Rough Set Based Approach for IMT Automatic Estimation

Carotid artery (CA) intima-media thickness (IMT) is commonly deemed as one of the risk marker for cardiovascular diseases. The automatic estimation of the IMT on ultrasound images is based on the correct identification of the lumen-intima (LI) and media-adventitia (MA) interfaces. This task is complicated by noise, vessel morphology and pathology of the carotid artery. In a previous study we applied four non-linear methods for feature selection on a set of variables extracted from ultrasound carotid images. The main aim was to select those parameters containing the highest amount of information useful to classify the image pixels in the carotid regions they belong to. In this study we present a pixel classifier based on the selected features. Once the pixels classification was correctly performed, the IMT was evaluated and compared with two sets of manual-traced profiles. The results showed that the automatic IMTs are not statistically different from the manual one

Ultrasound IMT measurement on a multi-ethnic and multi-institutional database: Our review and experience using four fully automated and one semi-automated methods

Automated and high performance carotid intima-media thickness (IMT) measurement is gaining increasing importance in clinical practice to assess the cardiovascular risk of patients. In this paper, we compare four fully automated IMT measurement techniques (CALEX, CAMES, CARES and CAUDLES) and one semi-automated technique (FOAM). We present our experience using these algorithms, whose lumen-intima and media-adventitia border estimation use different methods that can be: (a) edge-based; (b) training-based; (c) feature-based; or (d) directional Edge-Flow based. Our database (DB) consisted of 665 images that represented a multi-ethnic group and was acquired using four OEM scanners. The performance evaluation protocol adopted error measures, reproducibility measures, and Figure of Merit (FoM). FOAM showed the best performance, with an IMT bias equal to 0.025 ± 0.225 mm, and a FoM equal to 96.6%. Among the four automated methods, CARES showed the best results with a bias of 0.032 ± 0.279 mm, and a FoM to 95.6%, which was statistically comparable to that of FOAM performance in terms of accuracy and reproducibility. This is the first time that completely automated and user-driven techniques have been compared on a multi-ethnic dataset, acquired using multiple original equipment manufacturer (OEM) machines with different gain settings, representing normal and pathologic case

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

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

Attention-Based UNet Deep Learning Model for Plaque Segmentation in Carotid Ultrasound for Stroke Risk Stratification: An Artificial Intelligence Paradigm

Stroke and cardiovascular diseases (CVD) significantly affect the world population. The early detection of such events may prevent the burden of death and costly surgery. Conventional methods are neither automated nor clinically accurate. Artificial Intelligence-based methods of automatically detecting and predicting the severity of CVD and stroke in their early stages are of prime importance. This study proposes an attention-channel-based UNet deep learning (DL) model that identifies the carotid plaques in the internal carotid artery (ICA) and common carotid artery (CCA) images. Our experiments consist of 970 ICA images from the UK, 379 CCA images from diabetic Japanese patients, and 300 CCA images from post-menopausal women from Hong Kong. We combined both CCA images to form an integrated database of 679 images. A rotation transformation technique was applied to 679 CCA images, doubling the database for the experiments. The cross-validation K5 (80% training: 20% testing) protocol was applied for accuracy determination. The results of the Attention-UNet model are benchmarked against UNet, UNet++, and UNet3P models. Visual plaque segmentation showed improvement in the Attention-UNet results compared to the other three models. The correlation coefficient (CC) value for Attention-UNet is 0.96, compared to 0.93, 0.96, and 0.92 for UNet, UNet++, and UNet3P models. Similarly, the AUC value for Attention-UNet is 0.97, compared to 0.964, 0.966, and 0.965 for other models. Conclusively, the Attention-UNet model is beneficial in segmenting very bright and fuzzy plaque images that are hard to diagnose using other methods. Further, we present a multi-ethnic, multi-center, racial bias-free study of stroke risk assessment

Processing in vivo ultrasound images of the carotid artery

Carotid stenosis is a narrowing of the carotid arteries, the two major arteries that carry oxygen-rich blood from the heart to the brain. This disease is caused by a buildup of plaque (atherosclerosis) inside the artery wall that reduces blood flow to the brain. This thesis focuses on predicting whether the plaque in the carotid artery is unstable (symptomatic) or stable (asymptomatic) using ultrasound images of the carotid artery. If it is unstable it means that the plaque is going to grow, otherwise, is going to remain the same. Using a provided segmentation, a series of descriptors and a subsequent classification model has been developed to fulfil this task. We will see that between the linear regression classifier, SVC or Random Forest, SVC will give the best results. For the cross-sectional images, the descriptors that will give us the best accuracy in distinguishing the two classes will be: relative percentage stenosis, relative plaque area, wavelets and Haralick texture descriptors. The first two will be calculated on the cross-sectional segmentations and the last ones on the original cross-sectional images using segmentations as well. With this selection of features we will achieve 67% accuracy in the classification of our data.La estenosis carotídea es un estrechamiento de las arterias carótidas, las dos arterias principales que llevan la sangre rica en oxígeno del corazón al cerebro. Esta enfermedad está causada por una acumulación de placa (aterosclerosis) en el interior de la pared arterial que reduce el flujo sanguíneo al cerebro. La presente tesis se centra en predecir si la placa en la arteria carótida es inestable (sintomática) o estable (asintomática) utilizando las imágenes ecográficas de la arteria carótida. Si es inestable significa que la placa va a crecer, por otra parte, si es estable, se mantendrá igual. Mediante una segmentación que nos ha sido facilitada, se han desarrollado una serie de descriptores y un posterior modelo de clasificación para cumplir este cometido. Veremos que entre el clasificador de regresión lineal, SVC o Random Forest, SVC será el que nos dará mejores resultados. Para las imágenes transversales, los descriptores que nos darán una mayor precisión al distinguir las dos clases serán: porcentaje de estenosis relativa, área relativa de la placa, wavelets y los descriptores de textura de Haralick. Las dos primeras se calcularán sobre les segmentaciones transversales y las últimas sobre las imágenes transversales originales utilizando también las segmentaciones. Con esta selección de características se conseguirá un 67% de precisión en la clasificación de nuestros datos.L'estenosi carotídia és un estrenyiment de les artèries caròtides, les dues artèries principals que porten la sang rica en oxigen del cor al cervell. Aquesta malaltia està causada per una acumulació de placa (aterosclerosi) a l'interior de la paret arterial que redueix el flux sanguini al cervell. La tesis que es presenta es centra en predir si la placa en l'arteria caròtida és inestable (simptomàtica) o estable (asimptomàtica) utilitzant les imatges ecogràfiques de l'arteria caròtida. Si és inestable significa que la placa creixerà, d'altra banda, si és estable, romandrà igual. Mitjançant una segmentació que se'ns ha facilitat, s'han desenvolupat una sèrie de descriptors i un posterior model de classificació per complir aquesta comesa. Veurem que entre el classificador de regressió lineal, SVC o Random Forest, SVC serà amb el que obtindrem millors resultats. Per les imatges transversals, els descriptors que ens donaran una major precisió al distingir les dos classes seran: percentatge d'estenosis relativa, àrea relativa de la placa, wavelets i els descriptors de textura de Haralick. Les dues primeres es calcularan sobre les segmentacions transversals i les últimes sobre les imatges transversals originals utilitzant també les segmentacions. Amb aquesta selecció de característiques s'aconseguirà un 67% de precisió en la classificació de les nostres dades

Hypothesis Validation of Far-Wall Brightness in Carotid-Artery Ultrasound for Feature-Based IMT Measurement Using a Combination of Level-Set Segmentation and Registration

Intima-media thickness (IMT) is now being considered as an indicator of atherosclerosis. Our group has developed several feature-based IMT measurement algorithms such as the Completely Automated Layer EXtraction (CALEX) (which is a class of patented AtheroEdge Systems from Global Biomedical Technologies, Inc., CA, USA). These methods are based on the hypothesis that the highest pixel intensities are in the far wall of the common carotid artery (CCA) or the internal carotid artery (ICA). In this paper, we verify that this hypothesis holds true for B-mode longitudinal ultrasound (US) images of the carotid wall. This patented methodology consists of generating the composite image (the arithmetic sum of images) from the database by first registering the carotid image frames with respect to a nearly straight carotid-artery frame from the same database using: 1) B-spline-based nonrigid registration and 2) affine registration. Prior to registration, we segment the carotid-artery lumen using a level-set-based algorithm followed by morphological image processing. The binary lumen images are registered, and the transformations are applied to the original grayscale CCA images. We evaluated our technique using a database of 200 common carotid images of normal and pathologic carotids. The composite image presented the highest intensity distribution in the far wall of the CCA/ICA, validating our hypothesis. We have also demonstrated the accuracy and improvement in the IMT segmentation result with our CALEX 3.0 system. The CALEX system, when run on newly acquired US images, shows the IMT error of about 30 mu m. Thus, we have shown that the CALEX algorithm is able to exploit the far-wall brightness for accurate IMT measurements