Fully automated segmentation and tracking of the intima media thickness in ultrasound video sequences of the common carotid artery

Abstract—The robust identification and measurement of the intima media thickness (IMT) has a high clinical relevance because it represents one of the most precise predictors used in the assessment of potential future cardiovascular events. To facilitate the analysis of arterial wall thickening in serial clinical investigations, in this paper we have developed a novel fully automatic algorithm for the segmentation, measurement, and tracking of the intima media complex (IMC) in B-mode ultrasound video sequences. The proposed algorithm entails a two-stage image analysis process that initially addresses the segmentation of the IMC in the first frame of the ultrasound video sequence using a model-based approach; in the second step, a novel customized tracking procedure is applied to robustly detect the IMC in the subsequent frames. For the video tracking procedure, we introduce a spatially coherent algorithm called adaptive normalized correlation that prevents the tracking process from converging to wrong arterial interfaces. This represents the main contribution of this paper and was developed to deal with inconsistencies in the appearance of the IMC over the cardiac cycle. The quantitative evaluation has been carried out on 40 ultrasound video sequences of the common carotid artery (CCA) by comparing the results returned by the developed algorithm with respect to ground truth data that has been manually annotated by clinical experts. The measured IMTmean ± standard deviation recorded by the proposed algorithm is 0.60 mm ± 0.10, with a mean coefficient of variation (CV) of 2.05%, whereas the corresponding result obtained for the manually annotated ground truth data is 0.60 mm ± 0.11 with a mean CV equal to 5.60%. The numerical results reported in this paper indicate that the proposed algorithm is able to correctly segment and track the IMC in ultrasound CCA video sequences, and we were encouraged by the stability of our technique when applied to data captured under different imaging conditions. Future clinical studies will focus on the evaluation of patients that are affected by advanced cardiovascular conditions such as focal thickening and arterial plaques

Statistical Region Based Segmentation of Ultrasound Images

Segmentation of ultrasound images is a challenging problem due to speckle, which corrupts the image and can result in weak or missing image boundaries, poor signal to noise ratio, and diminished contrast resolution. Speckle is a random interference pattern that is characterized by an asymmetric distribution as well as significant spatial correla- tion. These attributes of speckle are challenging to model in a segmentation approach, so many previous ultrasound segmentation methods simplify the problem by assuming that the speckle is white and/or Gaussian distributed. Unlike these methods, in this paper we present an ultrasound-specific segmentation approach that addresses both the spatial correlation of the data as well as its intensity distribution. We first decorrelate the image and then apply a region-based active contour whose motion is derived from an appropri- ate parametric distribution for maximum likelihood image segmentation. We consider zero-mean complex Gaussian, Rayleigh, and Fisher-Tippett flows, which are designed to model fully formed speckle in the in-phase/quadrature (IQ), envelope detected, and display (log compressed) images, respectively. We present experimental results demon- strating the effectiveness of our method, and compare the results to other parametric and non-parametric active contours

Automatic segmentation of the lumen of the carotid artery in ultrasound B-mode images

A new algorithm is proposed for the segmentation of the lumen and bifurcation boundaries of the carotid artery in B-mode ultrasound images. It uses the hipoechogenic characteristics of the lumen for the identification of the carotid boundaries and the echogenic characteristics for the identification of the bifurcation boundaries. The image to be segmented is processed with the application of an anisotropic diffusion filter for speckle removal and morphologic operators are employed in the detection of the artery. The obtained information is then used in the definition of two initial contours, one corresponding to the lumen and the other to the bifurcation boundaries, for the posterior application of the Chan-vese level set segmentation model. A set of longitudinal B-mode images of the common carotid artery (CCA) was acquired with a GE Healthcare Vivid-e ultrasound system (GE Healthcare, United Kingdom). All the acquired images include a part of the CCA and of the bifurcation that separates the CCA into the internal and external carotid arteries. In order to achieve the uppermost robustness in the imaging acquisition process, i.e., images with high contrast and low speckle noise, the scanner was adjusted differently for each acquisition and according to the medical exam. The obtained results prove that we were able to successfully apply a carotid segmentation technique based on cervical ultrasonography. The main advantage of the new segmentation method relies on the automatic identification of the carotid lumen, overcoming the limitations of the traditional methods

Automating Carotid Intima-Media Thickness Video Interpretation with Convolutional Neural Networks

Cardiovascular disease (CVD) is the leading cause of mortality yet largely preventable, but the key to prevention is to identify at-risk individuals before adverse events. For predicting individual CVD risk, carotid intima-media thickness (CIMT), a noninvasive ultrasound method, has proven to be valuable, offering several advantages over CT coronary artery calcium score. However, each CIMT examination includes several ultrasound videos, and interpreting each of these CIMT videos involves three operations: (1) select three end-diastolic ultrasound frames (EUF) in the video, (2) localize a region of interest (ROI) in each selected frame, and (3) trace the lumen-intima interface and the media-adventitia interface in each ROI to measure CIMT. These operations are tedious, laborious, and time consuming, a serious limitation that hinders the widespread utilization of CIMT in clinical practice. To overcome this limitation, this paper presents a new system to automate CIMT video interpretation. Our extensive experiments demonstrate that the suggested system significantly outperforms the state-of-the-art methods. The superior performance is attributable to our unified framework based on convolutional neural networks (CNNs) coupled with our informative image representation and effective post-processing of the CNN outputs, which are uniquely designed for each of the above three operations.Comment: J. Y. Shin, N. Tajbakhsh, R. T. Hurst, C. B. Kendall, and J. Liang. Automating carotid intima-media thickness video interpretation with convolutional neural networks. CVPR 2016, pp 2526-2535; N. Tajbakhsh, J. Y. Shin, R. T. Hurst, C. B. Kendall, and J. Liang. Automatic interpretation of CIMT videos using convolutional neural networks. Deep Learning for Medical Image Analysis, Academic Press, 201

Image segmentation and reconstruction of 3D surfaces from carotid ultrasound images

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Adjusting the input ultrasound image data and the atherosclerotic plaque detection in the carotid artery by the FOTOMNG system

Stroke is the third most frequent cause of death. Specifically, ischemic stroke accounts for the largest group of this kind of cases. Despite all the advances in medical therapeutic methods, no methods that would reliably reduce mortality from ischemic stroke have been found. Prevention is still the most significant way to combat stroke. When the frequent cause of ischemic stroke is atherosclerotic plaque in the carotid artery, its exploration can help to determine the development of the disease. These problems were very extensively discussed in October 2013 during the XVI International Neurosonology Congress in Sofia organized under the auspices of World Research Neurosonology Group, Bulgarian Neurosonology and Cerebral Hemodynamics Association. Our goal was to develop special modules for carotid artery picture processing (AVI file processing, reparation and reconstruction) and modules containing tools for automated carotid artery plaque detection; and to solve its measurement and three-dimensional modelling of the carotid artery and the plaque. New modules were implemented into the FOTOMNG system and tested on appropriate input data files, which verified their functionality and applicability.Web of Scienc

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world