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

Classification approach for diagnosis of arteriosclerosis using B-mode ultrasound carotid images

Tese de mestrado. Engenharia Biomédica. Faculdade de Engenharia. Universidade do Porto. 201

Estimation of a Coronary Vessel Wall Deformation with High-Frequency Ultrasound Elastography

Elastography, which is based on applying pressure and estimating the resulting deformation, involves the forward problem to obtain the strain distributions and inverse problem to construct the elastic distributions consistent with the obtained strains on observation points. This thesis focuses on the former problem whose solution is used as an input to the latter problem. The aim is to provide the inverse problem community with accurate strain estimates of a coronary artery vessel wall. In doing so, a new ultrasonic image-based elastography approach is developed. Because the accuracy and quality of the estimated strain fields depend on the resolution level of the ultrasound image and to date best resolution levels obtained in the literature are not enough to clearly see all boundaries of the artery, one of the main goals is to acquire high-resolution coronary vessel wall ultrasound images at different pressures. For this purpose, first an experimental setup is designed to collect radio frequency (RF) signals, and then image formation algorithm is developed to obtain ultrasound images from the collected signals. To segment the noisy ultrasound images formed, a geodesic active contour-based segmentation algorithm with a novel stopping function that includes local phase of the image is developed. Then, region-based information is added to make the segmentation more robust to noise. Finally, elliptical deformable template is applied so that a priori information regarding the shape of the arteries could be taken into account, resulting in more stable and accurate results. The use of this template also implicitly provides boundary point correspondences from which high-resolution, size-independent, non-rigid and local strain fields of the coronary vessel wall are obtained.Ph.D.Committee Chair: Benkeser, Paul; Committee Member: Koblasz, Arthur; Committee Member: Skrinjar, Oskar; Committee Member: Vidakovic, Brani; Committee Member: Yezzi, Anthon

Using a distance map and an active contour model to segment the carotid artery boundary from the lumen contour in proton density weighted magnetic resonance images

Segmentation methods have assumed an important role in image-based diagnosis of several cardiovascular diseases. Particularly, the segmentation of the boundary of the carotid artery is demanded in the detection and characterization of atherosclerosis and assessment of the disease progression. In this article, a fully automatic approach for the segmentation of the carotid artery boundary in Proton Density Weighted Magnetic Resonance Images is presented. The approach relies on the expansion of the lumen contour based on a distance map built using the gray-weighted distance relative to the center of the identified lumen region in the image under analysis. Then, a Snake model with a modified weighted external energy based on the combination of a balloon force along with a Gradient Vector Flow-based external energy is applied to the expanded contour towards the correct boundary of the carotid artery. The average values of the Dice coefficient, Polyline distance, mean contour distance and centroid distance found in the segmentation of 139 carotid arteries were 0.83 ± 0.11, 2.70 ± 1.69 pixels, 2.79 ± 1.89 pixels and 3.44 ± 2.82 pixels, respectively. The segmentation results of the proposed approach were also compared against the ones obtained by related approaches found in the literature, which confirmed the outstanding performance of the new approach. Additionally, the proposed weighted external energy for the Snake model was shown to be also robust to carotid arteries with large thickness and weak boundary image edges. (c) 202

Vascular Segmentation Algorithms for Generating 3D Atherosclerotic Measurements

Atherosclerosis manifests as plaques within large arteries of the body and remains as a leading cause of mortality and morbidity in the world. Major cardiovascular events may occur in patients without known preexisting symptoms, thus it is important to monitor progression and regression of the plaque burden in the arteries for evaluating patient\u27s response to therapy. In this dissertation, our main focus is quantification of plaque burden from the carotid and femoral arteries, which are major sites for plaque formation, and are straight forward to image noninvasively due to their superficial location. Recently, 3D measurements of plaque burden have shown to be more sensitive to the changes of plaque burden than one-/two-dimensional measurements. However, despite the advancements of 3D noninvasive imaging technology with rapid acquisition capabilities, and the high sensitivity of the 3D plaque measurements of plaque burden, they are still not widely used due to the inordinate amount of time and effort required to delineate artery walls plus plaque boundaries to obtain 3D measurements from the images. Therefore, the objective of this dissertation is developing novel semi-automated segmentation methods to alleviate measurement burden from the observer for segmentation of the outer wall and lumen boundaries from: (1) 3D carotid ultrasound (US) images, (2) 3D carotid black-blood magnetic resonance (MR) images, and (3) 3D femoral black-blood MR images. Segmentation of the carotid lumen and outer wall from 3DUS images is a challenging task due to low image contrast, for which no method has been previously reported. Initially, we developed a 2D slice-wise segmentation algorithm based on the level set method, which was then extended to 3D. The 3D algorithm required fewer user interactions than manual delineation and the 2D method. The algorithm reduced user time by ≈79% (1.72 vs. 8.3 min) compared to manual segmentation for generating 3D-based measurements with high accuracy (Dice similarity coefficient (DSC)\u3e90%). Secondly, we developed a novel 3D multi-region segmentation algorithm, which simultaneously delineates both the carotid lumen and outer wall surfaces from MR images by evolving two coupled surfaces using a convex max-flow-based technique. The algorithm required user interaction only on a single transverse slice of the 3D image for generating 3D surfaces of the lumen and outer wall. The algorithm was parallelized using graphics processing units (GPU) to increase computational speed, thus reducing user time by 93% (0.78 vs. 12 min) compared to manual segmentation. Moreover, the algorithm yielded high accuracy (DSC \u3e 90%) and high precision (intra-observer CV \u3c 5.6% and inter-observer CV \u3c 6.6%). Finally, we developed and validated an algorithm based on convex max-flow formulation to segment the femoral arteries that enforces a tubular shape prior and an inter-surface consistency of the outer wall and lumen to maintain a minimum separation distance between the two surfaces. The algorithm required the observer to choose only about 11 points on its medial axis of the artery to yield the 3D surfaces of the lumen and outer wall, which reduced the operator time by 97% (1.8 vs. 70-80 min) compared to manual segmentation. Furthermore, the proposed algorithm reported DSC greater than 85% and small intra-observer variability (CV ≈ 6.69%). In conclusion, the development of robust semi-automated algorithms for generating 3D measurements of plaque burden may accelerate translation of 3D measurements to clinical trials and subsequently to clinical care

Desarrollo de técnicas específicas de procesado de imagen para su aplicación a la medida del grosor íntima-media de la arteria carótida sobre imágenes de ultrasonidos

[ESP] Las enfermedades cardiovasculares son una de las principales causas de mortalidad del mundo. Tras la mayoría de muertes por enfermedad cardiovascular la principal responsable es la arteriosclerosis. La arteriosclerosis consiste en un engrosamiento progresivo del tejido vascular que reduce la elasticidad de los vasos sanguíneos afectados y puede llegar incluso a obstruirlos. Esta enfermedad se desarrolla en la infancia y adolescencia, pudiendo llegar a pasar desapercibida toda la vida o bien actuando como detonador de otras afecciones más serias, como infartos, derrames cerebrales o isquemias. Por todo ello, la detección precoz de la arteriosclerosis resulta de vital importancia. En la actualidad, se viene usando el grosor íntima-media o IMT de la arteria carótida común como indicador fiable y precoz de la arteriosclerosis. Este indicador mide el grosor entre las capas íntima y media de la carótida común en cortes longitudinales de la arteria en imágenes ecográficas. El uso de los ultrasonidos para extraer esta medida, además de barato, resulta no invasivo para el paciente. Sin embargo, esta modalidad de imagen no está exenta de desventajas, como el elevado nivel de ruido que presenta o la alta dependencia del operador. La medida del IMT se realiza de manera manual sobre imágenes de ultrasonidos. Para ello, un observador experto realiza de una a cinco mediciones manuales del grosor de la arteria, donde cada medición consiste en un par de puntos. La presente Tesis Doctoral pretende realizar una segmentación automática de las paredes de la arteria carótida común, de modo que, en lugar de un conjunto de puntos limitado, se pueda extraer el IMT en toda la longitud de la arteria presente en la ecografía. De forma adicional al IMT, se segmenta también la pared anterior de la arteria, pudiendo así proporcionar no sólo el grosor del vaso, sino también el diámetro del cauce de la arteria. El hecho de que sea una segmentación totalmente automática, evita la interacción con el usuario existente en otros métodos de ayuda a la medida del IMT, a la vez que elimina la subjetividad de la medida. Para llevar a cabo una delineación automática de las paredes arteriales, el método desarrollado se implementa en dos etapas básicas. La primera consiste en la detección automática del lumen o cauce por el que fluye la sangre. En una segunda etapa, tras la detección del lumen, se refina el resultado mediante el uso de contornos activos o snakes. En esta Tesis, se emplean contornos activos implementados en el dominio frecuencial. Esta implementación consigue una importante reducción en el coste computacional respecto a la formulación original. Como función de forma se emplean B-splines cúbicas, que presentan una excelente relación entre rendimiento y tiempo de ejecución. El uso de splines proporciona unos contornos finales suaves, lo que evita la típica rugosidad presente en las imágenes de ultrasonidos. Los resultados tras el algoritmo de contornos activos se validan automáticamente para evitar la inclusión de tramos erróneos en las medidas finales. Estos tramos erróneos están ocasionados, principalmente, por la presencia de huecos en la imagen que dificultan el proceso de segmentación, por no proporcionar ninguna información a la imagen de fuerzas externas. Esta validación de los resultados se basa en dos estrategias: estadística y de intensidad. Combinando ambas estrategias se descartan tramos en los que no haya información o en los que la medida resulte anatómicamente improbable. Se ha llevado a cabo una caracterización de los resultados extensa, empleando cuatro métricas distintas para la evaluación de las distancias entre contornos. Tomando el promedio de cuatro segmentaciones manuales como ground truth, se ha comparado con la segmentación automática calculada por el algoritmo de snakes. Tanto las medidas de IMT y del diámetro del lumen, como las distancias entre trazados manuales y automáticos se han evaluado para las cuatro métricas consideradas. En cada caso, se ha calculado el coeficiente de correlación de Pearson, la distribución del error, así como los diagramas de Bland-Altman. A diferencia de otros métodos, en los que la resolución espacial es la misma para todas las imágenes, la configuración del ecógrafo se ha dejado a criterio de los radiólogos. De esta manera, la resolución espacial varía de una imagen a otra, situándose en un rango de 0,029 mm/píxel a 0,082 mm/píxel. Para las 58 imágenes analizadas, se ha logrado medir el IMT en todas ellas. El error medio midiendo el IMT es comparable e incluso inferior al de otros métodos automáticos. El error de segmentación o la distancia directa entre trazados manuales y automáticos es muy bajo, siendo como máximo de unos 2 píxeles para cualquiera de los tres contornos delineados. Como ayuda a la transferencia de resultados, se ha implementado una interfaz de usuario instalable en cualquier PC que implementa el método desarrollado. Esto facilita su uso por parte de personal médico interesado en evaluar el riesgo cardiovascular, tanto de pacientes concretos como de un conjunto de personas para su posterior análisis. Así pues, el método aquí descrito es útil tanto en la práctica clínica diaria como para la realización de estudios del riesgo cardiovascular en la población general. [ENG] Cardiovascular diseases (CVDs) are one of the main causes of death worldwide. Amongst all deaths related to CVDs, atherosclerosis is responsible for the biggest amount of them. Atherosclerosis consists in a progressive thickening of vascular tissue, provoking a loss of elasticity and an increase of thickness of the blood vessels. This thickening can even occlude the affected vessels. This pathology, which is developed during childhood and adolescence, may be unnoticed for years before triggering other more serious conditions, such as infarction, stroke or ischemia. For these reasons, an early detection of atherosclerosis is of paramount interest. Nowadays, the intima-media thickness (IMT) of the common carotid artery is being used as a reliable and early atherosclerosis detector. IMT measures the thickness between intima and media layers of the common carotid artery in longitudinal cuts of the artery in ultrasound images. The use of ultrasound imaging is relatively cheap as well as being non invasive for the patient. However, this image modality presents some drawbacks as it is operator dependent and is quite affected by noise. IMT is manually measured on ultrasound images. With that purpose, an expert observer takes from one to five measurements of the artery thickness. Each manual measurement consists in placing a pair of markers on the artery wall. The present PhD thesis aims at automatically segmenting the layers of the common carotid artery. Thus, instead of a limited set of points, IMT can be measured along the artery length. In addition to IMT, near wall of the artery is also segmented, which provides not only the artery wall thickness, but also the diameter of the artery channel. The fact that the segmentation is completely automatic avoids the user interaction which is present in other IMT measurent methods and, thus, removes the subjetivity of the measurement. To automatically delineate the artery walls, the developed method is implemented in two main stages. The first of them comprises the automatic detection of the lumen, which is the channel where the blood flows. During the second stage, after the lumen detection, the result is refined by means of active contours or snakes. Automatic lumen detection is performed thanks to a correlation of the ultrasound image with a model of the intima-media complex. This step allows the detection of the far wall, which is placed at the bottom of the lumen. In this stage a median filtering is also used to reduce the characteristic speckle noise in ultrasound images. The median filtering gives an image with homogeneous regions, while maintaining the edges of the vessel. Therefore, the median filtering helps in the robustness of the method when there are blood turbulences or backscattering. From the edges of the lumen, active contours or snakes are initialized. The snakes refine the result of the previous stage and provide the different interfaces that define IMT and lumen diameter. Three curves are considered, one for the near wall (located above the lumen in the ultrasound image) and two for the far wall (located under the lumen in the ultrasound image), corresponding to the lumen-intima and media-adventitia interface that determine IMT. In the present work, a Fourier-domain implementation of active contours is used. This implementation achieves a considerable computational cost reduction with respect to the original formulation. Cubic B-splines have been chosen as shape function because of their excellent performance versus running time ratio. Moreover, B-splines provide soft final contours, dealing with the typical rugosity in ultrasound images. Besides the frequency implementation of snakes, the success of the refinement stage is based on the calculation of an adequate external forces image. To compute the external forces image, positive and negative transitions of the intensity gradient of the image are combined in a single image. This combination allows the simultaneous convergence of the near and far wall curves. Although the external force image contains plenty information of the edges to be detected, combined gradient image might not be clear enough. Hence, a morphological reconstruction is performed, being the mask image the combined gradient image. As marker, the cumulative result of morphological openings is used. The openings are performed over the combined gradient image, being the structuring elements lines oriented in the main directions of the gradient image. These main directions are extracted via a Hough transform. Finally, the reconstruction result gives a clearer edge image, in which small structures disappear, making the convergence of the snakes faster. The results after the active contour algorithm are automatically validated to avoid the inclusion of wrong segmented sections in the final measurements. These wrong sections are due to, mainly, the presence of gaps in the image which do not provide any information about the external forces image. This additional validation stage is based on two strategies, statistical and intensity-based. By combining both strategies, we avoid the inclusion of sections with big gaps or in which the measurements are unlikely in anatomical terms. An exhaustive result characterization has been accomplished. Four different metrics have been used to evaluate the distances between contours. Considering the average of four manual measurements as ground truth, automatic segmentation has been compared to manual segmentation. The four considered metrics have been used to evaluate the IMT, the lumen diameter and the distances between manual and automatic delineations. En each case, Pearson’s correlation coefficient, error distribution and Bland-Altman’s plots have been calculated. The ultrasound image dataset has been obtained with a single ultrasound scanner with two different probes, each of them working at different frequency range. Unlike other methods, in which the spatial resolution is not variable, the scanner configuration is flexible for the radiologist, at his/her own discretion. Thus, spatial resolution may vary from one image to another, ranging from 0.029 mm/pixel to 0.082 mm/pixel in our database. IMT has been automatically measured in all 58 images in our database. The images correspond to 35 patients, none of them previously diagnosed with atherosclerosis. The mean IMT error is comparable or even less than the error in other automatic methods. Besides, the distance between manual and automatic contours is very low, being under 2 pixels for any of the three detected interfaces. In order to disseminate and transfer the results, a user interface has been implemented. The program can be installed in any PC, which makes its use easier. Medical staff interested in the evaluation of cardiovascular risk cannot only use the interface for individual patients but also for population-based studies. Thus, the developed method is useful in daily clinical practice as well as in cardiovascular risk studies over population. The segmentation of the carotid artery layers allows the calculation of other measurements and statistics apart from the mean IMT. Maximum and minimum values, texture or ecogenicity in the intimamedia complex region can be extracted. In a similar way, it is possible to monitor the atherosclerosis evolution via registration methods. Therefore, the doctor could easily detect slight changes in the IMT and adjust the treatment and recommendations to the patient in a reliable way. Besides, since the developed technique is based on snakes, the method is easily extendable to 3D cases. This would lead to new applications of the segmentation, such as the analysis of the IMT during a whole systole and diastole cycle or the study of the elastogram of the carotid artery.Universidad Politécnica de Cartagen

Combinatorial optimisation for arterial image segmentation.

Cardiovascular disease is one of the leading causes of the mortality in the western world. Many imaging modalities have been used to diagnose cardiovascular diseases. However, each has different forms of noise and artifacts that make the medical image analysis field important and challenging. This thesis is concerned with developing fully automatic segmentation methods for cross-sectional coronary arterial imaging in particular, intra-vascular ultrasound and optical coherence tomography, by incorporating prior and tracking information without any user intervention, to effectively overcome various image artifacts and occlusions. Combinatorial optimisation methods are proposed to solve the segmentation problem in polynomial time. A node-weighted directed graph is constructed so that the vessel border delineation is considered as computing a minimum closed set. A set of complementary edge and texture features is extracted. Single and double interface segmentation methods are introduced. Novel optimisation of the boundary energy function is proposed based on a supervised classification method. Shape prior model is incorporated into the segmentation framework based on global and local information through the energy function design and graph construction. A combination of cross-sectional segmentation and longitudinal tracking is proposed using the Kalman filter and the hidden Markov model. The border is parameterised using the radial basis functions. The Kalman filter is used to adapt the inter-frame constraints between every two consecutive frames to obtain coherent temporal segmentation. An HMM-based border tracking method is also proposed in which the emission probability is derived from both the classification-based cost function and the shape prior model. The optimal sequence of the hidden states is computed using the Viterbi algorithm. Both qualitative and quantitative results on thousands of images show superior performance of the proposed methods compared to a number of state-of-the-art segmentation methods