An automated segmentation method for three-dimensional carotid ultrasound images

CSM-Net: Automatic joint segmentation of intima-media complex and lumen in carotid artery ultrasound images

The intima-media thickness (IMT) is an effective biomarker for atherosclerosis, which is commonly measured by ultrasound technique. However, the intima-media complex (IMC) segmentation for the IMT is challenging due to confused IMC boundaries and various noises. In this paper, we propose a flexible method CSM-Net for the joint segmentation of IMC and Lumen in carotid ultrasound images. Firstly, the cascaded dilated convolutions combined with the squeeze-excitation module are introduced for exploiting more contextual features on the highest-level layer of the encoder. Furthermore, a triple spatial attention module is utilized for emphasizing serviceable features on each decoder layer. Besides, a multi-scale weighted hybrid loss function is employed to resolve the class-imbalance issues. The experiments are conducted on a private dataset of 100 images for IMC and Lumen segmentation, as well as on two public datasets of 1600 images for IMC segmentation. For the private dataset, our method obtain the IMC Dice, Lumen Dice, Precision, Recall, and F1 score of 0.814 ± 0.061, 0.941 ± 0.024, 0.911 ± 0.044, 0.916 ± 0.039, and 0.913 ± 0.027, respectively. For the public datasets, we obtain the IMC Dice, Precision, Recall, and F1 score of 0.885 ± 0.067, 0.885 ± 0.070, 0.894 ± 0.089, and 0.885 ± 0.067, respectively. The results demonstrate that the proposed method precedes some cutting-edge methods, and the ablation experiments show the validity of each module. The proposed method may be useful for the IMC segmentation of carotid ultrasound images in the clinic. Our code is publicly available at https://github.com/yuanyc798/US-IMC-code.

Evaluation of carotid intima media thickness measurement from ultrasound images

A third of deaths in the world are due to cardiovascular diseases [1]. Atherosclerosis is the major cause of myocardial infarction, which occurs by deposition of plaque in the coronary artery. The chance of stroke rises with the thickening of carotid artery due to the plaque. Hence, accurate measurement of the intima-media thickness is necessary for predicting the chance of stroke. The stopping criterion and active resampling are incorporated in greedy snake segmentation technique. This modified algorithm segmented and extracted the intima-media complex in the ultrasound images. The snake control points obtained from the boundary of the region of interest forms the contour and demarcates the boundary of intima-media complex. The thickness ± standard deviation and the intra-observer error values obtained by modified algorithm are in conformity with the measurements by expert. The intra-observer error values for greedy snake segmentation methods were 0.10 and 0.09 for manual snake initialization and automatic snake initialization, respectively. Shapiro-Wilk test and One-way ANOVA test explains there is no statistical difference between group means obtained from these segmentation techniques and the expert measurement. The statistical analysis proves values of the intima-media thickness obtained from both snake segmentation techniques are very close to expert measurements.

Bimodal Automated Carotid Ultrasound Segmentation Using Geometrically Constrained Deep Neural Networks

For asymptomatic patients suffering from carotid stenosis, the assessment of plaque morphology is an important clinical task which allows monitoring of the risk of plaque rupture and future incidents of stroke. Ultrasound Imaging provides a safe and non-invasive modality for this, and the segmentation of media-adventitia boundaries and lumen-intima boundaries of the Carotid artery form an essential part in this monitoring process. In this paper, we propose a novel Deep Neural Network as a fully automated segmentation tool, and its application in delineating both the media-adventitia boundary and the lumen-intima boundary. We develop a new geometrically constrained objective function as part of the Network's Stochastic Gradient Descent optimisation, thus tuning it to the problem at hand. Furthermore, we also apply a bimodal fusion of amplitude and phase congruency data proposed by us in previous work, as an input to the network, as the latter provides an intensity-invariant data source to the network. We finally report the segmentation performance of the network on transverse sections of the carotid. Tests are carried out on an augmented dataset of 81,000 images, and the results are compared to other studies by reporting the DICE coefficient of similarity, modified Hausdorff Distance, sensitivity and specificity. Our proposed modification is shown to yield improved results on the standard network over this larger dataset, with the advantage of it being fully automated. We conclude that Deep Neural Networks provide a reliable trained manner in which carotid ultrasound images may be automatically segmented, using amplitude data and intensity invariant phase congruency maps as a data source.

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

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

Automatic identification of blood vessel cross-section for central venous catheter placement using a cascading classifier

This paper presents a system that is able to automatically identify, segment, and track the cross-section of the internal jugular vein (IJV) and the common carotid artery (CCA) in an ultrasound image feed during a central venous catheter (CVC) placement procedure. The goal is to provide assistance to the practitioner in order to decrease the probability of complications stemming from inadvertent punctures of the CCA during the procedure. In the system, a modified Star algorithm is implemented to segment and track the blood vessel throughout an ultrasound video feed. A novel algorithm based on a cascading classifier is used to identify the location of the IJV and the CCA for two main tasks: (1) selecting the initial seed point at the start of tracking and (2) validating the segmentation results at each subsequent frame. The classifier uses shape features (vessel area, ellipse fitting error, vessel depth, vessel eccentricity) and pixel-based features (pixel intensity and the histogram of oriented gradients descriptor) to differentiate between vessel and non-vessel structures and also differentiate between the IJV and the CCA. Evaluated on a database of 800 ultrasound images containing the cross-section of both vessels, the cascading classifier was able to identify the IJV and the CCA in 92.25% and 85.13% of the images respectively without any initialization from the user at a maximum processing rate of 40.65 frames per second. This allows identification to be conducted in real-time with existing ultrasound machines.

Freehand three-dimensional ultrasound imaging of carotid artery using motion tracking technology

Ultrasound imaging has been extensively used for determining the severity of carotid atherosclerotic stenosis. In particular, the morphological characterization of carotid plaques can be performed for risk stratification of patients. However, using 2D ultrasound imaging for detecting morphological changes in plaques has several limitations. Due to the scan was performed on a single longitudinal cross-section, the selected 2D image is difficult to represent the entire morphology and volume of plaque and vessel lumen. In addition, the precise positions of 2D ultrasound images highly depend on the radiologists' experience, it makes the serial long-term exams of anti-atherosclerotic therapies are difficult to relocate the same corresponding planes by using 2D B-mode images. This has led to the recent development of three-dimensional (3D) ultrasound imaging, which offers improved visualization and quantification of complex morphologies of carotid plaques. In the present study, a freehand 3D ultrasound imaging technique based on optical motion tracking technology is proposed. Unlike other optical tracking systems, the marker is a small rigid body that is attached to the ultrasound probe and is tracked by eight high-performance digital cameras. The probe positions in 3D space coordinates are then calibrated at spatial and temporal resolutions of 10μm and 0.01s, respectively. The image segmentation procedure involves Otsu's and the active contour model algorithms and accurately detects the contours of the carotid arteries. The proposed imaging technique was verified using normal artery and atherosclerotic stenosis phantoms. Human experiments involving freehand scanning of the carotid artery of a volunteer were also performed. The results indicated that compared with manual segmentation, the lowest percentage errors of the proposed segmentation procedure were 7.8% and 9.1% for the external and internal carotid arteries, respectively. Finally, the effect of handshaking was calibrated using the optical tracking system for reconstructing a 3D image.

An Improved Algorithm for Coronary Bypass Anastomosis Segmentation in Epicardial Ultrasound Sequences

Epicardial ultrasound (EUS) can be used for intra-operative quality assessment of coronary artery bypass anastomoses. To quantify the anastomotic quality from EUS images, the area of anastomotic structures has to be extracted from EUS sequences. Currently, this is done manually as no objective methods are available. We used an automatic anastomosis segmentation algorithm to extract the area of anastomotic structures from in vivo EUS sequences obtained from 16 porcine anastomoses. The algorithm consists of four major components: vessel detection, vessel segmentation, segmentation quality control and inter-frame contour alignment. The segmentation accuracy was assessed using m-fold cross-validation based on 830 manual segmentations of the anastomotic structures. A Dice coefficient of 0.879 (±0.073) and an absolute area difference of 16.95% (±17.94) were obtained. The proposed segmentation algorithm has potential to automatically extract the area of anastomotic structures.

Survey statistics of automated segmentations applied to optical imaging of mammalian cells

BACKGROUND

The goal of this survey paper is to overview cellular measurements using optical microscopy imaging followed by automated image segmentation. The cellular measurements of primary interest are taken from mammalian cells and their components. They are denoted as two- or three-dimensional (2D or 3D) image objects of biological interest. In our applications, such cellular measurements are important for understanding cell phenomena, such as cell counts, cell-scaffold interactions, cell colony growth rates, or cell pluripotency stability, as well as for establishing quality metrics for stem cell therapies. In this context, this survey paper is focused on automated segmentation as a software-based measurement leading to quantitative cellular measurements.

METHODS

We define the scope of this survey and a classification schema first. Next, all found and manually filteredpublications are classified according to the main categories: (1) objects of interests (or objects to be segmented), (2) imaging modalities, (3) digital data axes, (4) segmentation algorithms, (5) segmentation evaluations, (6) computational hardware platforms used for segmentation acceleration, and (7) object (cellular) measurements. Finally, all classified papers are converted programmatically into a set of hyperlinked web pages with occurrence and co-occurrence statistics of assigned categories.

RESULTS

The survey paper presents to a reader: (a) the state-of-the-art overview of published papers about automated segmentation applied to optical microscopy imaging of mammalian cells, (b) a classification of segmentation aspects in the context of cell optical imaging, (c) histogram and co-occurrence summary statistics about cellular measurements, segmentations, segmented objects, segmentation evaluations, and the use of computational platforms for accelerating segmentation execution, and (d) open research problems to pursue.

CONCLUSIONS

The novel contributions of this survey paper are: (1) a new type of classification of cellular measurements and automated segmentation, (2) statistics about the published literature, and (3) a web hyperlinked interface to classification statistics of the surveyed papers at https://isg.nist.gov/deepzoomweb/resources/survey/index.html.

3D reconstruction of a carotid bifurcation from 2D transversal ultrasound images

Visualizing and analyzing the morphological structure of carotid bifurcations are important for understanding the etiology of carotid atherosclerosis, which is a major cause of stroke and transient ischemic attack. For delineation of vasculatures in the carotid artery, ultrasound examinations have been widely employed because of a noninvasive procedure without ionizing radiation. However, conventional 2D ultrasound imaging has technical limitations in observing the complicated 3D shapes and asymmetric vasodilation of bifurcations. This study aims to propose image-processing techniques for better 3D reconstruction of a carotid bifurcation in a rat by using 2D cross-sectional ultrasound images. A high-resolution ultrasound imaging system with a probe centered at 40MHz was employed to obtain 2D transversal images. The lumen boundaries in each transverse ultrasound image were detected by using three different techniques; an ellipse-fitting, a correlation mapping to visualize the decorrelation of blood flow, and the ellipse-fitting on the correlation map. When the results are compared, the third technique provides relatively good boundary extraction. The incomplete boundaries of arterial lumen caused by acoustic artifacts are somewhat resolved by adopting the correlation mapping and the distortion in the boundary detection near the bifurcation apex was largely reduced by using the ellipse-fitting technique. The 3D lumen geometry of a carotid artery was obtained by volumetric rendering of several 2D slices. For the 3D vasodilatation of the carotid bifurcation, lumen geometries at the contraction and expansion states were simultaneously depicted at various view angles. The present 3D reconstruction methods would be useful for efficient extraction and construction of the 3D lumen geometries of carotid bifurcations from 2D ultrasound images.

An integrated system for the segmentation of atherosclerotic carotid plaque ultrasound video

The robust border identification of atherosclerotic carotid plaque, the corresponding degree of stenosis of the common carotid artery (CCA), and also the characteristics of the arterial wall, including plaque size, composition, and elasticity, have significant clinical relevance for the assessment of future cardiovascular events. To facilitate the follow-up and analysis of the carotid stenosis in serial clinical investigations, we propose and evaluate an integrated system for the segmentation of atherosclerotic carotid plaque in ultrasound videos of the CCA based on video frame normalization, speckle reduction filtering, M-mode state-based identification, parametric active contours, and snake segmentation. Initially, the cardiac cycle in each video is identified and the video M-mode is generated, thus identifying systolic and diastolic states. The video is then segmented for a time period of at least one full cardiac cycle. The algorithm is initialized in the first video frame of the cardiac cycle, with human assistance if needed, and the moving atherosclerotic plaque borders are tracked and segmented in the subsequent frames. Two different initialization methods are investigated in which initial contours are estimated every 20 video frames. In the first initialization method, the initial snake contour is estimated using morphology operators; in the second initialization method, the Chan-Vese active contour model is used. The performance of the algorithm is evaluated on 43 real CCA digitized videos from B-mode longitudinal ultrasound segments and is compared with the manual segmentations of an expert, available every 20 frames in a time span of 3 to 5 s, covering, in general, 2 cardiac cycles. The segmentation results were very satisfactory, according to the expert objective evaluation, for the two different methods investigated, with true-negative fractions (TNF-specificity) of 83.7 ± 7.6% and 84.3 ± 7.5%; true-positive fractions (TPF-sensitivity) of 85.42 ± 8.1% and 86.1 ± 8.0%; and between the ground truth and the proposed segmentation method, kappa indices (KI) of 84.6% and 85.3% and overlap indices of 74.7% and 75.4%. The segmentation contours were also used to compute the cardiac state identification and radial, longitudinal, and shear strain indices for the CCA wall and plaque between the asymptomatic and symptomatic groups were investigated. The results of this study show that the integrated system investigated in this study can be successfully used for the automated video segmentation of the CCA plaque in ultrasound videos.

Three-dimensional segmentation of three-dimensional ultrasound carotid atherosclerosis using sparse field level sets

PURPOSE

Three-dimensional ultrasound (3DUS) vessel wall volume (VWV) provides a 3D measurement of carotid artery wall remodeling and atherosclerotic plaque and is sensitive to temporal changes of carotid plaque burden. Unfortunately, although 3DUS VWV provides many advantages compared to measurements of arterial wall thickening or plaque alone, it is still not widely used in research or clinical practice because of the inordinate amount of time required to train observers and to generate 3DUS VWV measurements. In this regard, semiautomated methods for segmentation of the carotid media-adventitia boundary (MAB) and the lumen-intima boundary (LIB) would greatly improve the time to train observers and for them to generate 3DUS VWV measurements with high reproducibility.

METHODS

The authors describe a 3D algorithm based on a modified sparse field level set method for segmenting the MAB and LIB of the common carotid artery (CCA) from 3DUS images. To the authors' knowledge, the proposed algorithm is the first direct 3D segmentation method, which has been validated for segmenting both the carotid MAB and the LIB from 3DUS images for the purpose of computing VWV. Initialization of the algorithm requires the observer to choose anchor points on each boundary on a set of transverse slices with a user-specified interslice distance (ISD), in which larger ISD requires fewer user interactions than smaller ISD. To address the challenges of the MAB and LIB segmentations from 3DUS images, the authors integrated regional- and boundary-based image statistics, expert initializations, and anatomically motivated boundary separation into the segmentation. The MAB is segmented by incorporating local region-based image information, image gradients, and the anchor points provided by the observer. Moreover, a local smoothness term is utilized to maintain the smooth surface of the MAB. The LIB is segmented by constraining its evolution using the already segmented surface of the MAB, in addition to the global region-based information and the anchor points. The algorithm-generated surfaces were sliced and evaluated with respect to manual segmentations on a slice-by-slice basis using 21 3DUS images.

RESULTS

The authors used ISD of 1, 2, 3, 4, and 10 mm for algorithm initialization to generate segmentation results. The algorithm-generated accuracy and intraobserver variability results are comparable to the previous methods, but with fewer user interactions. For example, for the ISD of 3 mm, the algorithm yielded an average Dice coefficient of 94.4% ± 2.2% and 90.6% ± 5.0% for the MAB and LIB and the coefficient of variation of 6.8% for computing the VWV of the CCA, while requiring only 1.72 min (vs 8.3 min for manual segmentation) for a 3DUS image.

CONCLUSIONS

The proposed 3D semiautomated segmentation algorithm yielded high-accuracy and high-repeatability, while reducing the expert interaction required for initializing the algorithm than the previous 2D methods.

Fully automatic plaque segmentation in 3-D carotid ultrasound images

Automatic segmentation of the carotid plaques from ultrasound images has been shown to be an important task for monitoring progression and regression of carotid atherosclerosis. Considering the complex structure and heterogeneity of plaques, a fully automatic segmentation method based on media-adventitia and lumen-intima boundary priors is proposed. This method combines image intensity with structure information in both initialization and a level-set evolution process. Algorithm accuracy was examined on the common carotid artery part of 26 3-D carotid ultrasound images (34 plaques ranging in volume from 2.5 to 456 mm(3)) by comparing the results of our algorithm with manual segmentations of two experts. Evaluation results indicated that the algorithm yielded total plaque volume (TPV) differences of -5.3 ± 12.7 and -8.5 ± 13.8 mm(3) and absolute TPV differences of 9.9 ± 9.5 and 11.8 ± 11.1 mm(3). Moreover, high correlation coefficients in generating TPV (0.993 and 0.992) between algorithm results and both sets of manual results were obtained. The automatic method provides a reliable way to segment carotid plaque in 3-D ultrasound images and can be used in clinical practice to estimate plaque measurements for management of carotid atherosclerosis.

Ultrasound common carotid artery segmentation based on active shape model

Carotid atherosclerosis is a major reason of stroke, a leading cause of death and disability. In this paper, a segmentation method based on Active Shape Model (ASM) is developed and evaluated to outline common carotid artery (CCA) for carotid atherosclerosis computer-aided evaluation and diagnosis. The proposed method is used to segment both media-adventitia-boundary (MAB) and lumen-intima-boundary (LIB) on transverse views slices from three-dimensional ultrasound (3D US) images. The data set consists of sixty-eight, 17 × 2 × 2, 3D US volume data acquired from the left and right carotid arteries of seventeen patients (eight treated with 80 mg atorvastatin and nine with placebo), who had carotid stenosis of 60% or more, at baseline and after three months of treatment. Manually outlined boundaries by expert are adopted as the ground truth for evaluation. For the MAB and LIB segmentations, respectively, the algorithm yielded Dice Similarity Coefficient (DSC) of 94.4% ± 3.2% and 92.8% ± 3.3%, mean absolute distances (MAD) of 0.26 ± 0.18 mm and 0.33 ± 0.21 mm, and maximum absolute distances (MAXD) of 0.75 ± 0.46 mm and 0.84 ± 0.39 mm. It took 4.3 ± 0.5 mins to segment single 3D US images, while it took 11.7 ± 1.2 mins for manual segmentation. The method would promote the translation of carotid 3D US to clinical care for the monitoring of the atherosclerotic disease progression and regression.

A review of noninvasive ultrasound image processing methods in the analysis of carotid plaque morphology for the assessment of stroke risk

Noninvasive ultrasound imaging of carotid plaques allows for the development of plaque-image analysis methods associated with the risk of stroke. This paper presents several plaque-image analysis methods that have been developed over the past years. The paper begins with a review of clinical methods for visual classification that have led to standardized methods for image acquisition, describes methods for image segmentation and denoising, and provides an overview of the several texture-feature extraction and classification methods that have been applied. We provide a summary of emerging trends in 3-D imaging methods and plaque-motion analysis. Finally, we provide a discussion of the emerging trends and future directions in our concluding remarks.

Volume segmentation and reconstruction from freehand three-dimensional ultrasound data with application to ovarian follicle measurement

This article presents a semi-automatic method for segmentation and reconstruction of freehand three-dimensional (3D) ultrasound data. The method incorporates a number of interesting features within the level-set framework: First, segmentation is carried out using region competition, requiring multiple distinct and competing regions to be encoded within the framework. This region competition uses a simple dot-product based similarity measure to compare intensities within each region. In addition, segmentation and surface reconstruction is performed within the 3D domain to take advantage of the additional spatial information available. This means that the method must interpolate the surface where there are gaps in the data, a feature common to freehand 3D ultrasound reconstruction. Finally, although the level-set method is restricted to a voxel grid, no assumption is made that the data being segmented will conform to this grid and may be segmented in its world-reference position. The volume reconstruction method is demonstrated in vivo for the volume measurement of ovarian follicles. The 3D reconstructions produce a lower error variance than the current clinical measurement based on a mean diameter estimated from two-dimensional (2D) images. However, both the clinical measurement and the semi-automatic method appear to underestimate the true follicular volume.

3D reconstruction of carotid atherosclerotic plaque: comparison between spatial compound ultrasound models and anatomical models

This study deals with the creation of 3D models that can work as a tool for discriminating between tissue and background in the development of tissue classification methods. Ten formalin-fixed atherosclerotic carotid plaques removed by endarterectomy were scanned with 3D multi-angle spatial compound ultrasound (US) and subsequently sliced and photographed to produce a 3D anatomical data set. Outlines in the ultrasound data were found by means of active contours and combined into 10 3D ultrasound models. The plaque regions of the anatomical photographs were outlined manually and then combined into 10 3D anatomical models. The volumes of the anatomical models correlated with the volume found by a water displacement method (r = 0.95), except for an offset. The models were compared in three ways. Visual inspection showed quite good agreement between the models. The volumes of the ultrasound models correlated with the volumes of the anatomical models (r = 0.93), again with an offset. Finally, the overlap between the anatomical models and the ultrasound models showed, on average, that the intersection comprised 90%(vol) of the anatomical models and 73%(vol) of the ultrasound models.

Validation of 3D ultrasound vessel wall volume: an imaging phenotype of carotid atherosclerosis

Carotid atherosclerotic lesions are a major cause of stroke and the identification and quantification of such lesions in patients is important for the development of a better understanding of atherogenesis in high risk populations and for the design of studies to assess treatment efficacy. Our objective was to develop and validate a new three-dimensional ultrasound (3DUS) measurement or phenotype of carotid atherosclerosis, vessel wall volume (VWV), which is a three-dimensional measurement of vessel wall thickness and plaque within the carotid arteries measured in 3DUS images. To assess both intraobserver and interscan variability, 3DUS images were acquired from the right and left carotid arteries of ten subjects with carotid atherosclerosis scanned twice within a period of 2 wk. For both VWV and total plaque volume (TPV), an expert observer performed five measurement trials of all images acquired at baseline scan and 2-wk rescan with a 5-d period between measurement trials for images. Images were re-randomized for each measurement trial and both TPV and VWV were measured by observers who were blinded to subject identification for each time-point measurement. Coefficients of variation (COV) and intraclass correlation coefficients (ICC), for VWV measurements indicated higher intraobserver (scan COV = 4.6% ICC = 0.95, rescan COV = 3.4%, ICC = 0.96) and interscan reproducibility (COV = 5.7%, ICC = 0.85) than TPV measurements (intraobserver variability scan COV = 22.7% ICC = 0.85, rescan COV = 21.1% ICC = 0.88 and interscan variability, COV = 31.1%, ICC = 0.83), although absolute variances for both phenotypes were very similar (VWV = 90 mm3, TPV = 80 mm3).

Manual planimetric measurement of carotid plaque volume using three-dimensional ultrasound imaging

We investigated the utility of three manual planimetric methods to quantify carotid plaque volume. A single observer measured 15 individual plaques from 15 three-dimensional (3D) ultrasound (3D US) images of patients ten times each using three different planimetric approaches. Individual plaque volumes were measured (range: 32.6-597.1 mm3) using a standard planimetric approach (M1) whereby a plaque end was identified and sequential contours were measured. The same plaques were measured using a second approach (M2), whereby plaque ends were first identified and the 3D US image of the plaque was then subdivided into equal intervals. A third method (M3) was used to measure total plaque burden (range: 165.1-1080.0 mm3) in a region (+/- 1.5 cm) relative to the carotid bifurcation. M1 systematically underestimated individual plaque volume compared to M2 (V2 = V1 + 14.0 mm3, r = 0.99, p = 0.006) due to a difference in the mean plaque length measured. Coefficients of variance (CV) for M1 and M2 decrease with increasing plaque volume, with M2 results less than M1. Root mean square difference between experimental and theoretical CV for M2 was 3.2%. The standard deviation in the identification of the transverse location of the carotid bifurcation was 0.56 mm. CVs for plaque burden measured using M3 ranged from 1.2% to 7.6% and were less than CVs determined for individual plaque volumes of the same volume. The utility of M3 was demonstrated by measuring carotid plaque burden and volume change over a period of 3 months in three patients. In conclusion, M2 was determined to be a more superior measurement technique than M1 to measure individual plaque volume. Furthermore, we demonstrated the utility of M3 to quantify regional plaque burden and to quantify change in plaque volume.

Snakes based segmentation of the common carotid artery intima media

Ultrasound measurements of the human carotid artery walls are conventionally obtained by manually tracing interfaces between tissue layers. In this study we present a snakes segmentation technique for detecting the intima-media layer of the far wall of the common carotid artery (CCA) in longitudinal ultrasound images, by applying snakes, after normalization, speckle reduction, and normalization and speckle reduction. The proposed technique utilizes an improved snake initialization method, and an improved validation of the segmentation method. We have tested and clinically validated the segmentation technique on 100 longitudinal ultrasound images of the carotid artery based on manual measurements by two vascular experts, and a set of different evaluation criteria based on statistical measures and univariate statistical analysis. The results showed that there was no significant difference between all the snakes segmentation measurements and the manual measurements. For the normalized despeckled images, better snakes segmentation results with an intra-observer error of 0.08, a coefficient of variation of 12.5%, best Bland-Altman plot with smaller differences between experts (0.01, 0.09 for Expert1 and Expert 2, respectively), and a Hausdorff distance of 5.2, were obtained. Therefore, the pre-processing of ultrasound images of the carotid artery with normalization and speckle reduction, followed by the snakes segmentation algorithm can be used successfully in the measurement of IMT complementing the manual measurements. The present results are an expansion of data published earlier as an extended abstract in IFMBE Proceedings (Loizou et al. IEEE Int X Mediterr Conf Medicon Med Biol Eng POS-03 499:1-4, 2004).

Submillimeter isotropic resolution carotid wall MRI with swallowing compensation: Imaging results and semiautomated wall morphometry

PURPOSE

To assess a swallowing-compensated, three-dimensional (3D) diffusion-prepared segmented steady-state free precession (3D Nav-D-SSFP) technique for carotid wall MRI with 0.6-mm isotropic spatial resolution, and its utility for semiautomated carotid wall morphometry.

MATERIALS AND METHODS

The carotid arteries of seven healthy volunteers (N=14) were imaged with 3D Nav-D-SSFP and black-blood T2-weighted (T2w) two-dimensional (2D) fast spin-echo (FSE). Carotid wall-lumen contrast-to-noise ratio (CNR) was measured with both sequences. Measurement of carotid wall area (WA) and lumen area (LA) made in a semiautomated manner off of the 3D Nav-D-SSFP images were compared to those made manually.

RESULTS

Adjusted for voxel volume and number of slices, a near six-fold improvement in CNR per unit time was achieved with 3D Nav-D-SSFP relative to 2D T2w FSE (P<0.001). Manual and semiautomated measurements of carotid WA and LA on the 3D Nav-D-SSFP images were highly correlated (intraclass correlation coefficient (ICC)=0.961 and 0.996, respectively; P<0.001).

CONCLUSION

3D Nav-D-SSFP is a time-efficient, swallowing-compensated, black-blood technique that lends itself for semiautomated measurements of carotid WA and LA that are in good agreement with manual measurements.

Ultrasound image segmentation: a survey

This paper reviews ultrasound segmentation paper methods, in a broad sense, focusing on techniques developed for medical B-mode ultrasound images. First, we present a review of articles by clinical application to highlight the approaches that have been investigated and degree of validation that has been done in different clinical domains. Then, we present a classification of methodology in terms of use of prior information. We conclude by selecting ten papers which have presented original ideas that have demonstrated particular clinical usefulness or potential specific to the ultrasound segmentation problem.

Quantification of carotid plaque volume measurements using 3D ultrasound imaging

An accurate and reliable technique used to quantify carotid plaque volume has practical importance in research and patient management. In this study, we develop and investigate a theoretical description of carotid plaque volume measurements made using three-dimensional (3D) ultrasound (US) images and compare it with experimental results. Multiple observers measured 48 3D US patient images of carotid plaque (13.2 to 544.0 mm(3)) by manual planimetry. Coefficients of variation in the measurement of plaque volume were found to decrease with increasing plaque size for both inter- (90.8 to 3.9%) and intraobserver (70.2 to 3.1%) measurements. Plaque volume measurement variability was found to increase with interslice distance (ISD), while the relative measurement accuracy remained constant for ISDs between 1.0 and 3.0 mm and then decreased. Root-mean-square (RMS) difference between our theoretical description of plaque volume measurement variance and the experimental results was 5.7%. Thus, our results support the clinical utility of measuring carotid plaque volume by manual planimetry noninvasively using 3D US.

Image-based cardiac gating for three-dimensional intravascular ultrasound imaging

Three-dimensional (3-D) intravascular ultrasound (US), or IVUS, provides valuable insight into the tissue characteristics of the coronary wall and plaque composition. However, artefacts due to cardiac motion and vessel wall pulsation limit the accuracy and variability of coronary lumen and plaque volume measurement in 3-D IVUS images. ECG-gated image acquisition can reduce these artefacts but it requires recording the ECG signal and may increase image acquisition time. The goal of our study was to reconstruct a 3-D IVUS image with negligible cardiac motion and vessel pulsation artefacts, by developing an image-based gating method to track 2-D IVUS images over the cardiac cycle. Our approach involved selecting 2-D IVUS images belonging to the same cardiac phase from an asynchronously-acquired series, by tracking the changing lumen contour over the cardiac cycle. The algorithm was tested with IVUS images of a custom-built coronary vessel phantom and with patient images. The artefact reduction achieved using the image-gating approach was > 86% in the in vitro images and > 80% in the in vivo images in our study. Our study shows that image-based gating of IVUS images provides a useful method for accurate reconstruction of 3-D IVUS images with reduced cardiac motion artefact.

Measurement of carotid plaque volume by 3-dimensional ultrasound

BACKGROUND AND PURPOSE

Measurement of carotid plaque volume and its progression are important tools for research and patient management. In this study, we investigate the observer variability in the measurement of plaque volume as determined by 3-dimensional (3D) ultrasound (US). We also investigate the effect of interslice distances (ISD) and repeated 3D US scans on measurement variability. Materials and Methods Forty 3D US patient images of plaques (range, 37.43 to 604.1 mm3) were measured by manual planimetry. We applied ANOVA to determine plaque volume measurement variability and reliability. Plaque volumes were measured with 9 ISDs to determine the effect of ISD on measurement variability. Additional plaque volumes were also measured from multiple 3D US scans to investigate repeated scan acquisition variability. Results Intraobserver and interobserver measurement reliabilities were 94% and 93.2%, respectively. Plaque volume measurement variability decreased with increasing plaque volume (range, 27.1% to 2.2%). Measurement precision was constant for ISDs between 1.0 and 3.0 mm, whereas plaque volume measurement variability increased with ISD. Repeated 3D US scan measurements were not different from single-scan measurements (P=0.867). Conclusions The coefficient of variation in the measurement of plaque volume decreased with plaque size. The volumetric change that must be observed to establish with 95% confidence that a plaque has undergone change is approximately 20% to 35% for plaques <100 mm3 and approximately 10% to 20% for plaques >100 mm3. Measurement precision was unchanged for ISDs <3.0 mm, whereas measurement variability increased with ISD. Repeated 3D US scans did not affect plaque volume measurement variability.

A computer aided diagnostic system for radiotherapy planning

Planning for radiation therapy intervention implies the definition of treatment volumes as well as a clear delimitation of normal tissue. This paper presents a Computer Aided Diagnostic system for the automatic CT image analysis. Two important problems are solved: the spinal cord segmentation and the detection of lung metastases. Some subordinate problems are also solved: the detection of spinal canal, lamina, lungs, and ribs, as well as the identification of thorax contour. The developed methodologies use a knowledge-driven image processing based on Anatomical Structures Maps and task-oriented architecture. Experiments were performed on CT images from La Chaux de Fonds Hospital (Switzerland). Evaluations were performed using a visual inspection of the contours projected on the CT image slices. The radiologist decided whether each of the contours obtained with our system was acceptable or not. The accuracy of the method was defined as the fraction of CT slices in which the particular contour was correctly located. In the case of spinal cord segmentation, the procedure was tested on 23 patients (1051 images), resulting in an accuracy of 91%. In the case of lung tumors detection, the method showed an accuracy of > 90%, with testing performed on 20 patients for a total of 988 images. The experiments performed show that the method is reliable, with possible future application in an oncology department.

A knowledge-based approach to automatic detection of the spinal cord in CT images

Accurate planning of radiation therapy entails the definition of treatment volumes and a clear delimitation of normal tissue of which unnecessary exposure should be prevented. The spinal cord is a radiosensitive organ, which should be precisely identified because an overexposure to radiation may lead to undesired complications for the patient such as neuronal disfunction or paralysis. In this paper, a knowledge-based approach to identifying the spinal cord in computed tomography images of the thorax is presented. The approach relies on a knowledge-base which consists of a so-called anatomical structures map (ASM) and a task-oriented architecture called the plan solver. The ASM contains a frame-like knowledge representation of the macro-anatomy in the human thorax. The plan solver is responsible for determining the position, orientation and size of the structures of interest to radiation therapy. The plan solver relies on a number of image processing operators. Some are so-called atomic (e.g., thresholding and snakes) whereas others are composite. The whole system has been implemented on a standard PC. Experiments performed on the image material from 23 patients show that the approach results in a reliable recognition of the spinal cord (92% accuracy) and the spinal canal (85% accuracy). The lamina is more problematic to locate correctly (accuracy 72%). The position of the outer thorax is always determined correctly.

Theoretical and experimental quantification of carotid plaque volume measurements made by three-dimensional ultrasound using test phantoms

An accurate technique that exhibits low variability has practical importance for the quantification of carotid plaque volume. Such a technique is necessary to monitor plaque progression or regression that may result in response to nonsurgical therapy. In this study, we investigate the accuracy and variability of plaque volume measurement by three-dimensional ultrasound using vascular plaque phantoms over a range of 68.2 mm3 to 285.5 mm3. The agar plaques maintained a consistent cylindrical geometry with variations in the height, length, and echogenicity. The volume of each plaque was determined by water displacement. The three-dimensional (3D) ultrasound (US) images were acquired with a mechanical scanning system which creates a 3D US Cartesian volume, that was manipulated and viewed in any orientation, from a collection of conventional parallel two-dimensional (2D) US images. The plaque volumes were measured by serial 2D manual planimtery. The mean accuracy in plaque volume measurement was 3.1+/-0.9%. Variability in plaque volume measurement was calculated to be 4.0+/-1.0% and 5.1+/-1.4% for intraobserver and interobserver measurements, respectively. We have also developed a theoretical description for the variance in measurement of plaque volume using manual planimetry. Root-mean-square difference between experimentally and theoretically determined values of plaque volume fractional variance was 9%.