Atherosclerotic plaques are focal and tend to occur at arterial bends and bifurcations. To quantitatively monitor the local changes in the carotid vessel-wall-plus-plaque thickness (VWT) and compare ...the VWT distributions for different patients or for the same patients at different ultrasound scanning sessions, a mapping technique is required to adjust for the geometric variability of different carotid artery models. In this work, we propose a novel method called density-equalizing reference map (DERM) for mapping 3D carotid surfaces to a standardized 2D carotid template, with an emphasis on preserving the local geometry of the carotid surface by minimizing the local area distortion. The initial map was generated by a previously described arc-length scaling (ALS) mapping method, which projects a 3D carotid surface onto a 2D non-convex L-shaped domain. A smooth and area-preserving flattened map was subsequently constructed by deforming the ALS map using the proposed algorithm that combines the density-equalizing map and the reference map techniques. This combination allows, for the first time, one-to-one mapping from a 3D surface to a standardized non-convex planar domain in an area-preserving manner. Evaluations using 20 carotid surface models show that the proposed method reduced the area distortion of the flattening maps by over 80% as compared to the ALS mapping method. The proposed method is capable of improving the accuracy of area estimation for plaque regions without compromising inter-scan reproducibility.
Supersonic shear-wave elastography (SWE) has emerged as a useful imaging modality for breast lesion assessment. Regions of interest (ROIs) were required to be specified for extracting features that ...characterize malignancy of lesions. Although analyses have been performed in small rectangular ROIs identified manually by expert observers, the results were subject to observer variability and the analysis of small ROIs would potentially miss out important features available in other parts of the lesion. Recent investigations extracted features from the entire lesion segmented by B-mode ultrasound images either manually or semi-automatically, but lesion delineation using existing techniques is time-consuming and prone to variability as intensive user interactions are required. In addition, rich diagnostic features were available along the rim surrounding the lesion. The width of the rim analyzed was subjectively and empirically determined by expert observers in previous studies after intensive visual study on the images, which is time-consuming and susceptible to observer variability. This paper describes an analysis pipeline to segment and classify lesions efficiently. The lesion boundary was first initialized and then deformed based on energy fields generated by the dyadic wavelet transform. Features of the SWE images were extracted from inside and outside of a lesion for different widths of the surrounding rim. Then, feature selection was performed followed by the Support Vector Machine (SVM) classification. This strategy obviates the empirical and time-consuming selection of the surrounding rim width before the analysis. The pipeline was evaluated on 137 lesions. Feature selection was performed 20 times using different sets of 14 lesions (7 malignant, 7 benign). Leave-one-out SVM classification was performed in each of the 20 experiments with a mean sensitivity, specificity and accuracy of 95.1%, 94.6% and 94.8% respectively. The pipeline took an average of 20 s to process a lesion. The fact that this efficient pipeline generated classification accuracy superior to that of existing algorithms suggests that improved efficiency did not compromise classification accuracy. The ability to streamline the quantitative assessment of SWE images will potentially accelerate the adoption of the combined use of ultrasound and elastography in clinical practice.
•A framework is proposed to segment and classify breast lesions efficiently.•Elastographic features inside the segmented lesion and along the peri-tumoural were used to identify malignant lesions.•Empirical selection of the width of the peri-tumoural rim is not required.•The classification performance is higher than existing methods for analyzing breast ultrasound elastography images.•The framework improves the efficiency for lesion segmentation and classification (20 s to process a lesion).
•First study on automatic segmentation of neonatal ventricles from 3D US images.•Multi-atlas initialized multi-region segmentation approach.•Convex optimization based minimization of the defined ...energy function.•Parallelized GPU implementation.
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Preterm neonates with a very low birth weight of less than 1,500 g are at increased risk for developing intraventricular hemorrhage (IVH). Progressive ventricle dilatation of IVH patients may cause increased intracranial pressure, leading to neurological damage, such as neurodevelopmental delay and cerebral palsy. The technique of 3D ultrasound (US) imaging has been used to quantitatively monitor the ventricular volume in IVH neonates, which may elucidate the ambiguity surrounding the timing of interventions in these patients as 2D clinical US imaging relies on linear measurement and visual estimation of ventricular dilation from a series of 2D slices. To translate 3D US imaging into the clinical setting, a fully automated segmentation algorithm is necessary to extract the ventricular system from 3D neonatal brain US images. In this paper, an automatic segmentation approach is proposed to delineate lateral ventricles of preterm neonates from 3D US images. The proposed segmentation approach makes use of phase congruency map, multi-atlas initialization technique, atlas selection strategy, and a multiphase geodesic level-sets (MGLS) evolution combined with a spatial shape prior derived from multiple pre-segmented atlases. Experimental results using 30 IVH patient images show that the proposed GPU-implemented approach is accurate in terms of the Dice similarity coefficient (DSC), the mean absolute surface distance (MAD), and maximum absolute surface distance (MAXD). To the best of our knowledge, this paper reports the first study on automatic segmentation of the ventricular system of premature neonatal brains from 3D US images.
Abstract Prostate segmentation from transrectal ultrasound (TRUS) images plays an important role in the diagnosis and treatment planning of prostate cancer. In this paper, a fully automatic ...slice-based segmentation method was developed to segment TRUS prostate images. The initial prostate contour was determined using a novel method based on the radial bas-relief (RBR) method and a false edge removal algorithm we proposed here. 2D slice-based propagation was used in which the contour on each image slice was deformed using a level-set evolution model driven by edge-based and region-based energy fields generated by dyadic wavelet transform. The optimized contour on an image slice propagated to the adjacent slice, on which the contour was deformed using the level-set model. The propagation continued until all image slices were segmented. To determine the initial slice where the propagation began, the initial prostate contour was deformed individually on each transverse image. A method was developed to self-assess the accuracy of the deformed contour based on the average image intensity inside and outside of the contour. The transverse image on which highest accuracy was attained was chosen to be the initial slice for the propagation process. Evaluation was performed for 336 transverse images from 15 prostates that include images acquired at mid-gland, base and apex regions of the prostates. The average mean absolute difference (MAD) between algorithm and manual segmentations was 0.79±0.26 mm, which is comparable to results produced by previously published semi-automatic segmentation methods. Statistical evaluation shows that accurate segmentation was not only obtained in mid-gland, but also the base and apex regions.
Purpose:
The peripheral arterial disease is a powerful indicator of coexistent generalized atherosclerosis. As plaques in femoral arteries are diffused and can span a length of 30 cm, a large ...coverage of the arteries is required to assess the full extent of atherosclerosis. Recent development of 3D black‐blood magnetic resonance imaging sequences has allowed fast acquisition of images with an extended longitudinal coverage. Vessel wall volume quantification requires the segmentation of the lumen and outer wall boundaries, and conventional manual planimetry would be too time‐consuming to be feasible for analyzing images with such a large coverage. To address this challenge in image analysis, this work introduces an efficient 3D algorithm to segment the lumen and outer wall boundaries for plaque and vessel wall quantification in the femoral artery.
Methods:
To generate the initial lumen surface, a user identified the location of the lumen centers manually on a set of transverse images with a user‐specified interslice distance (ISD). A number of geometric operators were introduced to automatically adjust the initial lumen surface based on pixel intensity and gradient along the boundary and at the center of each transverse slice. The adjusted surface was optimized by a 3D deformable model driven by the local stiffness force and external force based on image gradient. The optimized lumen surface was expanded to obtain the initial outer wall surface, which was subsequently optimized by the 3D deformable model.
Results:
The algorithm was executed with and without adjustment of the initial lumen surface and for three different selections of ISD: 10, 20, and 30 mm. The segmentation accuracy was improved in a statistically significant way with the introduction of initial lumen surface adjustment, but was insensitive to the ISD setting. When compared with the manual segmentation, the settings with adjustment have, on average, mean absolute differences (MADs) of 0.28 and 0.36 mm, respectively, for lumen and outer wall segmentations, which are significantly lower than those obtained when the adjustment operators were not applied (MAD = 0.43 and 0.59 mm for lumen and outer wall segmentations). The algorithm took about 1% of the time required for manual segmentation to complete segmenting the whole 3D femoral artery.
Conclusions:
The proposed semiautomatic algorithm generated accurate lumen and outer wall boundaries from 3D black‐blood MR images with few user interactions, thereby allowing rapid and streamlined assessment of plaque burden in the femoral arteries.
Dealing with high-dimensional dataset has always been an important problem and feature selection is one of useful tools. In this paper, we develop a new filter based supervised feature selection ...method by combining Trace Ratio Criterion of Linear Discriminant Analysis (TRC-LDA) and group sparsity regularization. The filter based supervised feature selection method is a classifier-independent method while the TRC-LDA criterion is a recently developed criterion for dimensionality reduction that can well preserve discriminative information of dataset. However, there are seldom methods by utilizing TRC-LDA criterion for feature selection. On the other hand, imposing the l2,0-norm to the projection matrix of TRC-LDA will force some rows in it to be zero while keep other rows nonzero making the index of nonzero rows to be the selected features, however, l2,0-nom minimizing problem is NP-hard and intractable. To solve the above problem, in this paper, we develop a new method, namely, Trace Ratio Criterion Discriminative Feature Selection (TRC-DFS), for feature selection. The proposed TRC-DFS has imposed l2,1-norm, i.e. an approximation of l2,0-norm, to the projection matrix W of TRC-LDA to achieve feature selection. As a result, the proposed TRC-DFS can both achieve feature selection as well as capture the discriminative structure of data. We also extend the proposed method with l2,p-norm (0 < p < 1) regularization to grasp more sparse property and develop an iterative approach to calculate the optimal solution, which is rigorously proved to be converged. Extensive simulations based on several real-world datasets verify the effectiveness of the proposed methods.
Multilabel learning, which handles instances associated with multiple labels, has attracted much attention in recent years. Many extant multilabel feature selection methods target global feature ...selection, which means feature selection weights for each label are shared by all instances. Also, many extant multilabel classification methods exploit global label selection, which means labels correlations are shared by all instances. In real-world objects, however, different subsets of instances may share different feature selection weights and different label correlations. In this article, we propose a novel framework with local feature selection and local label correlation, where we assume instances can be clustered into different groups, and the feature selection weights and label correlations can only be shared by instances in the same group. The proposed framework includes a group-specific feature selection process and a label-specific group selection process. The former process projects instances into different groups by extracting the instance-group correlation. The latter process selects labels for each instance based on its related groups by extracting the group-label correlation. In addition, we also exploit the intergroup correlation. These three kinds of group-based correlations are combined to perform effective multilabel classification. The experimental results on various datasets validate the effectiveness of our approach.
Abstract Rapid progression in total plaque area and volume measured from ultrasound images has been shown to be associated with an elevated risk of cardiovascular events. Since atherosclerosis is ...focal and predominantly occurring at bifurcation, biomarkers that are able to quantify the spatial distribution of vessel-wall-plus-plaque thickness ( VET ) change may allow for more sensitive detection of treatment effect. The goal of this paper is to develop simple and sensitive biomarkers to quantify the responsiveness to therapies based on the spatial distribution of VWT-Change on the entire 2D carotid standardized map previously described. Point-wise VWT-Changes computed for each patient was reordered lexicographically to a high-dimensional data node in a graph. A graph-based random walk framework was applied with the novel Weighted Cosine (WCos) similarity function introduced, which was tailored for quantification of responsiveness to therapy. The converging probability of each data node to the VWT regression template in the random walk process served as a scalar descriptor for VWT responsiveness to treatment. The WCos-based biomarker was 14 times more sensitive than the mean VWT-Change in discriminating responsive and unresponsive subjects based on the the p-values obtained in T-tests. The proposed framework was extended to quantify where VWT-Change occurred by including multiple VWT-Change distribution templates representing focal changes at different regions. Experimental results show that the framework was effective in classifying carotid arteries with focal VWT-Change at different locations and may facilitate future investigations to correlate risk of cardiovascular events with the location where focal VWT-Change occurs.
One of the distinct characteristics of radiologists reading multiparametric prostate MR scans, using reporting systems like PI-RADS v2.1, is to score individual types of MR modalities, including ...T2-weighted, diffusion-weighted, and dynamic contrast-enhanced, and then combine these image-modality-specific scores using standardised decision rules to predict the likelihood of clinically significant cancer. This work aims to demonstrate that it is feasible for low-dimensional parametric models to model such decision rules in the proposed Combiner networks, without compromising the accuracy of predicting radiologic labels. First, we demonstrate that either a linear mixture model or a nonlinear stacking model is sufficient to model PI-RADS decision rules for localising prostate cancer. Second, parameters of these combining models are proposed as hyperparameters, weighing independent representations of individual image modalities in the Combiner network training, as opposed to end-to-end modality ensemble. A HyperCombiner network is developed to train a single image segmentation network that can be conditioned on these hyperparameters during inference for much-improved efficiency. Experimental results based on 751 cases from 651 patients compare the proposed rule-modelling approaches with other commonly-adopted end-to-end networks, in this downstream application of automating radiologist labelling on multiparametric MR. By acquiring and interpreting the modality combining rules, specifically the linear-weights or odds ratios associated with individual image modalities, three clinical applications are quantitatively presented and contextualised in the prostate cancer segmentation application, including modality availability assessment, importance quantification and rule discovery.
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.
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