Intravascular Ultrasound Frames Research Articles

Automated Classification of Coronary Plaque on Intravascular Ultrasound by Deep Classifier Cascades.

Intravascular ultrasound (IVUS) is the gold standard modality for in vivo visualization of coronary arteries and atherosclerotic plaques. Classification of coronary plaques helps to characterize heterogeneous components and evaluate the risk of plaque rupture. Manual classification is time-consuming and labor-intensive. Several machine learning-based classification approaches have been proposed and evaluated in recent years. In the current study, we develop a novel pipeline composed of serial classifiers for distinguishing IVUS images into five categories: normal, calcified plaque, attenuated plaque, fibrous plaque, and echolucent plaque. The cascades comprise densely connected classification models and machine learning classifiers at different stages. Over 100,000 IVUS frames of five different lesion types were collected and labeled from 471 patients for model training and evaluation. The overall accuracy of the proposed classifier is 0.877, indicating that the proposed framework has the capacity to identify the nature and category of coronary plaques in IVUS images. Further, it may provide real-time assistance on plaque identification and facilitate clinical decision-making in routine practice.

Deep Learning-Based Lumen and Vessel Segmentation of Intravascular Ultrasound Images in Coronary Artery Disease.

Intravascular ultrasound (IVUS) evaluation of coronary artery morphology is based on the lumen and vessel segmentation. This study aimed to develop an automatic segmentation algorithm and validate the performances for measuring quantitative IVUS parameters. A total of 1,063 patients were randomly assigned, with a ratio of 4:1 to the training and test sets. The independent data set of 111 IVUS pullbacks was obtained to assess the vessel-level performance. The lumen and external elastic membrane (EEM) boundaries were labeled manually in every IVUS frame with a 0.2-mm interval. The Efficient-UNet was utilized for the automatic segmentation of IVUS images. At the frame-level, Efficient-UNet showed a high dice similarity coefficient (DSC, 0.93±0.05) and Jaccard index (JI, 0.87±0.08) for lumen segmentation, and demonstrated a high DSC (0.97±0.03) and JI (0.94±0.04) for EEM segmentation. At the vessel-level, there were close correlations between model-derived vs. experts-measured IVUS parameters; minimal lumen image area (r=0.92), EEM area (r=0.88), lumen volume (r=0.99) and plaque volume (r=0.95). The agreement between model-derived vs. expert-measured minimal lumen area was similarly excellent compared to the experts' agreement. The model-based lumen and EEM segmentation for a 20-mm lesion segment required 13.2 seconds, whereas manual segmentation with a 0.2-mm interval by an expert took 187.5 minutes on average. The deep learning models can accurately and quickly delineate vascular geometry. The artificial intelligence-based methodology may support clinicians' decision-making by real-time application in the catheterization laboratory.

Contrast Venography Versus Intravenous Ultrasound in Hemodialysis Arteriovenous Access Dysfunction

Contrast Venography Versus Intravenous Ultrasound in Hemodialysis Arteriovenous Access Dysfunction

An All-in-One Tool for 2D Atherosclerotic Disease Assessment and 3D Coronary Artery Reconstruction.

Diagnosis of coronary artery disease is mainly based on invasive imaging modalities such as X-ray angiography, intravascular ultrasound (IVUS) and optical coherence tomography (OCT). Computed tomography coronary angiography (CTCA) is also used as a non-invasive imaging alternative. In this work, we present a novel and unique tool for 3D coronary artery reconstruction and plaque characterization using the abovementioned imaging modalities or their combination. In particular, image processing and deep learning algorithms were employed and validated for the lumen and adventitia borders and plaque characterization at the IVUS and OCT frames. Strut detection is also achieved from the OCT images. Quantitative analysis of the X-ray angiography enables the 3D reconstruction of the lumen geometry and arterial centerline extraction. The fusion of the generated centerline with the results of the OCT or IVUS analysis enables hybrid coronary artery 3D reconstruction, including the plaques and the stent geometry. CTCA image processing using a 3D level set approach allows the reconstruction of the coronary arterial tree, the calcified and non-calcified plaques as well as the detection of the stent location. The modules of the tool were evaluated for efficiency with over 90% agreement of the 3D models with the manual annotations, while a usability assessment using external evaluators demonstrated high usability resulting in a mean System Usability Scale (SUS) score equal to 0.89, classifying the tool as "excellent".

3D pyramidal densely connected network with cross-frame uncertainty guidance for intravascular ultrasound sequence segmentation

Objective. Automatic extraction of external elastic membrane border (EEM) and lumen-intima border (LIB) in intravascular ultrasound (IVUS) sequences aids atherosclerosis diagnosis. Existing IVUS segmentation networks ignored longitudinal relations among sequential images and neglected that IVUS images of different vascular conditions vary largely in intricacy and informativeness. As a result, they suffered from performance degradation in complicated parts in IVUS sequences. Approach. In this paper, we develop a 3D Pyramidal Densely-connected Network (PDN) with Adaptive learning and post-Correction guided by a novel cross-frame uncertainty (CFU). The proposed method is named PDN-AC. Specifically, the PDN enables the longitudinal information exploitation and the effective perception of size-varied vessel regions in IVUS samples, by pyramidally connecting multi-scale 3D dilated convolutions. Additionally, the CFU enhances the robustness of the method to complicated pathology from the frame-level (f-CFU) and pixel-level (p-CFU) via exploiting cross-frame knowledge in IVUS sequences. The f-CFU weighs the complexity of IVUS frames and steers an adaptive sampling during the PDN training. The p-CFU visualizes uncertain pixels probably misclassified by the PDN and guides an active contour-based post-correction. Main results. Human and animal experiments were conducted on IVUS datasets acquired from atherosclerosis patients and pigs. Results showed that the f-CFU weighted adaptive sampling reduced the Hausdorff distance (HD) by 10.53%/7.69% in EEM/LIB detection. Improvements achieved by the p-CFU guided post-correction were 2.94%/5.56%. Significance. The PDN-AC attained mean Jaccard values of 0.90/0.87 and HD values of 0.33/0.34 mm in EEM/LIB detection, preferable to state-of-the-art IVUS segmentation methods.

Abstract 14690: Deep Learning Assisted Target Lesion Analysis Using Advanced Border Detection Algorithm of Intravascular Ultrasound

The vessel-plaque interface detection and lesion length of coronary artery disease are still challenging using the deep learning with intravascular ultrasound (IVUS). We used AI-based image segmentation technology to develop a deep learning model for classifying frames with IVUS-derived target lesion. Methods. A total of 3,782 target lesion frames from 28 acute coronary syndrome(ACS) patients were randomized into training and test sets in a 2:1 ratio. The training algorithm was based on the measurement of the core lab. A segmentation model using non-local blocks was used to increase the similarity between pixels of the same class in a U-Net-based model. The edge detection was performed on the region of interest, and the detected edge was used as an aid in deriving the final segmentation result. And the morphological guided measurements were used to provide the information about the vessel area, lumen area, plaque burden and lesion length. Quantitative evaluation was conducted on four scales: Jaccard Measure (JM), Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), and Percentage of Area Difference (PAD). And we compared the diagnostic accuracy to detect the vessel-plaque interface of the target lesion. Results. In the training sample (1,256 IVUS frames of target lesion), and the test samples (1,072 IVUS frames of target lesion) were used for the proposed model. The external elastic membrane area (EEM), lumen area, plaque area and thrombus were assessed with the proposed algorithm. The measurement of EEM was DSC 0.99, JM 0.98, HD 0.06, and PAD 0.01. And the lumen also shows excellent performance with DSC 0.98, JM 0.97, HD 0.08, and PAD 0.01. In addition, the proposed model was compared with ground-truth for Thrombus, and showed high values of 0.88 for DSC, 0.83 for JM, 0.21 for HD, and 0.09 for PAD. Conclusion. An advanced algorithm can accurately detect the vessel-plaque interface and estimated stent length of coronary artery disease with high reproducibility. F

Interobserver Variability Between Expert, Experienced, and Novice Operator Affects Interpretation of Optical Coherence Tomography and 20 MHz Intravascular Ultrasound Imaging

BackgroundVessel sizing and calcium detection by intracoronary imaging informs optimal strategy during coronary interventions, but image interpretation and analysis software vary considerably between platforms. We compared the interobserver variability of clinicians with a range of experience in assessing co-registered optical coherence tomography (OCT) and 20 MHz solid state intravascular ultrasound (IVUS) vessel/ lumen geometry and quantitative plaque data. MethodsCo-registered OCT and IVUS frames at the minimum lumen area (MLA) and 5 frames at 2 mm intervals upstream and downstream were read blinded by an expert, consultant, interventional fellow and registrar to define vessel and lumen sizes, plaque characteristics (arc of calcium and lipid) and presence of OCT-defined thin-capped fibroatheroma (TCFA). ResultsOverall, 143 paired frames of OCT and IVUS were analysed. Excellent consistency was seen for all OCT measures of vessel/luminal geometry irrespective of experience (all intraclass correlation coefficients (ICC) >0.89). Inexperience compromised ICC for IVUS (lumen area ICC 0.56; vessel size ICC 0.65) and overestimated lumen size compared to expert (p < 0.001). Calcium arc agreement for OCT and IVUS was equally strong for all but the most inexperienced reader (ICC >0.84). OCT lipid arc agreement was moderate for experienced readers but poor for all grades of reader with IVUS (ICC 0.24–0.43). OCT-TCFA agreement was moderate between expert and consultant (κ = 0.55) and poor for less experienced readers. ConclusionOCT dimensions are minimally affected by observer experience and more consistent than IVUS. Inexperienced readers oversize with IVUS. Calcium arc is dependably assessed by IVUS and OCT by all but the most inexperienced reader.

Cascaded learning in intravascular ultrasound: coronary stent delineation in manual pullbacks.

Purpose: Implanting stents to re-open stenotic lesions during percutaneous coronary interventions is considered a standard treatment for acute or chronic coronary syndrome. Intravascular ultrasound (IVUS) can be used to guide and assess the technical success of these interventions. Automatically segmenting stent struts in IVUS sequences improves workflow efficiency but is non-trivial due to a challenging image appearance entailing manifold ambiguities with other structures. Manual, ungated IVUS pullbacks constitute a challenge in this context. We propose a fully data-driven strategy to first longitudinally detect and subsequently segment stent struts in IVUS frames. Approach: A cascaded deep learning approach is presented. It first trains an encoder model to classify frames as "stent," "no stent," or "no use." A segmentation model then delineates stent struts on a pixel level only in frames with a stent label. The first stage of the cascade acts as a gateway to reduce the risk for false positives in the second stage, the segmentation, which is trained on a smaller and difficult-to-annotate dataset. Training of the classification and segmentation model was based on 49,888 and 1826 frames of 74 sequences from 35 patients, respectively. Results: The longitudinal classification yielded Dice scores of 92.96%, 82.35%, and 94.03% for the classes stent, no stent, and no use, respectively. The segmentation achieved a Dice score of 65.1% on the stent ground truth (intra-observer performance: 75.5%) and 43.5% on all frames (including frames without stent, with guidewires, calcium, or without clinical use). The latter improved to 49.5% when gating the frames by the classification decision and further increased to 57.4% with a heuristic on the plausible stent strut area. Conclusions: A data-driven strategy for segmenting stents in ungated, manual pullbacks was presented-the most common and practical scenario in the time-critical clinical workflow. We demonstrated a mitigated risk for ambiguities and false positive predictions.

Machine learning for atherosclerotic tissue component classification in combined near-infrared spectroscopy intravascular ultrasound imaging: Validation against histology

Background and aimsAccurate classification of plaque composition is essential for treatment planning. Intravascular ultrasound (IVUS) has limited efficacy in assessing tissue types, while near-infrared spectroscopy (NIRS) provides complementary information to IVUS but lacks depth information. The aim of this study is to train and assess the efficacy of a machine learning classifier for plaque component classification that relies on IVUS echogenicity and NIRS-signal, using histology as reference standard. MethodsMatched NIRS-IVUS and histology images from 15 cadaveric human coronary arteries were analyzed (10 vessels were used for training and 5 for testing). Fibrous/pathological intimal thickening (F-PIT), early necrotic core (ENC), late necrotic core (LNC), and calcific tissue regions-of-interest were detected on histology and superimposed onto IVUS frames. The pixel intensities of these tissue types from the training set were used to train a J48 classifier for plaque characterization (ECHO-classification). To aid differentiation of F-PIT from necrotic cores, the NIRS-signal was used to classify non-calcific pixels outside yellow-spot regions as F-PIT (ECHO-NIRS classification). The performance of ECHO and ECHO-NIRS classifications were validated against histology. Results262 matched frames were included in the analysis (162 constituted the training set and 100 the test set). The pixel intensities of F-PIT and ENC were similar and thus these two tissues could not be differentiated by echogenicity. With ENC and LNC as a single class, ECHO-classification showed good agreement with histology for detecting calcific and F-PIT tissues but had poor efficacy for necrotic cores (recall 0.59 and precision 0.29). Similar results were found when F-PIT and ENC were treated as a single class (recall and precision for LNC 0.78 and 0.33, respectively). ECHO-NIRS classification improved necrotic core and LNC detection, resulting in an increase of the overall accuracy of both models, from 81.4% to 91.8%, and from 87.9% to 94.7%, respectively. Comparable performance of the two models was seen in the test set where the overall accuracy of ECHO-NIRS classification was 95.0% and 95.5%, respectively. ConclusionsThe combination of echogenicity with NIRS-signal appears capable of overcoming limitations of echogenicity, enabling more accurate characterization of plaque components.

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.

First in‐human evaluation of a novel intravascular ultrasound and optical coherence tomography system for intracoronary imaging

AbstractAimsWe evaluated the first in‐human performance of a novel hybrid imaging catheter that permits simultaneous and co‐registered acquisition of intravascular ultrasound (IVUS) and optical coherence tomography (OCT) images.Methods and ResultsA total of 17 patients undergoing planned percutaneous coronary intervention (PCI) were imaged between August 2018 and August 2019. Eleven patients with both pre‐ and post‐PCI IVUS and OCT images were included in the offline image analysis. IVUS and OCT images were analyzed separately then together with co‐registered images for pre‐stent findings, and only separately for post‐stent findings. A total of 926 frames were analyzed (218 pre‐PCI, 708 post‐PCI). There was substantial agreement to detect calcific plaque between co‐registered IVUS‐OCT and standalone IVUS (Kappa 0.72 [0.65–0.79]) and standalone OCT (Kappa 0.75 [0.68–0.81]) while standalone imaging modalities showed lower agreement to detect lipidic and fibrotic plaques compared with co‐registered IVUS‐OCT. There were more frames with stent underexpansion on IVUS than OCT [72 (28.7%) vs. 58 (23.1%), respectively, p = 0.039]. Detection rates of incomplete stent apposition (present on 20 OCT frames vs. 2 IVUS frames, p &lt; 0.001) and tissue protrusion (40 vs. 27 frames, p &lt; 0.001) were higher on OCT than IVUS. One stent edge dissection was detected in the image analysis and was seen on OCT but not IVUS. All 177 frames with image artifacts contained at least one co‐registered imaging modality with interpretable diagnostic content. There were no study device‐related adverse events.ConclusionsHybrid image acquisition was safe. The availability of both IVUS and OCT changed image interpretation compared to either modality alone, suggesting a complementary role of these two techniques.

End-diastolic segmentation of intravascular ultrasound images enables more reproducible volumetric analysis of atheroma burden.

Volumetric intravascular ultrasound (IVUS) analysis is currently performed at a fixed frame interval, neglecting the cyclic changes in vessel dimensions occurring during the cardiac cycle that can affect the reproducibility of the results. Analysis of end-diastolic (ED) IVUS frames has been proposed to overcome this limitation. However, at present, there is lack of data to support its superiority over conventional IVUS. The present study aims to compare the reproducibility of IVUS volumetric analysis performed at a fixed frame interval and at the ED frames, identified retrospectively using a novel deep-learning methodology. IVUS data acquired from 97 vessels were included in the present study; each vessel was segmented at 1 mm interval (conventional approach) and at ED frame twice by an expert analyst. Reproducibility was tested for the following metrics; normalized lumen, vessel and total atheroma volume (TAV), and percent atheroma volume (PAV). The mean length of the analyzed segments was 50.0 ± 24.1 mm. ED analysis was more reproducible than the conventional analysis for the normalized lumen (mean difference: 0.76 ± 4.03 mm3 vs. 1.72 ± 11.37 mm3 ; p for the variance of differences ratio < 0.001), vessel (0.30 ± 1.79 mm3 vs. -0.47 ± 10.26 mm3 ; p < 0.001), TAV (-0.46 ± 4.03 mm3 vs. -2.19 ± 14.39 mm3 ; p < 0.001) and PAV (-0.12 ± 0.59% vs. -0.34 ± 1.34%; p < 0.001). Results were similar when the analysis focused on the 10 mm most diseased segment. The superiority of the ED approach was due to a more reproducible detection of the segment of interest and to the fact that it was not susceptible to the longitudinal motion of the IVUS probe and the cyclic changes in vessel dimensions during the cardiac cycle. ED IVUS segmentation enables more reproducible volumetric analysis and quantification of TAV and PAV that are established end points in longitudinal studies assessing the efficacy of novel pharmacotherapies. Therefore, it should be preferred over conventional IVUS analysis as its higher reproducibility is expected to have an impact on the sample size calculation for the primary end point.

Extraction of media adventitia and luminal intima borders by reconstructing intravascular ultrasound image sequences with vascular structural continuity.

Most published methods directly achieve vessel membrane border detection on cross-sectional intravascular ultrasound (IVUS) images. The vascular structural continuity that exists in entire IVUS image sequences has been overlooked. However, this continuity can have a helpful role in the delineation of vessel membrane contours. To achieve the vessel membrane segmentation more effectively through employing this continuity, a strategy, referred to as multiangle reconstruction, segmentation, and recovery (RSR), is proposed in this paper. Four main steps are contained in the multiangle-RSR: first, a combination of sampling and interpolation is employed to reconstruct long-axis-model IVUS frames, in which continuity information becomes available. Second, a clustering algorithm is conducted on long-axis-model IVUS frames to roughly extract the media-adventitia (MA) and lumen-intima (LI) boundaries. Third, the segmentation results of cross-sectional IVUS frames are recovered based on the rough results of long-axis-model IVUS frames, and an optimization process that combines downsampling, fitting and smoothing is designed to reduce the interference of bifurcation and side vessels. Multiangle-RSR is tested on a public dataset, and the Hausdorff distance (HD), Jaccard measure (JM), and percentage of area difference (PAD) are utilized as quantitative evaluation metrics. Mean HDs of 0.34 and 0.29mm are obtained for MA border detection and LI border detection, respectively, which decrease by 43.3% and 9.4%, respectively, compared with their counterparts in previously published approaches. Furthermore, the mean JM is 0.87 for both MA border detection and LI border detection. The mean PADs of the MA contour extraction and the LI contour extraction are 0.10 and 0.11, respectively. The results indicate that the proposed strategy effectively introduces vascular structural continuity by reconstructing long-axis-model IVUS frames and achieves more precise extraction of MA and LI borders.

Intravascular ultrasound-based deep learning for plaque characterization in coronary artery disease

Background and aimsAlthough plaque characterization by intravascular ultrasound (IVUS) is important for risk stratification, frame-by-frame analysis of a whole vascular segment is time-consuming. The aim was to develop IVUS-based algorithms for classifying attenuation and calcified plaques. MethodsIVUS image sets of 598 coronary arteries from 598 patients were randomized into training and test sets with 5:1 ratio. Each IVUS frame at a 0.4-mm interval was circumferentially labeled as one of three classes: attenuated plaque, calcified plaque, or plaque without attenuation or calcification. The model was trained on multi-class classification with 5-fold cross validation. By converting from Cartesian to polar coordinate images, the class corresponding to each array from 0 to 360° was plotted. ResultsAt the angle-level, Dice similarity coefficients for identifying calcification vs. attenuation vs. none by using ensemble model were 0.79, 0.74 and 0.99, respectively. Also, the maximal accuracy was 98% to classify those groups in the test set. At the frame-level, the model identified the presence of attenuation with 80% sensitivity, 96% specificity, and 93% overall accuracy, and the presence of calcium with 86% sensitivity, 97% specificity, and 96% overall accuracy. In the per-vessel analysis, the attenuation and calcification burden index closely correlated with human measurements (r = 0.89 and r = 0.95, respectively), as did the maximal attenuation and calcification burden index over 4 mm (r = 0.82 and r = 0.91, respectively). The inference times were 0.05 s per frame and 7.8 s per vessel. ConclusionsOur deep learning algorithms for plaque characterization may assist clinicians in recognizing high-risk coronary lesions.

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

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

IVUS images segmentation using spatial fuzzy clustering and hierarchical level set evolution

The detection of the lumen and media-adventitia (MA) borders in intravascular ultrasound (IVUS) images is crucial for quantifying plaque burdens. The challenge of the segmentation work mainly roots in various artifacts in the image. Most of the published methods involve the establishment of complex models but do not behave well on images with artifacts. In this study, aiming at automatically delineating borders in IVUS frames acquired by 20 MHz ultrasound probes, we present a fuzzy clustering-initialized hierarchical level set evolution (FC-HLSE) method. A cluster selection strategy based on the spatial fuzzy c-means (FCM) is proposed to generate the initial value and regularization term of the level set evolution (LSE). The contour convergence splits into two LSE steps between which an ingenious contour extraction (consisting of the morphological processing, the seek and linear interpolation, the gradient-based and circular fitting-based refinement) is carried out. We evaluate the proposed methodology on the publicly available 435 images by comparing auto-segmented results with the ground truth. The performance of the method is quantified using the Jaccard measure (JM), the Hausdorff distance (HD), the percentage of area difference (PAD), the linear regression and Bland-Altman analysis. Results reveal that our method can handle images with or without artifacts. The algorithm is able to extract the lumen/MA border with the JM of 0.90/0.89, the HD of 0.31/0.40 mm, the PAD of 0.07/0.08 in average, which is better in some cases compared with several state-of-the-art methods.

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.

Segmentation of the lumen and media‐adventitial borders in intravascular ultrasound images using a geometric deformable model

This study presents a geometric deformable model-based segmentation approach to segmentation of the intima and media-adventitial (MA) borders in sequential intravascular ultrasound (IVUS) images. The initial estimation of the vessel borders was done manually only for the first frame of each sequence. After the border initialisation, pre-processing including edge preservation, noise reduction, and dead zone preservation was successively performed on each IVUS frame. To improve segmentation performance, the image masks were determined preliminarily by local binary pattern-based mask initialisation. Then, the inner and outer borders were approximated using a modified distance regularised level set evolution model. The results showed superior performance of the suggested approach for estimating intima and MA layers from the IVUS images. The corresponding correlation coefficients of area, vessel perimeter, maximum vessel diameter, and maximum lumen diameter were r = 0.782, r = 0.716, r = 0.956, and r = 0.874 for the 20 MHz images, respectively, and r = 0.990, r = 0.995, r = 0.989, and r = 0.996 for the 45 MHz images, respectively. In addition, linear regression analysis indicated that the manual segmentation had significantly high similarity at r > 0.967 and r > 0.993 for 20 and 45 MHz images, respectively.

Segmentation of arterial walls in intravascular ultrasound cross-sectional images using extremal region selection

Intravascular Ultrasound (IVUS) is an intra-operative imaging modality that facilitates observing and appraising the vessel wall structure of the human coronary arteries. Segmentation of arterial wall boundaries from the IVUS images is not only crucial for quantitative analysis of the vessel walls and plaque characteristics, but is also necessary for generating 3D reconstructed models of the artery. The aim of this study is twofold. Firstly, we investigate the feasibility of using a recently proposed region detector, namely Extremal Region of Extremum Level (EREL) to delineate the luminal and media-adventitia borders in IVUS frames acquired by 20 MHz probes. Secondly, we propose a region selection strategy to label two ERELs as lumen and media based on the stability of their textural information. We extensively evaluated our selection strategy on the test set of a standard publicly available dataset containing 326 IVUS B-mode images. We showed that in the best case, the average Hausdorff Distances (HD) between the extracted ERELs and the actual lumen and media were 0.22 mm and 0.45 mm, respectively. The results of our experiments revealed that our selection strategy was able to segment the lumen with ⩽0.3 mm HD to the gold standard even though the images contained major artifacts such as bifurcations, shadows, and side branches. Moreover, when there was no artifact, our proposed method was able to delineate media-adventitia boundaries with 0.31 mm HD to the gold standard. Furthermore, our proposed segmentation method runs in time that is linear in the number of pixels in each frame. Based on the results of this work, by using a 20 MHz IVUS probe with controlled pullback, not only can we now analyze the internal structure of human arteries more accurately, but also segment each frame during the pullback procedure because of the low run time of our proposed segmentation method.

Intravascular ultrasound assessment of the effect of laser energy on the arterial wall during the treatment of femoro-popliteal lesions: a CliRpath excimer laser system to enlarge lumen openings (CELLO) registry study

The CliRpath Excimer Laser System to Enlarge Lumen Openings (CELLO) registry included patients treated with modified excimer laser catheters for the endovascular treatment of peripheral artery disease affecting the superficial femoral artery (SFA) and proximal popliteal artery. The aim of this study was to assess, via intravascular ultrasound (IVUS) the dissections in the vessel wall following treatment with the laser catheters. IVUS grayscale images from the CELLO registry were systematically reviewed for dissections in the treated vessel segments by two investigators. Images from 33 patients; 66 pullbacks (1867 IVUS frames in 2 phases), were successfully matched frame-to-frame to evaluate identical segments of the treated vessels in the two phases; post-2 mm Turbo-Elite laser pilot channel creation and post Turbo-Booster laser atherectomy. Dissections were categorized as; (1) intimal, (2) medial, (3) intramural hematoma, and (4) adventitial according to the ACC Clinical Expert Consensus Document classification of dissections. An average of 57 frames was evaluated per pullback, giving a total of 3734 frames (1867 matched for pre-ablation (post channel creation) and post-ablation phases). Treatments with the modified Excimer laser catheters resulted in a significant increase in lumen area of 5.5 ± 3.2-mm2 (95% CI 4.3–6.8, p < 0.0001) and reduction in plaque plus media volume of −10.6 ± 36.0 mm3 (95% CI −25.8 to 4.6, p = 0.1619) whilst giving rise to mainly intramural hematoma formations post Turbo-Booster laser treatment in 55% of frames assessed and 24% medial dissections with less than 1% adventitial disruption. The Excimer laser based Turbo-Booster treatment of peripheral artery lesions resulted in significant plaque debulking and increased lumen diameter with negligible degree of adventitial layer injury.