Automated lumen definition from 30 MHz intravascular ultrasound images

One prerequisite for standard clinical use of intravascular ultrasound imaging is rapid evaluation of the data. The main quantities to be extracted from the data are the size and the shape of the lumen. Until now, no accurate, robust and reproducible method to obtain the lumen boundaries from intravascular ultrasound images has been described. In this study, 21 different (semi-)automated binary-segmentation methods for determining the lumen are compared with manual segmentation to find an alternative for the laborious and subjective procedure of manual editing. After a preprocessing step in which the catheter area is filled with lumen-like grey values, all approaches consist of two steps: (i) smoothing the images with different filtering methods and (ii) extracting the lumen by an object definition method. The combination of different filtering methods and object definition methods results in a total of 21 methods and 80 experiments. The results are compared with a reference image, obtained from manual editing, by use of four different quality parameters—two based on squared distances and two based on Mahalanobis distances. The evaluation has been carried out on 15 images, of which seven are obtained before balloon dilation and eight after balloon dilation. While for the post-dilation images no definite conclusions can be drawn, an automated contour model applied to images smoothed with a large kernel appears to be a good alternative to manual contouring. For pre-dilation images a fully automated active contour model, initialized by thresholding, preceded by filtering with a small-scale median filter is the best alternative for manual delineation. The results of this method are even better than manual segmentation, i.e. they are consistently closer to the reference image than the average distance of all individual manual segmentations.     

Automatic coronary wall segmentation in intravascular ultrasound images using binary morphological reconstruction

Intravascular ultrasound (IVUS) image segmentation can provide more detailed vessel and plaque information, resulting in better diagnostics, evaluation and therapy planning. A novel automatic segmentation proposal is described herein; the method relies on a binary morphological object reconstruction to segment the coronary wall in IVUS images. First, a preprocessing followed by a feature extraction block are performed, allowing for the desired information to be extracted. Afterward, binary versions of the desired objects are reconstructed, and their contours are extracted to segment the image. The effectiveness is demonstrated by segmenting 1300 images, in which the outcomes had a strong correlation to their corresponding gold standard. Moreover, the results were also corroborated statistically by having as high as 92.72% and 91.9% of true positive area fraction for the lumen and media adventitia border, respectively. In addition, this approach can be adapted easily and applied to other related modalities, such as intravascular optical coherence tomography and intravascular magnetic resonance imaging.     

Ultrasound image segmentation using spectral clustering

Segmentation of ultrasound images is necessary in a variety of clinical applications, but the development of automatic techniques is still an open problem. Spectral clustering techniques have recently become popular for data and image analysis. In particular, image segmentation has been proposed via the normalized cut (NCut) criterion. This article describes an initial investigation to determine the suitability of such segmentation techniques for ultrasound images. The adaptation of the NCut technique to ultrasound is described first. Segmentation is then performed on simulated ultrasound images. Tests are also performed on abdominal and fetal images with the segmentation results compared to manual segmentation. The success of the segmentation on these test cases warrants further research into NCut-based segmentation of ultrasound images.     

Segmentation of lumen and outer wall of abdominal aortic aneurysms from 3D black-blood MRI with a registration based geodesic active contour model

Segmentation of the geometric morphology of abdominal aortic aneurysm is important for interventional planning. However, the segmentation of both the lumen and the outer wall of aneurysm in magnetic resonance (MR) image remains challenging. This study proposes a registration based segmentation methodology for efficiently segmenting MR images of abdominal aortic aneurysms. The proposed methodology first registers the contrast enhanced MR angiography (CE-MRA) and black-blood MR images, and then uses the Hough transform and geometric active contours to extract the vessel lumen by delineating the inner vessel wall directly from the CE-MRA. The proposed registration based geometric active contour is applied to black-blood MR images to generate the outer wall contour. The inner and outer vessel wall are then fused presenting the complete vessel lumen and wall segmentation. The results obtained from 19 cases showed that the proposed registration based geometric active contour model was efficient and comparable to manual segmentation and provided a high segmentation accuracy with an average Dice value reaching 89.79%.     

The reproducibility of intravascular ultrasound imaging in vitro

To determine which factors may affect the image quality when an intravascular ultrasound catheter is used in vivo, the influence of blood, temperature change, and contrast media were evaluated. In addition, to confirm the reproducibility of intravascular ultrasound imaging to measure cross-sectional lumen area, intraobserver and interobserver variability were determined. The findings indicated that ultrasound images in blood are mildly attenuated, that changes from room temperature to body temperature do not have a significant impact on the image quality, that contrast media attenuates the image intensity in a dose-dependent manner, and that the intravascular ultrasound imaging catheter provides a reproducible method for measuring arterial lumen area with excellent intraobserver and interobserver correlation.     

Automatic recognition of the common carotid artery in longitudinal ultrasound B-mode scans

Many morphological and dynamic properties of the common carotid artery (CCA), e.g. lumen diameter, distension and wall thickness, can be measured non-invasively with ultrasound (US) techniques. As common to other medical image segmentation processes, this requires as a preliminary step the manual recognition of the artery of interest within the ultrasound image. In real-time US imaging, such manual initialization procedure interferes with the difficult task of the sonographer to select and maintain a proper image scan plane. Even for off-line US segmentation the requirement for human supervision and interaction precludes full automation. To eliminate user interference and to speed up processing for both real-time and off-line applications, we developed an algorithm for the automatic artery recognition in longitudinal US scans of the CCA. It acts directly on the envelopes of received radio frequency echo signals, eventually composing the ultrasound image. In order to properly exploit the information content of the arterial structure the envelopes are decimated, according to the two-dimensional resolution characteristics of the echo system, thereby substantially decreasing computational load. Subsequently, based upon the expected diameter range and a priori knowledge of the typical pattern in the echo envelope of the arterial wall-lumen complex, parametrical template matching is performed, resulting in the location of the lumen position along each echo line considered. Finally, in order to reject incorrect estimates, a spatial and temporal clustering method is applied. Adequate values for the parameters involved in the processing are obtained via off-line testing of the proposed algorithm on 128 echo data recordings from 45 subjects. Using those robust parameter values, correct and fast recognition of the artery is achieved in more than 98% of the 6185 processed frames. Since these results are obtained via rigorous data decimation and using a cascade of rather simple steps, the proposed automatic algorithm is suitable for real-time recognition of the CCA.     

Image analysis techniques for automated IVUS contour detection

Intravascular ultrasound (IVUS) constitutes a valuable technique for the diagnosis of coronary atherosclerosis. The detection of lumen and media-adventitia borders in IVUS images represents a necessary step towards the reliable quantitative assessment of atherosclerosis. In this work, a fully automated technique for the detection of lumen and media-adventitia borders in IVUS images is presented. This comprises two different steps for contour initialization: one for each corresponding contour of interest and a procedure for the refinement of the detected contours. Intensity information, as well as the result of texture analysis, generated by means of a multilevel discrete wavelet frames decomposition, are used in two different techniques for contour initialization. For subsequently producing smooth contours, three techniques based on low-pass filtering and radial basis functions are introduced. The different combinations of the proposed methods are experimentally evaluated in large datasets of IVUS images derived from human coronary arteries. It is demonstrated that our proposed segmentation approaches can quickly and reliably perform automated segmentation of IVUS images. (E-mail: mpapad@iti.gr).     

Calcification segmentation based on a different scales superpixels saliency detection algorithm

Accurate detection of breast tumor calcifications is of great significance in assisting doctors’ diagnosis to improve the accuracy of breast cancer early detection. In this article, a different scale of superpixels saliency detection algorithm is used to segment calcifications in breast tumor ultrasound images based on a simple linear iterative cluster. First, a multi-scale saliency segmentation algorithm was used to divide the tumor region of different sizes and weak calcification (Wca) was extracted according to uneven gray distribution and texture contrast between regions. Second, based on single-scale superpixel segmentation of the original image, the strong calcification extraction map was calculated by measuring gray value difference and calcification gray distance features. Finally, the final calcification extraction map was obtained by combining the strong and weak calcification extraction maps. The detection algorithm proposed in this article could effectively detect calcifications in breast ultrasound images.     

基于图割与粗糙集的MRI脑部肿瘤图像检索方法

背景:基于内容的医学图像检索是一门涉及多领域的学科,由于各种医学图像的成像原理不同,产生的图像在颜色、纹理和形状等视觉特征方面存在差别,使得此方法的实现还存在许多需要解决的问题.目的:针对基于内容的医学图像检索中存在特征提取困难、检索时间长的问题,提出一种基于图割与粗糙集结合的相似图像检索方法.方法:为克服图割仅适用于较少象素的图像和倾向于小割集的缺陷,首先对图像进行聚类,然后构建图像的Gomory-Hu割树,按割值大小依次去掉值较小的边,提取出图像的特征子图并构建特征库.为实现快速检索,借助粗糙集对特征库中的特征进行约简,有效减少参与相似性比较的特征数量.并将此方法应用到MRI脑部肿瘤图像的检索.结果与结论:实验结果表明该方法能快速有效地检索出MRI脑部图像库中的肿瘤图像,检索的平均查准率为78.4%,平均查全率为62.9%.     

Automatic segmentation of the lumen region in intravascular images of the coronary artery

Image assessment of the arterial system plays an important role in the diagnosis of cardiovascular diseases. The segmentation of the lumen and media-adventitia in intravascular (IVUS) images of the coronary artery is the first step towards the evaluation of the morphology of the vessel under analysis and the identification of possible atherosclerotic lesions. In this study, a fully automatic method for the segmentation of the lumen in IVUS images of the coronary artery is presented. The proposed method relies on the K-means algorithm and the mean roundness to identify the region corresponding to the potential lumen. An approach to identify and eliminate side branches on bifurcations is also proposed to delimit the area with the potential lumen regions. Additionally, an active contour model is applied to refine the contour of the lumen region. In order to evaluate the segmentation accuracy, the results of the proposed method were compared against manual delineations made by two experts in 326 IVUS images of the coronary artery. The average values of the Jaccard measure, Hausdorff distance, percentage of area difference and Dice coefficient were 0.88 ± 0.06, 0.29 ± 0.17  mm, 0.09 ± 0.07 and 0.94 ± 0.04, respectively, in 324 IVUS images successfully segmented. Additionally, a comparison with the studies found in the literature showed that the proposed method is slight better than the majority of the related methods that have been proposed. Hence, the new automatic segmentation method is shown to be effective in detecting the lumen in IVUS images without using complex solutions and user interaction.     

Fetal abdominal contour extraction and measurement in ultrasound images

A novel method is developed for the fetal abdominal contour extraction and measurement in ultrasound images. Fetal abdominal circumference (AC) is one of the standardized measurements in the antepartum ultrasound monitoring. Among several standardized measurements, AC is best correlated with fetal growth but is also the most difficult to be accurately measured. To overcome the difficulties in the abdominal contour extraction, the proposed method is a four-step procedure that integrates several image segmentation techniques. The proposed method is able to make the best use of the strength of different segmentation algorithms, while avoiding their deficiencies. An enhanced instantaneous coefficient of variation (ICOV) edge detector is first developed to detect edges of the abdominal contour and alleviate the effects of most speckle noise. Then, the Fuzzy C-Means clustering is employed to distinguish salient edges attributable to the abdominal contour from weak edges due to the other texture. Subsequently, the iterative Hough transform is applied to determine an elliptical contour and obtain an initial estimation of the AC. Finally, the gradient vector field (GVF) snake adapts the initial ellipse to the real edges of the abdominal contour. Quantitative validation of the proposed method on synthetic images under different imaging conditions achieves satisfactory segmentation accuracy (98.78%+/-0.16%). Experiments on 150 clinical images are carried out in three aspects: comparisons between inter-observer and inter-run variation, the fitness analysis between the automatically detected ellipse and the manual delineation, and the accuracy comparisons between automatic measurements and manual measurements in estimation of fetal weight (EFW). Experimental results show that the proposed method can provide consistent and accurate measurements. The reductions of the mean absolute difference and the standard deviation of EFW based on automatic measurements are about 1.2% and 2.1%, respectively, which indicate its potential in clinical antepartum monitoring application.     

Intraluminal ultrasonic palpation: assessment of local and cross-sectional tissue stiffness

Many intravascular therapeutic techniques for the treatment of significant atherosclerotic lesions are mechanical in nature: examples are angioplasty, stenting and atherectomy. The selection of the most adequate treatment would be advantageously aided by knowledge of the mechanical properties of the lesion and surrounding tissues. Based on the success of intravascular ultrasound (IVUS) in accurately depicting the morphology of atheromatous lesions, ultrasonic tissue characterisation has been proposed as a tool to determine the composition of atheroma. We describe the addition of local compliance information to the IVUS image in the form of a colour-coded line congruent with the lumen perimeter. The technique involves analysis of echo signals obtained at two or more states of incremental intravascular pressure. Using vessel phantoms and specimens, we demonstrate the utility of intravascular compliance imaging. The palpograms are able to identify lesions of different elasticity independently of the echogenicity contrast, because the information provided by the elastograms is generally independent of that obtained from the IVUS image. Thus, the palpogram can complement the characterisation of lesion from the IVUS image. We also describe cross-sectional measures of elasticity that are based on the elastogram. Finally, natural extensions of intravascular palpation to other endoluminal ultrasound applications are proposed.     

Prostate boundary detection and volume estimation using TRUS images for brachytherapy applications

Objective: Authors propose a semi-automatic segmentation algorithm for three-dimensional prostate boundary detection from trans-rectal ultrasound images. As a part of brachytherapy treatment with seeds for early stage prostate cancer, a patient’s prostate is scanned using a trans-rectal ultrasound probe, its boundary is manually outlined, and its volume is estimated for dosimetry purposes. Proposed algorithm requires a reduced amount of radiologist’s input, and thus speeds up the surgical procedure. Methods: The proposed segmentation algorithm utilizes texture differences between ultrasound images of the prostate and the surrounding tissues. It is carried out in the polar coordinate system and uses three-dimensional data correlation to improve the smoothness and reliability of the segmentation. The algorithm is applied to axial trans-rectal ultrasound images and the results are compared to the “ground truth” set by manual prostate boundary outlining (by experienced radiologist). Method is validated on six patients. Results: In our tests, the proposed algorithm estimated prostate volume within 95% of the original radiologist’s estimate. Conclusions: The boundary segmentation obtained from the algorithm can reduce manual input by a factor of 3, without significantly affecting the accuracy of the segmentation. The reduction in the manual input reduces the overall brachytherapy procedure time.     

Method for Carotid Artery 3-D Ultrasound Image Segmentation Based on CSWin Transformer

Precise segmentation of carotid artery (CA) structure is an important prerequisite for the medical assessment and detection of carotid plaques. For automatic segmentation of the media–adventitia boundary (MAB) and lumen–intima boundary (LIB) in 3-D ultrasound images of the CA, a U-shaped CSWin transformer (U-CSWT) is proposed. Both the encoder and decoder of the U-CSWT are composed of hierarchical CSWT modules, which can capture rich global context information in the 3-D image. Experiments were performed on a 3-D ultrasound image data set of the CA, and the results indicate that the U-CSWT performs better than other convolutional neural network (CNN)-based and CNN–transformer hybrid methods. The model yields Dice coefficients of 94.6 ± 3.0% and 90.8 ± 5.1% for the MAB and LIB in the common carotid artery (CCA) and 92.9 ± 4.9% and 89.6 ± 6.2% for MAB and LIB in the bifurcation, respectively. Our U-CSWT is expected to become an effective method for automatic segmentation of 3-D ultrasound images of CA.     

Automated Segmentation of Ultrasonic Breast Lesions Using Statistical Texture Classification and Active Contour Based on Probability Distance

Because of its complicated structure, low signal/noise ratio, low contrast and blurry boundaries, fully automated segmentation of a breast ultrasound (BUS) image is a difficult task. In this paper, a novel segmentation method for BUS images without human intervention is proposed. Unlike most published approaches, the proposed method handles the segmentation problem by using a two-step strategy: ROI generation and ROI segmentation. First, a well-trained texture classifier categorizes the tissues into different classes, and the background knowledge rules are used for selecting the regions of interest (ROIs) from them. Second, a novel probability distance-based active contour model is applied for segmenting the ROIs and finding the accurate positions of the breast tumors. The active contour model combines both global statistical information and local edge information, using a level set approach. The proposed segmentation method was performed on 103 BUS images (48 benign and 55 malignant). To validate the performance, the results were compared with the corresponding tumor regions marked by an experienced radiologist. Three error metrics, true-positive ratio (TP), false-negative ratio (FN) and false-positive ratio (FP) were used for measuring the performance of the proposed method. The final results (TP = 91.31%, FN = 8.69% and FP = 7.26%) demonstrate that the proposed method can segment BUS images efficiently, quickly and automatically. (E-mail: hengda.cheng@usu.edu)     

超声图像中基于经验模态分解自动提取内-中膜厚度

目的为改善传统人工标记测量血管内-中膜厚度(IMT)的准确性和稳定性,提出基于图像分割技术的经验模态分解(EMD)改进算法。方法采用EMD改进算法去噪,根据血管壁的特点,在其中的极值点插值步骤使用非均匀的二维B样条函数,在水平和垂直方向上控制网格的密度不同,分别满足不同的分辨精度和平滑程度要求,改进了原始的二维EMD算法;然后通过K均值方法从图像中分离出血管腔、血管壁和其他组织,使用数学形态学算法逐步得到最终的内-中膜组织分割结果。结果改进EMD算法取得了较好的重建和滤波效果,有效克服了超声图像的强噪声和低分辨力对图像分割的限制,整个算法分割比较准确,算法复杂度相对较小。结论改进EMD算法是在超声图像中自动提取内-中膜的较有潜力的方法,能有效去除超声噪声,同时保留条纹结构的细节和边缘信息,有望于其他强噪声环境下提取条纹结构。     

Assessment of arterial disease and arterial reconstructions by intravascular ultrasound

Clinical application of intravascular ultrasound to assess arterial atherosclerotic disease was introduced in humans after extensivein vitro andin vivo animal studies. Real-time images, obtained with a 30 MHz element mounted on a 5 F catheter, consistently confirmed angiographic images, up till now considered to be the gold standard. In addition to these data, ultrasonic cross-sectional imaging provided information on the composition of atheroselectic lesions and the size and shape of the lumen. Based on the experimentally derived criteria for tissue characterization, a better insight into arterial morphology could be obtained, allowing improved planning of interventional or reconstructional procedures. Moreover intravascular ultrasound has proved valuable as a post-interventional procedure to monitor and assess the quality of interventional results. The ultrasound images are clearly superior to angiographic studies, albeit the ultrasonic information is an adjunct to angiography and, as yet, not a substitute. We present our initial experience with intravascular ultrasound obtained in patients with substantial peripheral arterial disease.     

超声影像下基于ResNet34-Unet的静脉分割方法研究

静脉超声图像存在噪点多、阈值分割效果不佳的问题，对此本文提出一种基于ResNet34主干网络的ResNet34-UNet分割网络模型，利用ResNet34网络残差学习的结构特点，在保证网络能够提取充足图像特征的前提下，有效避免梯度消失和网络退化问题，且34层的网络深度维持了较小的网络规模；利用U-Net结构特有的长连接（Skip Connection）模块，将静脉超声图像的深层特征与浅层特征有效融合，使静脉的识别精度得以较大幅度的提升，实现了静脉边缘的平滑分割。将300张静脉超声图像作为训练集，200张作为测试集，通过随机旋转、翻转、投影等操作进行数据集的增强，经过十轮迭代训练后得到模型的准确度（ACC）达96.3%，较全卷积神经网络（FCN）高5.9%，较DeepLab v3+高5.2%。结果表明基于ResNet34-UNet的静脉分割方法能够准确地分割静脉超声图像，为后续超声影像下静脉的自动识别与跟踪提供了技术参考。     

Registration-based segmentation of three-dimensional ultrasound images for quantitative measurement of fetal craniofacial structure

Segmentation of a fetal head from three-dimensional (3-D) ultrasound images is a critical step in the quantitative measurement of fetal craniofacial structure. However, two main issues complicate segmentation, including fuzzy boundaries and large variations in pose and shape among different ultrasound images. In this article, we propose a new registration-based method for automatically segmenting the fetal head from 3-D ultrasound images. The proposed method first detects the eyes based on Gabor features to identify the pose of the fetus image. Then, a reference model, which is constructed from a fetal phantom and contains prior knowledge of head shape, is aligned to the image via feature-based registration. Finally, 3-D snake deformation is utilized to improve the boundary fitness between the model and image. Four clinically useful parameters including inter-orbital diameter (IOD), bilateral orbital diameter (BOD), occipital frontal diameter (OFD) and bilateral parietal diameter (BPD) are measured based on the results of the eye detection and head segmentation. Ultrasound volumes from 11 subjects were used for validation of the method accuracy. Experimental results showed that the proposed method was able to overcome the aforementioned difficulties and achieve good agreement between automatic and manual measurements.