Robust CTA lumen segmentation of the atherosclerotic carotid artery bifurcation in a large patient population

CA-UNet Segmentation Makes a Good Ischemic Stroke Risk Prediction

Stroke is still the World's second major factor of death, as well as the third major factor of death and disability. Ischemic stroke is a type of stroke, in which early detection and treatment are the keys to preventing ischemic strokes. However, due to the limitation of privacy protection and labeling difficulties, there are only a few studies on the intelligent automatic diagnosis of stroke or ischemic stroke, and the results are unsatisfactory. Therefore, we collect some data and propose a 3D carotid Computed Tomography Angiography (CTA) image segmentation model called CA-UNet for fully automated extraction of carotid arteries. We explore the number of down-sampling times applicable to carotid segmentation and design a multi-scale loss function to resolve the loss of detailed features during the process of down-sampling. Moreover, based on CA-Unet, we propose an ischemic stroke risk prediction model to predict the risk in patients using their 3D CTA images, electronic medical records, and medical history. We have validated the efficacy of our segmentation model and prediction model through comparison tests. Our method can provide reliable diagnoses and results that benefit patients and medical professionals.

LIVE-Net: Comprehensive 3D vessel extraction framework in CT angiography

The extraction of vessels from computed tomography angiography (CTA) is significant in diagnosing and evaluating vascular diseases. However, due to the anatomical complexity, wide intensity distribution, and small volume proportion of vessels, vessel extraction is laborious and time-consuming, and it is easy to lead to error-prone diagnostic results in clinical practice. This study proposes a novel comprehensive vessel extraction framework, called the Local Iterative-based Vessel Extraction Network (LIVE-Net), to achieve 3D vessel segmentation while tracking vessel centerlines. LIVE-Net contains dual dataflow pathways that work alternately: an iterative tracking network and a local segmentation network. The former can generate the fine-grain direction and radius prediction of a vascular patch by using the attention-embedded atrous pyramid network (aAPN), and the latter can achieve 3D vascular lumen segmentation by constructing the multi-order self-attention U-shape network (MOSA-UNet). LIVE-Net is trained and evaluated on two datasets: the MICCAI 2008 Coronary Artery Tracking Challenge (CAT08) dataset and head and neck CTA dataset from the clinic. Experimental results of both tracking and segmentation show that our proposed LIVE-Net exhibits superior performance compared with other state-of-the-art (SOTA) networks. In the CAT08 dataset, the tracked centerlines have an average overlap of 95.2%, overlap until first error of 91.2%, overlap with the clinically relevant vessels of 98.3%, and error distance inside of 0.21 mm. The corresponding tracking overlap metrics in the head and neck CTA dataset are 96.7%, 91.0%, and 99.8%, respectively. In addition, the results of the consistent experiment also show strong clinical correspondence. For the segmentation of bilateral carotid and vertebral arteries, our method can not only achieve better accuracy with an average dice similarity coefficient (DSC) of 90.03%, Intersection over Union (IoU) of 81.97%, and 95% Hausdorff distance (95%HD) of 3.42 mm , but higher efficiency with an average time of 67.25 s , even three times faster compared to some methods applied in full field view. Both the tracking and segmentation results prove the potential clinical utility of our network.

Simultaneous vessel segmentation and unenhanced prediction using self-supervised dual-task learning in 3D CTA (SVSUP)

BACKGROUND AND OBJECTIVE

The vessel segmentation in CT angiography (CTA) provides an important basis for automatic diagnosis and hemodynamics analysis. Virtual unenhanced (VU) CT images obtained by dual-energy CT can assist clinical diagnosis and reduce radiation dose by obviating true unenhanced imaging (UECT). However, accurate segmentation of all vessels in the head-neck CTA (HNCTA) remains a challenge, and VU images are currently not available from conventional single-energy CT imaging.

METHODS

In this paper, we proposed a self-supervised dual-task deep learning strategy to fully automatically segment all vessels and predict unenhanced CT images from single-energy HNCTA based on a developed iterative residual-sharing scheme. The underlying idea was to use the correlation between the two tasks to improve task performance while avoiding manual annotation for model training.

RESULTS

The feasibility of the strategy was verified using the data of 24 patients. For vessel segmentation task, the proposed model achieves a significantly higher average Dice coefficient (84.83%, P-values 10^-3 in paired t-test) than the state-of-the-art segmentation model, vanilla VNet (78.94%), and several popular 3D vessel segmentation models, including Hessian-matrix based filter (62.59%), optically-oriented flux (66.33%), spherical flux model (66.91%), and deep vessel net (66.47%). For the unenhanced prediction task, the average ROI-based error compared to the UECT in the artery tissue is 6.1±4.5 HU, similar to previously reported 6.4±5.1 HU for VU reconstruction.

CONCLUSIONS

Results show that the proposed dual-task framework can effectively improve the accuracy of vessel segmentation in HNCTA, and it is feasible to predict the unenhanced image from single-energy CTA, providing a potential new approach for radiation dose saving. Moreover, to our best knowledge, this is the first reported annotation-free deep learning-based full-image vessel segmentation for HNCTA.

Overcoming calcium blooming and improving the quantification accuracy of percent area luminal stenosis by material decomposition of multi-energy computed tomography datasets

Purpose: Conventional stenosis quantification from single-energy computed tomography (SECT) images relies on segmentation of lumen boundaries, which suffers from partial volume averaging and calcium blooming effects. We present and evaluate a method for quantifying percent area stenosis using multienergy CT (MECT) images. Approach: We utilize material decomposition of MECT images to measure stenosis based on the ratio of iodine mass between vessel locations with and without a stenosis, thereby eliminating the requirement for segmentation of iodinated lumen. The method was first assessed using simulated MECT images created with different spatial resolutions. To experimentally assess this method, four phantoms with different stenosis severity (30% to 51%), vessel diameters (5.5 to 14 mm), and calcification densities (700 to 1100    mgHA / cc ) were fabricated. Conventional SECT images were acquired using a commercial CT system and were analyzed with commercial software. MECT images were acquired using a commercial dual-energy CT (DECT) system and also from a research photon-counting detector CT (PCD-CT) system. Three-material-decomposition was performed on MECT data, and iodine density maps were used to quantify stenosis. Clinical radiation doses were used for all data acquisitions. Results: Computer simulation verified that this method reduced partial volume and blooming effects, resulting in consistent stenosis measurements. Phantom experiments showed accurate and reproducible stenosis measurements from MECT images. For DECT and two-threshold PCD-CT images, the estimation errors were 4.0% to 7.0%, 2.0% to 9.0%, 10.0% to 18.0%, and - 1.0 % to - 5.0 % (ground truth: 51%, 51%, 51%, and 30%). For four-threshold PCD-CT images, the errors were 1.0% to 3.0%, 4.0% to 6.0%, - 1.0 % to 9.0%, and 0.0% to 6.0%. Errors using SECT were much larger, ranging from 4.4% to 46%, and were especially worse in the presence of dense calcifications. Conclusions: The proposed approach was shown to be insensitive to acquisition parameters, demonstrating the potential to improve the accuracy and precision of stenosis measurements in clinical practice.

Accurate model-based segmentation of gynecologic brachytherapy catheter collections in MRI-images

The gynecological cancer mortality rate, including cervical, ovarian, vaginal and vulvar cancers, is more than 20,000 annually in the US alone. In many countries, including the US, external-beam radiotherapy followed by high dose rate brachytherapy is the standard-of-care. The superior ability of MR to visualize soft tissue has led to an increase in its usage in planning and delivering brachytherapy treatment. A technical challenge associated with the use of MRI imaging for brachytherapy, in contrast to that of CT imaging, is the visualization of catheters that are used to place radiation sources into cancerous tissue. We describe here a precise, accurate method for achieving catheter segmentation and visualization. The algorithm, with the assistance of manually provided tip locations, performs segmentation using image-features, and is guided by a catheter-specific, estimated mechanical model. A final quality control step removes outliers or conflicting catheter trajectories. The mean Hausdorff error on a 54 patient, 760 catheter reference database was 1.49  mm; 51 of the outliers deviated more than two catheter widths (3.4  mm) from the gold standard, corresponding to catheter identification accuracy of 93% in a Syed-Neblett template. In a multi-user simulation experiment for evaluating RMS precision by simulating varying manually-provided superior tip positions, 3σ maximum errors were 2.44  mm. The average segmentation time for a single catheter was 3 s on a standard PC. The segmentation time, accuracy and precision, are promising indicators of the value of this method for clinical translation of MR-guidance in gynecologic brachytherapy and other catheter-based interventional procedures.

Semi-automated carotid lumen segmentation in computed tomography angiography images

Carotid artery stenosis causes narrowing of carotid lumens and may lead to brain infarction. The purpose of this study was to develop a semi-automated method of segmenting vessel walls, surrounding tissues, and more importantly, the carotid artery lumen by contrast computed tomography angiography (CTA) images and to define the severity of stenosis and present a three-dimensional model of the carotid for visual inspection. In vivo contrast CTA images of 14 patients (7 normal subjects and 7 patients undergoing endarterectomy) were analyzed using a multi-step segmentation algorithm. This method uses graph cut followed by watershed and Hessian based shortest path method in order to extract lumen boundary correctly without being corrupted in the presence of surrounding tissues. Quantitative measurements of the proposed method were compared with those of manual delineation by independent board-certified radiologists. The results were quantitatively evaluated using spatial overlap surface distance indices. A slightly strong match was shown in terms of dice similarity coefficient (DSC) = 0.87±0.08; mean surface distance (Dmsd) = 0.32±0.32; root mean squared surface distance (Drmssd) = 0.49±0.54 and maximum surface distance (Dmax) = 2.14±2.08 between manual and automated segmentation of common, internal and external carotid arteries, carotid bifurcation and stenotic artery, respectively. Quantitative measurements showed that the proposed method has high potential to segment the carotid lumen and is robust to the changes of the lumen diameter and the shape of the stenosis area at the bifurcation site. The proposed method for CTA images provides a fast and reliable tool to quantify the severity of carotid artery stenosis.

Modelling and extraction of pulsatile radial distension and compression motion for automatic vessel segmentation from video

Identification of vascular structures from medical images is integral to many clinical procedures. Most vessel segmentation techniques ignore the characteristic pulsatile motion of vessels in their formulation. In a recent effort to automatically segment vessels that are hidden under fat, we motivated the use of the magnitude of local pulsatile motion extracted from surgical endoscopic video. In this article we propose a new approach that leverages the local orientation, in addition to magnitude of motion, and demonstrate that the extended computation and utilization of motion vectors can improve the segmentation of vascular structures. We implement our approach using four alternatives to magnitude-only motion estimation by using traditional optical flow and by exploiting the monogenic signal for fast flow estimation. Our evaluations are conducted on both synthetic phantoms as well as two real ultrasound datasets showing improved segmentation results with negligible change in computational performance compared to the previous magnitude only approach.

Automated Anatomy-Based Tracking of Systemic Arteries in Arbitrary Field-of-View CTA Scans

We propose an automated pipeline for vessel centerline extraction in 3-D computed tomography angiography (CTA) scans with arbitrary fields of view. The principal steps of the pipeline are body part detection, candidate seed selection, segment tracking, which includes centerline extraction, and vessel tree growing. The final tree-growing step can be instantiated in either a semi- or fully automated fashion. The fully automated initialization is carried out using a vessel position regression algorithm. Both semi-and fully automated methods were evaluated on 30 CTA scans comprising neck, abdominal, and leg arteries in multiple fields of view. High detection rates and centerline accuracy values for 38 distinct vessels demonstrate the effectiveness of our approach.

Review: Mechanical Characterization of Carotid Arteries and Atherosclerotic Plaques

Cardiovascular disease (CVD) is a leading cause of death and is in the majority of cases due to the formation of atherosclerotic plaques in arteries. Initially, thickening of the inner layer of the arterial wall occurs. Continuation of this process leads to plaque formation. The risk of a plaque to rupture and thus to induce an ischemic event is directly related to its composition. Consequently, characterization of the plaque composition and its proneness to rupture are of crucial importance for risk assessment and treatment strategies. The carotid is an excellent artery to be imaged with ultrasound because of its superficial position. In this review, ultrasound-based methods for characterizing the mechanical properties of the carotid wall and atherosclerotic plaque are discussed. Using conventional echography, the intima media thickness (IMT) can be quantified. There is a wealth of studies describing the relation between IMT and the risk for myocardial infarction and stroke. Also the carotid distensibility can be quantified with ultrasound, providing a surrogate marker for the cross-sectional mechanical properties. Although all these parameters are associated with CVD, they do not easily translate to individual patient risk. Another technique is pulse wave velocity (PWV) assessment, which measures the propagation of the pressure pulse over the arterial bed. PWV has proven to be a marker for global arterial stiffness. Recently, an ultrasound-based method to estimate the local PWV has been introduced, but the clinical effectiveness still needs to be established. Other techniques focus on characterization of plaques. With ultrasound elastography, the strain in the plaque due to the pulsatile pressure can be quantified. This technique was initially developed using intravascular catheters to image coronaries, but recently noninvasive methods were successfully developed. A high correlation between the measured strain and the risk for rupture was established. Acoustic radiation force impulse (ARFI) imaging also provides characterization of local plaque components based on mechanical properties. However, both elastography and ARFI provide an indirect measure of the elastic modulus of tissue. With shear wave imaging, the elastic modulus can be quantified, although the carotid artery is one of the most challenging tissues for this technique due to its size and geometry. Prospective studies still have to establish the predictive value of these techniques for the individual patient. Validation of ultrasound-based mechanical characterization of arteries and plaques remains challenging. Magnetic resonance imaging is often used as the "gold" standard for plaque characterization, but its limited resolution renders only global characterization of the plaque. CT provides information on the vascular tree, the degree of stenosis, and the presence of calcified plaque, while soft plaque characterization remains limited. Histology still is the gold standard, but is available only if tissue is excised. In conclusion, elastographic ultrasound techniques are well suited to characterize the different stages of vascular disease.

A semi-automated method for measuring the evolution of both lumen area and blood flow in carotid from Phase Contrast MRI

Phase-Contrast (PC) velocimetry Magnetic Resonance Imaging (MRI) is a useful modality to explore cardiovascular pathologies, but requires the automatic segmentation of vessels and the measurement of both lumen area and blood flow evolutions. In this paper, we propose a semi-automated method for extracting lumen boundaries of the carotid artery and compute both lumen area and blood flow evolutions over the cardiac cycle. This method uses narrow band region-based active contours in order to correctly capture the lumen boundary without being corrupted by surrounding structures. This approach is compared to traditional edge-based active contours, considered in related works, which significantly underestimate lumen area and blood flow. Experiments are performed using both a sequence of a homemade phantom and sequences of 20 real carotids, including a comparison with manual segmentation performed by a radiologist expert. Results obtained on the phantom sequence show that the edge-based approach leads to an underestimate of carotid lumen area and related flows of respectively 18.68% and 4.95%. This appears significantly larger than weak errors obtained using the region-based approach (respectively 2.73% and 1.23%). Benefits appear even better on the real sequences. The edge-based approach leads to underestimates of 40.88% for areas and 13.39% for blood flows, compared to limited errors of 7.41% and 4.6% with our method. Experiments also illustrate the high variability and therefore the lack of reliability of manual segmentation.

DWT-based segmentation method for coronary arteries

This work presents a new method for segmenting coronary arteries automatically in computed tomography angiography (CTA) data sets. The method automatically isolates heart and coronary arteries from surrounding structures and search for the probable location of coronary arteries by 3D region growing. Based on the dilation of the probable location, discrete wavelet transformation (DWT) and λ - mean operation complete accurate detection of coronary arties. Finally, the proposed method is tested on clinical CTA data-sets. The results on clinical datasets show that the proposed method is able to extract each branch of arteries when comparing to commercial software GE Healthcare and delineated ground truth.

Lumen segmentation and stenosis quantification of atherosclerotic carotid arteries in CTA utilizing a centerline intensity prior

PURPOSE

The degree of stenosis is an important biomarker in assessing the severity of cardiovascular disease. The purpose of our work is to develop and evaluate a semiautomatic method for carotid lumen segmentation and subsequent carotid artery stenosis quantification in CTA images.

METHODS

The authors present a semiautomatic stenosis detection and quantification method following lumen segmentation. The lumen of the carotid arteries is segmented in three steps. First, centerlines of the internal and external carotid arteries are extracted with an iterative minimum cost path approach in which the costs are based on a measure of medialness and intensity similarity to lumen. Second, the lumen boundary is delineated using a level set procedure which is steered by gradient information, regional intensity information, and spatial information. Special effort is made in adding terms based on local centerline intensity prior so as to exclude all possible plaque tissues from the segmentation. Third, side branches in the segmented lumen are removed by applying a shape constraint to the envelope of the maximum inscribed spheres of the segmentation. From the segmented lumen, the authors detect and quantify the cross-sectional area-based and cross-sectional diameter-based stenosis degrees according to the North American Symptomatic Carotid En-darterectomy Trial criterion.

RESULTS

The method is trained and tested on a publicly available database from the cls2009 challenge. For the segmentation, the authors obtain a Dice similarity coefficient of 90.2% and a mean absolute surface distance of 0.34 mm. For the stenosis quantification, the authors obtain an average error of 15.7% for cross-sectional diameter-based stenosis and 19.2% for cross-sectional area-based stenosis quantification.

CONCLUSIONS

With these results, the method ranks second in terms of carotid lumen segmentation accuracy, and first in terms of carotid artery stenosis quantification.

Automated knowledge-based detection of nonobstructive and obstructive arterial lesions from coronary CT angiography

PURPOSE

Visual analysis of three-dimensional (3D) coronary computed tomography angiography (CCTA) remains challenging due to large number of image slices and tortuous character of the vessels. The authors aimed to develop a robust, automated algorithm for unsupervised computer detection of coronary artery lesions.

METHODS

The authors' knowledge-based algorithm consists of centerline extraction, vessel classification, vessel linearization, lumen segmentation with scan-specific lumen attenuation ranges, and lesion location detection. Presence and location of lesions are identified using a multi-pass algorithm which considers expected or "normal" vessel tapering and luminal stenosis from the segmented vessel. Expected luminal diameter is derived from the scan by automated piecewise least squares line fitting over proximal and mid segments (67%) of the coronary artery considering the locations of the small branches attached to the main coronary arteries.

RESULTS

The authors applied this algorithm to 42 CCTA patient datasets, acquired with dual-source CT, where 21 datasets had 45 lesions with stenosis ≥ 25%. The reference standard was provided by visual and quantitative identification of lesions with any stenosis ≥ 25% by three expert readers using consensus reading. The authors algorithm identified 42 lesions (93%) confirmed by the expert readers. There were 46 additional lesions detected; 23 out of 39 (59%) of these were less-stenosed lesions. When the artery was divided into 15 coronary segments according to standard cardiology reporting guidelines, per-segment basis, sensitivity was 93% and per-segment specificity was 81% using 10-fold cross-validation.

CONCLUSIONS

The authors' algorithm shows promising results in the detection of both obstructive and nonobstructive CCTA lesions.

Automatic carotid artery distensibility measurements from CTA using nonrigid registration

The distensibility of a blood vessel is a marker of atherosclerotic disease. In this paper we investigate the feasibility of measuring carotid artery distensibility on 4D CTA, both manually and using a new automatic method. On 4D CTA datasets manual (n=38) and automatic (n=76) measurements of the carotid distensibility were performed. A subset (n=10) of the manual annotations were repeated by a second observer. The interobserver variability was assessed using a Bland-Altman analysis and appeared to be too large to reliably measure the distensibility using manual annotation. We compared two versions of the automatic method: one using 3D registration and one using a 4D registration method. The latter resulted in a more smooth deformation over time. The automatic method was evaluated using a synthetic deformation and by investigating whether known relations with cardiovascular risk factors could be reproduced. The relation between distensibility and cardiovascular risk factors was tested with a Mann-Whitney U test. Automatic measurements revealed an association with hypertension whereas the manual measurements did not. This relation has been found by other studies too. We conclude that carotid artery distensibility measurements should be performed automatically and that the method described in this paper is suitable for that. All CTA datasets and related clinical data used in this study can be downloaded from our website (http://ctadist.bigr.nl).

Carotid atherosclerotic plaque progression and change in plaque composition over time: a 5-year follow-up study using serial CT angiography

BACKGROUND AND PURPOSE

Serial in vivo imaging of atherosclerosis is important for understanding plaque progression and is potentially useful in predicting cardiovascular events and monitoring treatment efficacy. This prospective study aims to quantify temporal changes in carotid atherosclerotic plaque volume and plaque composition using MDCTA.

MATERIALS AND METHODS

In 109 patients with TIA or ischemic stroke, serial MDCTA of the carotid arteries was performed after 5.3 ± 0.7 years. The carotid bifurcation was semiautomatically registered for paired baseline follow-up datasets. Outer vessel wall and lumen boundaries were defined using semiautomated segmentation tools. Plaque component volumes were measured using HU thresholds. Annual changes in plaque volume and plaque component proportions were calculated.

RESULTS

One-hundred-ninety-three carotid arteries were analyzed. Plaque volume decreased in 31% and increased in 69% of vessels (range -5.6-10.1%/year). Overall, plaque volume increased 1.2% per year (95% CI, 0.8-1.6, P ≤ .001). Plaque composition changed significantly from BL (fibrous 66.4%, lipid 28.8%, calcifications 4.8%): fibrous tissue decreased by 1.5%, lipid decreased by 1.8%, and calcification increased by 3.3% (P < .001). Intraobserver reproducibility of all volume and proportion measurements was good (ICC 0.78-1.00) and interobserver reproducibility was moderate (ICC 0.76-0.99).

CONCLUSIONS

Changes in carotid plaque burden and plaque composition can be quantified by using serial MDCTA. Plaque burden development is a heterogeneous and slow process.