronary syndrome. Advanced plaques which start to encroach into the lumen experience high ESS at their most stenotic region, which appears to promote plaque destabilization. This review describes the role of ESS from early atherogenesis to early plaque formation, plaque progression to advanced high-risk stenotic or non-stenotic plaque, and plaque destabilization. The critical implication of the vascular remodelling response to plaque growth is also discussed. Current developments in technology to characterize local ESS and vascular remodelling in vivo may provide a rationale for innovative diagnostic and therapeutic strategies for coronary patients that aim to prevent clinical coronary syndromes.

A state-of-the-art review on segmentation algorithms in intravascular ultrasound (IVUS) images

Over the past two decades, intravascular ultrasound (IVUS) image segmentation has remained a challenge for researchers while the use of this imaging modality is rapidly growing in catheterization procedures and in research studies. IVUS provides cross-sectional grayscale images of the arterial wall and the extent of atherosclerotic plaques with high spatial resolution in real time. In this paper, we review recently developed image processing methods for the detection of media-adventitia and luminal borders in IVUS images acquired with different transducers operating at frequencies ranging from 20 to 45 MHz. We discuss methodological challenges, lack of diversity in reported datasets, and weaknesses of quantification metrics that make IVUS segmentation still an open problem despite all efforts. In conclusion, we call for a common reference database, validation metrics, and ground-truth definition with which new and existing algorithms could be benchmarked.

Automatic coronary wall segmentation in intravascular ultrasound images using binary morphological reconstruction

Intravascular ultrasound (IVUS) image segmentation can provide more detailed vessel and plaque information, resulting in better diagnostics, evaluation and therapy planning. A novel automatic segmentation proposal is described herein; the method relies on a binary morphological object reconstruction to segment the coronary wall in IVUS images. First, a preprocessing followed by a feature extraction block are performed, allowing for the desired information to be extracted. Afterward, binary versions of the desired objects are reconstructed, and their contours are extracted to segment the image. The effectiveness is demonstrated by segmenting 1300 images, in which the outcomes had a strong correlation to their corresponding gold standard. Moreover, the results were also corroborated statistically by having as high as 92.72% and 91.9% of true positive area fraction for the lumen and media adventitia border, respectively. In addition, this approach can be adapted easily and applied to other related modalities, such as intravascular optical coherence tomography and intravascular magnetic resonance imaging.

Contour detection of atherosclerotic plaques in IVUS images using ellipse template matching and particle swarm optimization

It is valuable for diagnosis of atherosclerosis to detect lumen and media-adventitia contours in intravascular ultrasound (IVUS) images of atherosclerotic plaques. In this paper, a method for contour detection of plaques is proposed utilizing the prior knowledge of elliptic geometry of plaques. Contours are initialized as ellipses by using ellipse template matching, where a matching function is maximized by particle swarm optimization. Then the contours are refined by boundary vector field snakes. The method was evaluated via 88 in vivo images from 21 patients. It outperformed a state-of-the-art method by 3.8 pixels and 4.8% in terms of the mean distance error and relative mean distance error, respectively.

An inverse scattering algorithm for the segmentation of the luminal border on intravascular ultrasound data

Intravascular ultrasound (IVUS) is a catheter-based medical imaging technique that produces cross-sectional images of blood vessels and is particularly useful for studying atherosclerosis. In this paper, we present a novel method for segmentation of the luminal border on IVUS images using the radio frequency (RF) raw signal based on a scattering model and an inversion scheme. The scattering model is based on a random distribution of point scatterers in the vessel. The per-scatterer signal uses a differential backscatter cross-section coefficient (DBC) that depends on the tissue type. Segmentation requires two inversions: a calibration inversion and a reconstruction inversion. In the calibration step, we use a single manually segmented frame and then solve an inverse problem to recover the DBC for the lumen and vessel wall (kappa(l) and kappa(w), respectively) and the width of the impulse signal theta. In the reconstruction step, we use the parameters from the calibration step to solve a new inverse problem: for each angle theta(i) of the IVUS data, we reconstruct the lumen-vessel wall interface. We evaluated our method using three 40MHz IVUS sequences by comparing with manual segmentations. Our preliminary results indicate that it is possible to segment the luminal border by solving an inverse problem using the IVUS RF raw signal with the scatterer model.

Ultrasound image segmentation and tissue characterization

Ultrasound image segmentation deals with delineating the boundaries of structures, as a step towards semi-automated or fully automated measurement of dimensions or for characterizing tissue regions. Ultrasound tissue characterization (UTC) is driven by knowledge of the physics of ultrasound and its interactions with biological tissue, and has traditionally used signal modelling and analysis to characterize and differentiate between healthy and diseased tissue. Thus, both aim to enhance the capabilities of ultrasound as a quantitative tool in clinical medicine, and the two end goals can be the same, namely to characterize the health of tissue. This article reviews both research topics, and finds that the two fields are becoming more tightly coupled, even though there are key challenges to overcome in each area, influenced by factors such as more open software-based ultrasound system architectures, increased computational power, and advances in imaging transducer design.

Automatic segmentation of calcifications in intravascular ultrasound images using snakes and the contourlet transform

It is valuable to detect calcifications in intravascular ultrasound images for studies of coronary artery diseases. An image segmentation method based on snakes and the Contourlet transform is proposed to automatically and accurately detect calcifications. With the Contourlet transform, an original image is decomposed into low-pass bands and band-pass directional sub-bands. The 2-D Renyi's entropy is used to adaptively threshold the low-pass bands in a multiresolution hierarchy to determine regions-of-interest (ROIs). Then a mean intensity ratio, reflecting acoustic shadowing, is presented to classify calcifications from noncalcifications and obtain initial contours of calcifications. The anisotropic diffusion is used in bandpass directional sub-bands to suppress noise and preserve calcific edges. Finally, the contour deformation in the boundary vector field is used to obtain final contours of calcifications. The method was evaluated via 60 simulated images and 86 in vivo images. It outperformed a recently proposed method, the Santos Filho method, by 2.76% and 14.53%, in terms of the sensitivity and specificity of calcification detection, respectively. The area under the receiver operating characteristic curve increased by 0.041. The relative mean distance error, relative difference degree, relative arc difference, relative thickness difference and relative length difference were reduced by 5.73%, 19.79%, 11.62%, 12.06% and 20.51%, respectively. These results reveal that the proposed method can automatically and accurately detect calcifications and delineate their boundaries. (E-mail: yywang@fudan.edu.cn).