Three-dimensional carotid ultrasound segmentation variability dependence on signal difference and boundary orientation

Segmentation of Carotid Arteries From Three-Dimensional Black-Blood Magnetic Resonance Imaging With Sparse Annotation Using a Multi-Dimensional Hybrid Model

Quantification of carotid atherosclerosis is important in monitoring patients at risk of cardiovascular events and in evaluating therapies. High-resolution 3D carotid magnetic resonance imaging (MRI) has been developed to provide extended coverage of the carotid arteries. However, the extended coverage poses a challenge as several hundreds of 2D axial images are required to be segmented for analysis. We propose a multi-dimensional hybrid framework that requires only a sparse set of manual segmentation. Dense surrogate ground truth boundaries required to train the framework are automatically generated by propagating the sparse manual segmentation using the proposed region of interest (ROI) U-Net. Furthermore, the Point U-Net was developed to generate surrogate ground truth for carotid branches without manual segmentation. The proposed framework leverages the advantages of 3D and 2D convolution neural networks (CNNs) to segment the outer wall and lumen from 3D MRI. The 3D multiscale U-Net provides a rough outer wall segmentation, which serves as the ROI to guide outer wall and lumen segmentation by the 2D ROI U-Net. The 3D Multiscale U-Net localizes the ROI automatically, bypassing the need for manual ROI identification. The 3D Multiscale U-Net was further improved by a 3D inception module installed at the bottleneck and the novel loss functions that promote longitudinal continuity and minimize the overlap of the internal and external carotid arteries. Extensive evaluation on the publicly available Carotid Artery Vessel Wall Segmentation challenge dataset shows that our approach outperforms the top-ranked solution in the challenge and state-of-the-art segmentation methods.

A Generalized Approach for Automatic 3-D Geometry Assessment of Blood Vessels in Transverse Ultrasound Images Using Convolutional Neural Networks

Accurate 3-D geometries of arteries and veins are important clinical data for diagnosis of arterial disease and intervention planning. Automatic segmentation of vessels in the transverse view suffers from the low lateral resolution and contrast. Convolutional neural networks are a promising tool for automatic segmentation of medical images, outperforming the traditional segmentation methods with high robustness. In this study, we aim to create a general, robust, and accurate method to segment the lumen-wall boundary of healthy central and peripheral vessels in large field-of-view freehand ultrasound (US) datasets. Data were acquired using the freehand US, in combination with a probe tracker. A total of ±36 000 cross-sectional images, acquired in the common, internal, and external carotid artery ( N = 37 ), in the radial, ulnar artery, and cephalic vein ( N = 12 ), and in the femoral artery ( N = 5 ) were included. To create masks (of the lumen) for training data, a conventional automatic segmentation method was used. The neural networks were trained on: 1) data of all vessels and 2) the carotid artery only. The performance was compared and tested using an open-access dataset. The recall, precision, DICE, and intersection over union (IoU) were calculated. Overall, segmentation was successful in the carotid and peripheral arteries. The Multires U-net architecture performs best overall with DICE = 0.93 when trained on the total dataset. Future studies will focus on the inclusion of vascular pathologies.

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.

Breast lesion classification based on supersonic shear-wave elastography and automated lesion segmentation from B-mode ultrasound images

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 computer-aided method for automatic localization and thickness measurement of peritoneum in ultrasound images

This paper presents a method of automatically measuring peritoneum thickness in ultrasound images. In our previous work, a method of manually selecting the region of interest (ROI) area has been developed. To achieve an automatic ROI area selection, two phases: Gaussian high-pass filtering and bilateral filtering, are used in the proposed method. In the bilateral filtering phase, the ultrasound image is enhanced for obtaining more details of the peritoneum so that probable areas can be extracted. In the other phase, the ultrasound image is processed with a Gaussian high pass filter, and the result is used to locate the precise area of peritoneum in the first phase result. The experimental results show that the proposed method has high accuracy and fast processing speed in determining the peritoneum area and its thickness distribution.