A novel active contour model for fully automated segmentation of intravascular ultrasound images: In vivo validation in human coronary arteries

Transformer and Attention-Based Architectures for Segmentation of Coronary Arterial Walls in Intravascular Ultrasound: A Narrative Review

Background: The leading global cause of death is coronary artery disease (CAD), necessitating early and precise diagnosis. Intravascular ultrasound (IVUS) is a sophisticated imaging technique that provides detailed visualization of coronary arteries. However, the methods for segmenting walls in the IVUS scan into internal wall structures and quantifying plaque are still evolving. This study explores the use of transformers and attention-based models to improve diagnostic accuracy for wall segmentation in IVUS scans. Thus, the objective is to explore the application of transformer models for wall segmentation in IVUS scans to assess their inherent biases in artificial intelligence systems for improving diagnostic accuracy. Methods: By employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, we pinpointed and examined the top strategies for coronary wall segmentation using transformer-based techniques, assessing their traits, scientific soundness, and clinical relevancy. Coronary artery wall thickness is determined by using the boundaries (inner: lumen-intima and outer: media-adventitia) through cross-sectional IVUS scans. Additionally, it is the first to investigate biases in deep learning (DL) systems that are associated with IVUS scan wall segmentation. Finally, the study incorporates explainable AI (XAI) concepts into the DL structure for IVUS scan wall segmentation. Findings: Because of its capacity to automatically extract features at numerous scales in encoders, rebuild segmented pictures via decoders, and fuse variations through skip connections, the UNet and transformer-based model stands out as an efficient technique for segmenting coronary walls in IVUS scans. Conclusions: The investigation underscores a deficiency in incentives for embracing XAI and pruned AI (PAI) models, with no UNet systems attaining a bias-free configuration. Shifting from theoretical study to practical usage is crucial to bolstering clinical evaluation and deployment.

Supervised segmentation for guiding peripheral revascularization with forward-viewing, robotically steered ultrasound guidewire

BACKGROUND

Approximately 500 000 patients present with critical limb ischemia (CLI) each year in the U.S., requiring revascularization to avoid amputation. While peripheral arteries can be revascularized via minimally invasive procedures, 25% of cases with chronic total occlusions are unsuccessful due to inability to route the guidewire beyond the proximal occlusion. Improvements to guidewire navigation would lead to limb salvage in a greater number of patients.

PURPOSE

Integrating ultrasound imaging into the guidewire could enable direct visualization of routes for guidewire advancement. In order to navigate a robotically-steerable guidewire with integrated imaging beyond a chronic occlusion proximal to the symptomatic lesion for revascularization, acquired ultrasound images must be segmented to visualize the path for guidewire advancement.

METHODS

The first approach for automated segmentation of viable paths through occlusions in peripheral arteries is demonstrated in simulations and experimentally-acquired data with a forward-viewing, robotically-steered guidewire imaging system. B-mode ultrasound images formed via synthetic aperture focusing (SAF) were segmented using a supervised approach (U-net architecture). A total of 2500 simulated images were used to train the classifier to distinguish the vessel wall and occlusion from viable paths for guidewire advancement. First, the size of the synthetic aperture resulting in the highest classification performance was determined in simulations (90 test images) and compared with traditional classifiers (global thresholding, local adaptive thresholding, and hierarchical classification). Next, classification performance as a function of the diameter of the remaining lumen (0.5 to 1.5 mm) in the partially-occluded artery was tested using both simulated (60 test images at each of 7 diameters) and experimental data sets. Experimental test data sets were acquired in four 3D-printed phantoms from human anatomy and six ex vivo porcine arteries. Accuracy of classifying the path through the artery was evaluated using microcomputed tomography of phantoms and ex vivo arteries as a ground truth for comparison.

RESULTS

An aperture size of 3.8 mm resulted in the best-performing classification based on sensitivity and Jaccard index, with a significant increase in Jaccard index (p < 0.05) as aperture diameter increased. In comparing the performance of the supervised classifier and traditional classification strategies with simulated test data, sensitivity and F1 score for U-net were 0.95 ± 0.02 and 0.96 ± 0.01, respectively, compared to 0.83 ± 0.03 and 0.41 ± 0.13 for the best-performing conventional approach, hierarchical classification. In simulated test images, sensitivity (p < 0.05) and Jaccard index both increased with increasing artery diameter (p < 0.05). Classification of images acquired in artery phantoms with remaining lumen diameters ≥ 0.75 mm resulted in accuracies > 90%, while mean accuracy decreased to 82% when artery diameter decreased to 0.5 mm. For testing in ex vivo arteries, average binary accuracy, F1 score, Jaccard index, and sensitivity each exceeded 0.9.

CONCLUSIONS

Segmentation of ultrasound images of partially-occluded peripheral arteries acquired with a forward-viewing, robotically-steered guidewire system was demonstrated for the first-time using representation learning. This could represent a fast, accurate approach for guiding peripheral revascularization.

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.

A Multiscale Approach for Predicting Certain Effects of Hand-Transmitted Vibration on Finger Arteries

Prolonged exposure to strong hand-arm vibrations can lead to vascular disorders such as Vibration White Finger (VWF). We modeled the onset of this peripheral vascular disease in two steps. The first consists in assessing the reduction in shearing forces exerted by the blood on the walls of the arteries (Wall Shear Stress—WSS) during exposure to vibrations. An acute but repeated reduction in WSS can lead to arterial stenosis characteristic of VWF. The second step is devoted to using a numerical mechano-biological model to predict this stenosis as a function of WSS. WSS is reduced by a factor of 3 during exposure to vibration of 40 m·s−2. This reduction is independent of the frequency of excitation between 31 Hz and 400 Hz. WSS decreases logarithmically when the amplitude of the vibration increases. The mechano-biological model simulated arterial stenosis of 30% for an employee exposed for 4 h a day for 10 years. This model also highlighted the chronic accumulation of matrix metalloproteinase 2. By considering daily exposure and the vibratory level, we can calculate the degree of stenosis, thus that of the disease for chronic exposure to vibrations.

Quantifying Patient-Specific in vivo Coronary Plaque Material Properties for Accurate Stress/Strain Calculations: An IVUS-Based Multi-Patient Study

Introduction: Mechanical forces are closely associated with plaque progression and rupture. Precise quantifications of biomechanical conditions using in vivo image-based computational models depend heavily on the accurate estimation of patient-specific plaque mechanical properties. Currently, mechanical experiments are commonly performed on ex vivo cardiovascular tissues to determine plaque material properties. Patient-specific in vivo coronary material properties are scarce in the existing literature.

Methods:In vivo Cine intravascular ultrasound and virtual histology intravascular ultrasound (IVUS) slices were acquired at 20 plaque sites from 13 patients. A three-dimensional thin-slice structure-only model was constructed for each slice to obtain patient-specific in vivo material parameter values following an iterative scheme. Effective Young's modulus (YM) was calculated to indicate plaque stiffness for easy comparison purposes. IVUS-based 3D thin-slice models using in vivo and ex vivo material properties were constructed to investigate their impacts on plaque wall stress/strain (PWS/PWSn) calculations.

Results: The average YM values in the axial and circumferential directions for the 20 plaque slices were 599.5 and 1,042.8 kPa, respectively, 36.1% lower than those from published ex vivo data. The YM values in the circumferential direction of the softest and stiffest plaques were 103.4 and 2,317.3 kPa, respectively. The relative difference of mean PWSn on lumen using the in vivo and ex vivo material properties could be as high as 431%, while the relative difference of mean PWS was much lower, about 3.07% on average.

Conclusion: There is a large inter-patient and intra-patient variability in the in vivo plaque material properties. In vivo material properties have a great impact on plaque stress/strain calculations. In vivo plaque material properties have a greater impact on strain calculations. Large-scale-patient studies are needed to further verify our findings.

A new nonparametric statistical approach to detect lumen and Media-Adventitia borders in intravascular ultrasound frames

Intravascular ultrasound (IVUS) imaging is widely known as a powerful interventional imaging modality for diagnosing atherosclerosis, and for treatment planning. In this regard, the detection of lumen and media-adventitia (MA) borders is considered to be a vital process. However, the manual detection of these two borders by the physician is cumbersome due to the large number of frames in a sequence. In addition, no approved universal automatic method has been presented so far due to the great diversity in the appearance of the coronary artery in the images acquired by different IVUS systems. To this end, the present study aimed to provide a new border search theory on the radial profile, based upon the nonparametric statistical approach, and to develop a generic and fully automatic three-step process for extracting the lumen and MA borders in IVUS frames based on the proposed theory. Thereafter, the proposed theory and three-step process were evaluated on synthetic images, as well as on a test set of standard publicly available images, respectively. The results showed that our three-step process could segment the borders with ≥0.82 and with ≥0.75 Jaccard measure (JM) to manual borders in IVUS frames acquired by the 20 MHz and 40 MHz probes, respectively. Based on the results, the lumen and MA borders can be extracted automatically, and the border extraction process can be implemented in parallel for a polar image due to the capability of the present proposed method to estimate the borders for each angle independently.

Thin Cap Fibroatheroma Detection in Virtual Histology Images Using Geometric and Texture Features

Atherosclerotic plaque rupture is the most common mechanism responsible for a majority of sudden coronary deaths. The precursor lesion of plaque rupture is thought to be a thin cap fibroatheroma (TCFA), or “vulnerable plaque”. Virtual Histology-Intravascular Ultrasound (VH-IVUS) images are clinically available for visualising colour-coded coronary artery tissue. However, it has limitations in terms of providing clinically relevant information for identifying vulnerable plaque. The aim of this research is to improve the identification of TCFA using VH-IVUS images. To more accurately segment VH-IVUS images, a semi-supervised model is developed by means of hybrid K-means with Particle Swarm Optimisation (PSO) and a minimum Euclidean distance algorithm (KMPSO-mED). Another novelty of the proposed method is fusion of different geometric and informative texture features to capture the varying heterogeneity of plaque components and compute a discriminative index for TCFA plaque, while the existing research on TCFA detection has only focused on the geometric features. Three commonly used statistical texture features are extracted from VH-IVUS images: Local Binary Patterns (LBP), Grey Level Co-occurrence Matrix (GLCM), and Modified Run Length (MRL). Geometric and texture features are concatenated in order to generate complex descriptors. Finally, Back Propagation Neural Network (BPNN), kNN (K-Nearest Neighbour), and Support Vector Machine (SVM) classifiers are applied to select the best classifier for classifying plaque into TCFA and Non-TCFA. The present study proposes a fast and accurate computer-aided method for plaque type classification. The proposed method is applied to 588 VH-IVUS images obtained from 10 patients. The results prove the superiority of the proposed method, with accuracy rates of 98.61% for TCFA plaque.

Automated in vivo delineation of lumen wall using intravascular ultrasound imaging

This paper presents a novel methodology for automated detection and extraction of the lumen wall from Intravascular Ultrasound (IVUS) frames. IVUS is an in-vivo pull back imaging technique and provides a sequential frame of images for diagnosis of atherosclerotic heart disease. The detection and segmentation of lumen wall is necessary for predicting the arterial wall blockage. Lumen wall is recognized and segmented with the help of seed refinement and random walks algorithms, in tunica and lumen area. The proposed methodology was tested on 147 frames of 13 patients. Proposed method achieves significant performances for automated lumen wall detection and extraction as compared with existing literature.

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.

Currently available methodologies for the processing of intravascular ultrasound and optical coherence tomography images

Optical coherence tomography and intravascular ultrasound are the most widely used methodologies in clinical practice as they provide high resolution cross-sectional images that allow comprehensive visualization of the lumen and plaque morphology. Several methods have been developed in recent years to process the output of these imaging modalities, which allow fast, reliable and reproducible detection of the luminal borders and characterization of plaque composition. These methods have proven useful in the study of the atherosclerotic process as they have facilitated analysis of a vast amount of data. This review presents currently available intravascular ultrasound and optical coherence tomography processing methodologies for segmenting and characterizing the plaque area, highlighting their advantages and disadvantages, and discusses the future trends in intravascular imaging.

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.

Improving quantification of intravascular fluorescence imaging using structural information

Intravascular near-infrared fluorescence (iNIRF) imaging can enable the in vivo visualization of biomarkers of vascular pathology, including high-risk plaques. The technique resolves the bio-distribution of systemically administered fluorescent probes with molecular specificity in the vessel wall. However, the geometrical variations that may occur in the distance between fibre-tip and vessel wall can lead to signal intensity variations and challenge quantification. Herein we examined whether the use of anatomical information of the cross-section vessel morphology, obtained from co-registered intravascular ultrasound (IVUS), can lead to quantification improvements when fibre-tip and vessel wall distance variations are present. The algorithm developed employs a photon propagation model derived from phantom experiments that is used to calculate the relative attenuation of fluorescence signals as they are collected over 360° along the vessel wall, and utilizes it to restore accurate fluorescence readings. The findings herein point to quantification improvements when employing hybrid iNIRF, with possible implications to the clinical detection of high-risk plaques or blood vessel theranostics.

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.

Automated tracing of the adventitial contour of aortoiliac and peripheral arterial walls in CT angiography (CTA) to allow calculation of non-calcified plaque burden

Aortoiliac and lower extremity arterial atherosclerotic plaque burden is a risk factor for the development of visceral and peripheral ischemic and aneurismal vascular disease. While prior research allows automated quantification of calcified plaque in these body regions using CT angiograms, no automated method exists to quantify soft plaque. We developed an automatic algorithm that defines the outer wall contour and wall thickness of vessels to quantify non-calcified plaque in CT angiograms of the chest, abdomen, pelvis, and lower extremities. The algorithm encodes the search space as a constrained graph and calculates the outer wall contour by deriving a minimum cost path through the graph, following the visible outer wall contour while minimizing path tortuosity. Our algorithm was statistically equivalent to a reference standard made by two reviewers. Absolute error was 1.9 ± 2.3% compared to the inter-observer variability of 3.9 ± 3.6%. Wall thickness in vessels with atherosclerosis was 3.4 ± 1.6 mm compared to 1.2 ± 0.4 mm in normal vessels. The algorithm shows promise as a tool for quantification of non-calcified plaque in CT angiography. When combined with previous research, our method has the potential to quantify both non-calcified and calcified plaque in all clinically significant systemic arteries, from the thoracic aorta to the arteries of the calf, over a wide range of diameters. This algorithm has the potential to enable risk stratification of patients and facilitate investigations into the relationships between asymptomatic atherosclerosis and a variety of behavioral, physiologic, pathologic, and genotypic conditions.

A Parallel Method for Segmenting Intravascular Ultrasound Image Sequence

Intravascular Ultrasound (IVUS) is one of interventional imaging modalities widely used in clinical diagnosis of vascular diseases, especially coronary artery diseases. Segmentation of IVUS images to extract vessel wall boundaries is of importance for quantitative analysis and 3D vessel reconstruction. A 3D parallel method for segmenting IVUS image sequence is proposed in this paper. Firstly, original images are preprocessed to reduce possible noises and eliminate ring-down artifacts. Then, several longitudinal cuts are obtained and intima-lumen and media-adventitia boundaries are detected. Once these boundaries are mapped onto each cross-sectional slice, initial plan of vessel wall boundaries in each frame is obtained. Finally, these initial contours evolve continuously until stop at target contours. Consequently, segmentation of each IVUS tomographic frame is implemented simultaneously and the efficiency is greatly raised compared with 2D sequential approaches.

A snake-based method for segmentation of intravascular ultrasound images and its in vivo validation

Image segmentation for detection of vessel walls is necessary for quantitative assessment of vessel diseases by intravascular ultrasound. A new segmentation method based on gradient vector flow (GVF) snake model is proposed in this paper. The main characteristics of the proposed method include two aspects: one is that nonlinear filtering is performed on GVF field to reduce the critical points, change the morphological structure of the parallel curves and extend the capture range; the other is that balloon snake is combined with the model. Thus, the improved GVF and balloon snake can be automatically initialized and overcome the problem caused by local energy minima. Results of 20 in vivo cases validated the accuracy and stability of the segmentation method for intravascular ultrasound images.

Segmentation of ultrasound images of the carotid using RANSAC and cubic splines

A new algorithm is proposed for the semi-automatic segmentation of the near-end and the far-end adventitia boundary of the common carotid artery in ultrasound images. It uses the random sample consensus method to estimate the most significant cubic splines fitting the edge map of a longitudinal section. The consensus of the geometric model (a spline) is evaluated through a new gain function, which integrates the responses to different discriminating features of the carotid boundary: the proximity of the geometric model to any edge or to valley shaped edges; the consistency between the orientation of the normal to the geometric model and the intensity gradient; and the distance to a rough estimate of the lumen boundary. A set of 50 longitudinal B-mode images of the common carotid and their manual segmentations performed by two medical experts were used to assess the performance of the method. The image set was taken from 25 different subjects, most of them having plaques of different classes (class II to class IV), sizes and shapes. The quantitative evaluation showed promising results, having detection errors similar to the ones observed in manual segmentations for 95% of the far-end boundaries and 73% of the near-end boundaries.

Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation

The performance of the level set segmentation is subject to appropriate initialization and optimal configuration of controlling parameters, which require substantial manual intervention. A new fuzzy level set algorithm is proposed in this paper to facilitate medical image segmentation. It is able to directly evolve from the initial segmentation by spatial fuzzy clustering. The controlling parameters of level set evolution are also estimated from the results of fuzzy clustering. Moreover the fuzzy level set algorithm is enhanced with locally regularized evolution. Such improvements facilitate level set manipulation and lead to more robust segmentation. Performance evaluation of the proposed algorithm was carried on medical images from different modalities. The results confirm its effectiveness for medical image segmentation.

Wall shear stress on LDL accumulation in human RCAs

The blood flow and transportation of molecules in the cardiovascular system plays crucial role in the genesis and progression of atherosclerosis. Atherosclerosis shows predilection in regions of the arterial tree with hemodynamic particularities, as local disturbances of wall shear stress in space, and locally high concentrations of lipoprotein. A semi-permeable nature of the arterial wall computational model is incorporated with hydraulic conductivity and permeability treated as wall shear stress dependent. Six image-based human diseased right coronary arteries (RCA) are used to elucidate the low-density lipoprotein (LDL) transport. The 3D reconstruction technique is a combination of angiography and IVUS. The numerical simulation couples the flow equations with the transport equation applying realistic boundary conditions at the wall. The coupling of fluid dynamics and solute dynamics at the endothelium is achieved by the Kedem-Katchalsky equation (water infiltration). The luminal surface LDL concentration at the arterial wall is flow-dependent with local variations due to geometric features. The relationship between WSS and luminal surface concentration of LDL indicates that LDL is elevated at locations where WSS is low. There is medium correlation (Pearson) between low WSS and high LDL. The degree of elevation in luminal surface LDL concentration is mostly affected by the water infiltration velocity at the vessel wall. Under constant water infiltration the shear dependent endothelial permeability effects, in comparison to those using constant value, are marginal. Area-averaged normalized LDL concentration over the RCAs, using constant water infiltration and endothelial permeability is 3.6% higher than that at the entrance. Area-averaged normalized LDL concentration over the RCAs, using shear dependent water infiltration and endothelial permeability is 9.6%. Perspective computational fluid dynamics users, incorporating mass transfer (LDL) within the blood flow, are forced to treat the problem using shear dependent endothelial values.

Image analysis techniques for automated IVUS contour detection

Intravascular ultrasound (IVUS) constitutes a valuable technique for the diagnosis of coronary atherosclerosis. The detection of lumen and media-adventitia borders in IVUS images represents a necessary step towards the reliable quantitative assessment of atherosclerosis. In this work, a fully automated technique for the detection of lumen and media-adventitia borders in IVUS images is presented. This comprises two different steps for contour initialization: one for each corresponding contour of interest and a procedure for the refinement of the detected contours. Intensity information, as well as the result of texture analysis, generated by means of a multilevel discrete wavelet frames decomposition, are used in two different techniques for contour initialization. For subsequently producing smooth contours, three techniques based on low-pass filtering and radial basis functions are introduced. The different combinations of the proposed methods are experimentally evaluated in large datasets of IVUS images derived from human coronary arteries. It is demonstrated that our proposed segmentation approaches can quickly and reliably perform automated segmentation of IVUS images.

In vivo comparative study of linear versus geometrically correct three-dimensional reconstruction of coronary arteries

Although conventional linear 3-dimensional (3D) reconstruction of coronary arteries by intravascular ultrasound has been widely used for the assessment of plaque volume and progression; the volumetric error (VE) that is produced has not been adequately studied. Linear and geometrically correct 3D reconstruction was applied in 16 coronary arterial segments from 9 patients. Using geometrically correct reconstruction as reference, VE was assessed in 1-mm-long arterial slices. Although for the entire length of the coronary arteries VEs for lumen, external elastic membrane (EEM), and intima-media volumes were minimal (lumen VE 0.4%, -0.8 to 1.8; EEM VE 0.3%, -0.9 to 1.9; intima-media VE 0.4%, -1.4 to 2.2), the VE in each arterial slice exhibited a large variation from -15.6% to 36.2% for lumen volume, from -12.9% to 33.1% for EEM volume, and from -17.2% to 46.7% for intima-media volume, suggesting that linear reconstruction over- or underestimates the true arterial volumes. Lumen VE, EEM VE, and intima-media VE were also significantly higher in curved arterial subsegments than in relatively straight arterial subsegments (p <0.05). In conclusion, in highly curved arterial subsegments, the VE that is produced by linearly stacking the intravascular ultrasound images may be not negligible. Geometrically correct reconstruction of coronary arteries provides more reliable arterial reconstructions and plaque volume measurements. It is anticipated that clinical application of this technique will contribute to more accurate follow-up of the progression of atherosclerosis and assessment of arterial remodeling.

Texture Analysis and Radial Basis Function Approximation for IVUS Image Segmentation

>Intravascular ultrasound (IVUS) has become in the last years an important tool in both clinical and research applications. The detection of lumen and media-adventitia borders in IVUS images represents a first necessary step in the utilization of the IVUS data for the 3D reconstruction of human coronary arteries and the reliable quantitative assessment of the atherosclerotic lesions. To serve this goal, a fully automated technique for the detection of lumen and media-adventitia boundaries has been developed. This comprises two different steps for contour initialization, one for each corresponding contour of interest, based on the results of texture analysis, and a procedure for approximating the initialization results with smooth continuous curves. A multilevel Discrete Wavelet Frames decomposition is used for texture analysis, whereas Radial Basis Function approximation is employed for producing smooth contours. The proposed method shows promising results compared to a previous approach for texture-based IVUS image analysis.