MARCO ANTONIO GUTIERREZ
Projetos de Pesquisa
Unidades Organizacionais
Instituto do Coração, Hospital das Clínicas, Faculdade de Medicina
LIM/65, Hospital das Clínicas, Faculdade de Medicina - Líder
LIM/65, Hospital das Clínicas, Faculdade de Medicina - Líder
7 resultados
Resultados de Busca
Agora exibindo 1 - 7 de 7
conferenceObject Spatial-Frequency Approach to Fibrous Tissue Classification in Intracoronary Optical Images(2016) MACEDO, Maysa M. G.; NICZ, Pedro F. G.; CAMPOS, Carlos M.; LEMOS, Pedro A.; GUTIERREZ, Marco A.Increased understanding about the mechanisms of coronary thrombosis in humans has been limited by the lack of imaging modalities with resolution sufficient to characterize fibrous cap tissue and determine its thickness in vivo. Intravascular optical coherence tomography (IOCT) provides images with micrometer axial (10-15 mu m) and lateral resolution (40 mu m), enabling detailed visualization of micro-structural changes of the arterial wall. This article describes a fully automated method for identification and quantification of fibrous tissue in IOCT human coronary images based on spatial-frequency analysis by means Short-Time Fourier transform. Forty IOCT frames from nine IOCT in-vivo datasets were annotated by an expert and used to evaluate the proposed fibrous tissue characterization method.conferenceObject Directional Analysis of Cardiac Motion Field based on the Discrete Helmholtz Hodge Decomposition(2016) SIMS, John A.; MACEDO, Maysa M. G.; GUTIERREZ, Marco A.The analysis of LV rotational motion could provide insights into myocardial dysfunction and predict the outcome of interventions, and this analysis could be performed more simply in separate rotational and radial components. In this study we present an automatic method for decomposing the cardiac motion field into radial and rotational components using the Discrete Helmholtz Hodge Decomposition (DHHD). The DHHD was applied to the following 3D motion fields (i) Synthetic complex motion fields, created by applying curl and gradient operators to Gaussian potentials, to determine numerical accuracy; (ii) Synthetic motion field from the 4D Extended Cardiac-Torso (XCAT) phantom (v2.0), to validate the use of the DHHD in decomposing cardiac motion fields. Decomposition error was found to decrease with increasing smoothness of the fields, while keeping motion field components small at the boundary of the motion field domain. The DHHD was seen to separate radial and rotational cardiac motion, allowing possible simplification of motion analysis.- Classification of bifurcations regions in IVOCT images using support vector machine and artificial neural network models(2017) PORTO, C. D. N.; COSTA FILHO, C. F. F.; MACEDO, M. M. G.; GUTIERREZ, M. A.; COSTA, M. G. F.Studies in intravascular optical coherence tomography (IV-OCT) have demonstrated the importance of coronary bifurcation regions in intravascular medical imaging analysis, as plaques are more likely to accumulate in this region leading to coronary disease. A typical IV-OCT pullback acquires hundreds of frames, thus developing an automated tool to classify the OCT frames as bifurcation or non-bifurcation can be an important step to speed up OCT pullbacks analysis and assist automated methods for atherosclerotic plaque quantification. In this work, we evaluate the performance of two state-of-the-art classifiers, SVM and Neural Networks in the bifurcation classification task. The study included IV-OCT frames from 9 patients. In order to improve classification performance, we trained and tested the SVM with different parameters by means of a grid search and different stop criteria were applied to the Neural Network classifier: mean square error, early stop and regularization. Different sets of features were tested, using feature selection techniques: PCA, LDA and scalar feature selection with correlation. Training and test were performed in sets with a maximum of 1460 OCT frames. We quantified our results in terms of false positive rate, true positive rate, accuracy, specificity, precision, false alarm, f-measure and area under ROC curve. Neural networks obtained the best classification accuracy, 98.83%, overcoming the results found in literature. Our methods appear to offer a robust and reliable automated classification of OCT frames that might assist physicians indicating potential frames to analyze. Methods for improving neural networks generalization have increased the classification performance.
- An automatic labeling bifurcation method for Intracoronary Optical Coherence Tomography images(2015) MACEDO, Maysa M. G.; TAKIMURA, Celso K.; LEMOS, Pedro A.; GUTIERREZ, Marco A.Vessel branchings are critical vascular locations from the clinical point of view. In these sites, the arterial hemodynamic plays a relevant role in the progression of atherosclerosis, an important vascular pathology. In this paper, a fully automatic approach for the bifurcation classification in human Intravascular Optical Coherence Tomography (IV-OCT) sequences is introduced. Given the lumen contours, the method is capable of labeling the bifurcation slices. A geometric feature extraction was performed and the Forward Regression Orthogonal Least Squares method (FROLS) was applied to analyze the best features and to determine the appropriated weights in a binary classifier. A cross-validation scheme is applied in order to evaluate the performance of the classification approach and the results have shown a sensitivity of 86% and specificity of 92% to FROLS.
- A robust fully automatic lumen segmentation method for in vivo intracoronary optical coherence tomography(2016) MACEDO, Maysa Malfiza Garcia de; TAKIMURA, Celso Kiyoshi; LEMOS, Pedro Alves; GUTIERREZ, Marco AntonioAbstract Introduction: Intravascular optical coherence tomography (IVOCT) is an in-vivo imaging modality based on the introduction of a catheter in a blood vessel for viewing its inner wall using electromagnetic radiation. One of the most developed automatic applications for this modality is the lumen area segmentation, however on the evaluation of these methods, the slices inside bifurcation regions, or with the presence of complex atherosclerotic plaques and dissections are usually discarded. This paper describes a fully-automatic method for computing the lumen area in IVOCT images where the set of slices includes complex atherosclerotic plaques and dissections. Methods The proposed lumen segmentation method is divided into two steps: preprocessing, including the removal of artifacts and the second step comprises a lumen detection using morphological operations. In addition, it is proposed an approach to delimit the lumen area for slices inside bifurcation region, considering only the main branch. Results Evaluation of the automatic lumen segmentation used manual segmentations as a reference, it was performed on 1328 human IVOCT images, presenting a mean difference in lumen area and Dice metrics of 0.19 mm2 and 97% for slices outside the bifurcation, 1.2 mm2 and 88% in the regions with bifurcation without automatic contour correction and 0.52 mm2 and 90% inside bifurcation region with automatic contour correction. Conclusion This present study shows a robust lumen segmentation method for vessel cross-sections with dissections and complex plaque and bifurcation avoiding the exclusion of such regions from the dataset analysis.
- A bifurcation identifier for IV-OCT using orthogonal least squares and supervised machine learning(2015) MACEDO, Maysa M. G.; GUIMARAES, Welingson V. N.; GALON, Micheli Z.; TAKIMURA, Celso K.; LEMOS, Pedro A.; GUTIERREZ, Marco AntonioIntravascular optical coherence tomography (IV-OCT) is an in-vivo imaging modality based on the intravascular introduction of a catheter which provides a view of the inner wall of blood vessels with a spatial resolution of 10-20 mu m. Recent studies in IV-OCT have demonstrated the importance of the bifurcation regions. Therefore, the development of an automated tool to classify hundreds of coronary OCT frames as bifurcation or nonbifurcation can be an important step to improve automated methods for atherosclerotic plaques quantification, stent analysis and co-registration between different modalities. This paper describes a fully automated method to identify IV-OCT frames in bifurcation regions. The method is divided into lumen detection; feature extraction; and classification, providing a lumen area quantification, geometrical features of the cross-sectional lumen and labeled slices. This classification method is a combination of supervised machine learning algorithms and feature selection using orthogonal least squares methods. Training and tests were performed in sets with a maximum of 1460 human coronary OCT frames. The lumen segmentation achieved a mean difference of lumen area of 0.11 mm(2) compared with manual segmentation, and the AdaBoost classifier presented the best result reaching a F-measure score of 97.5% using 104 features.
- CAD of cardiovascular diseases(2017) GUTIERREZ, M. A.; REBELO, M. S.; MORENO, R. A.; MACEDO, M. M. G.Noninvasive cardiac imaging is an invaluable tool for the diagnosis and treatment of cardiovascular disease (CVD). Computed Tomography (CT), Single Photon Emission (SPECT), Positron Emission Tomography (PET), Ultrasound (US), Magnetic Resonance Imaging (MRI), Intravascular Ultrasound (IVUS), and Intravascular Optical Coherence Tomography (IVOCT) have been used extensively for physiologic understanding and diagnostic purposes in cardiology. These imaging technologies have greatly increased our understanding of normal and diseased anatomy. Cardiac image segmentation plays a crucial role and allows for a wide range of applications, including quantification of volume, computer-aided diagnosis, localization of pathology, and image-guided interventions. However, manual delineation is tedious, time consuming, and is limited by inter- and intra-observer variability. In addition, many segmentation algorithms are sensitive to the initialization, and, therefore, the results are 146not always reproducible as they are also limited by inter algorithm variability. Furthermore, the amount and quality of imaging data that needs to be routinely acquired in one or more subjects have increased significantly. Therefore, it is crucial to develop automated, precise, and reproducible segmentation methods. A variety of segmentation techniques has been proposed over the last few decades. While earlier approaches were often based on heuristics, recent studies employ techniques that are more sophisticated. However, cardiac image segmentation remained a challenge due to the highly variable nature of cardiac anatomy, function, and pathology. Furthermore, diseases, imaging protocols, -artifacts, and noise heavily influence intensity distributions on images. © 2018 by Taylor & Francis Group, LLC.