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246 results on '"Blanco, Pablo J."'

3. Noise-specific denoising method with applications to high-frequency ultrasonic images

Author

Talou, Gonzalo D. Maso and Blanco, Pablo J.

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Denoising is of utmost importance for the visualization and processing of images featuring low signal-to-noise ratio. Total variation methods are among the most popular techniques to perform this task improving the signal-to-noise ratio while preserving coherent intensity discontinuities. In this work, a novel method, termed maximum likelihood data, is proposed, endowing the total variation formulation with the capability to deal with noise-specific models and pre-processing stages for a certain image of interest. To do this, the data fidelity term is modified by means of a maximum likelihood estimator between the original and the denoised image. To assess the improvements of the proposed method with respect to the total variation formulation, we study the denoising of high-frequency ultrasonic images on in-silico and in-vivo setups. The proposed method delivered a better contrast, preservation and localization of the structures while diminishing the intensity bias of the total variation formulation for the multiplicative noise. The enhancement of medical images through denoising helps to improve the outcome of subsequently applied image processing such as registration and segmentation procedures.

Published
2022

4. Comparison of endothelial shear stress between ultrathin strut bioresorbable polymer drug-eluting stent vs durable-polymer drug-eluting stent post-stent implantation: An optical coherence tomography substudy from BIOFLOW II

Author

Beyene, Solomon, Tufaro, Vincenzo, Garg, Mohil, Gkargkoulas, Fotis, Calderon, Andrea Teira, Safi, Hannah, Waksman, Ron, Windecker, Stephan, Torii, Ryo, Melaku, Gebremedhin D., Bulant, Carlos A., Bourantas, Christos V., Blanco, Pablo J., and Garcia-Garcia, Hector M.

Published
2024
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7. Human vs. machine vs. core lab for the assessment of coronary atherosclerosis with lumen and vessel contour segmentation with intravascular ultrasound

Author

Bass, Ronald D., Garcia-Garcia, Hector M., Sanz-Sánchez, Jorge, Ziemer, Paulo G. P., Bulant, Carlos A., Kuku, Kayode K., Kahsay, Yirga A., Beyene, Solomon, Melaku, Gebremedhin, Otsuka, Tatsuhiko, Choi, JooHee, Fernández-Peregrina, Estefanía, Erdogan, Emrah, Gonzalo, Nieves, Bourantas, Christos V., Blanco, Pablo J., and Räber, Lorenz

Published
2022
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8. Towards a glaucoma risk index based on simulated hemodynamics from fundus images

Author

Orlando, José Ignacio, Breda, João Barbosa, van Keer, Karel, Blaschko, Matthew B., Blanco, Pablo J., and Bulant, Carlos A.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Glaucoma is the leading cause of irreversible but preventable blindness in the world. Its major treatable risk factor is the intra-ocular pressure, although other biomarkers are being explored to improve the understanding of the pathophysiology of the disease. It has been recently observed that glaucoma induces changes in the ocular hemodynamics. However, its effects on the functional behavior of the retinal arterioles have not been studied yet. In this paper we propose a first approach for characterizing those changes using computational hemodynamics. The retinal blood flow is simulated using a 0D model for a steady, incompressible non Newtonian fluid in rigid domains. The simulation is performed on patient-specific arterial trees extracted from fundus images. We also propose a novel feature representation technique to comprise the outcomes of the simulation stage into a fixed length feature vector that can be used for classification studies. Our experiments on a new database of fundus images show that our approach is able to capture representative changes in the hemodynamics of glaucomatous patients. Code and data are publicly available in https://ignaciorlando.github.io., Comment: MICCAI 2018 paper

Published
2018

13. Comparison of endothelial shear stress between ultrathin strut Bioresorbable Polymer Drug Eluting Stent vs Durable Polymer Drug Eluting Stent post-stent implantation: An optical coherence tomography substudy from BIOFLOW II

Author

Beyene, Solomon, primary, Tufaro, Vincenzo, additional, Garg, Mohil, additional, Gkargkoulas, Fotis, additional, Calderon, Andrea Teira, additional, Safi, Hannah, additional, Waksman, Ron, additional, Windecker, Stephan, additional, Torii, Ryo, additional, Melaku, Gebremedhin O., additional, Bulant, Carlos A., additional, Bourantas, Christos V., additional, Blanco, Pablo J., additional, and Garcia-Garcia, Hector M., additional

Published
2023
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21. Quantitative coronary three‐dimensional geometry and its association with atherosclerotic disease burden and composition

Author

A. Prado, Guy F., primary, Blanco, Pablo J., additional, Bulant, Carlos A., additional, Ares, Gonzalo D., additional, Mariani, Jose, additional, Caixeta, Adriano, additional, Almeida, Breno O., additional, Garzon, Stefano, additional, Pinton, Fábio A., additional, Barbato, Emanuele, additional, Ribichini, Flavio L., additional, Toth, Gabor G., additional, Mahfoud, Felix, additional, Wijns, William, additional, Garcia‐Garcia, Hector M., additional, and Lemos, Pedro A., additional

Published
2023
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23. Pleiotropic antifibrotic actions of aspirin-triggered resolvin D1 in the lungs

Author

Guilherme, Rafael F., primary, Silva, José Bruno N.F., additional, Waclawiack, Ingrid, additional, Fraga-Junior, Vanderlei S., additional, Nogueira, Thaís O., additional, Pecli, Cyntia, additional, Araújo-Silva, Carlla A., additional, Magalhães, Nathalia S., additional, Lemos, Felipe S., additional, Bulant, Carlos A., additional, Blanco, Pablo J., additional, Serra, Rafaela, additional, Svensjö, Erik, additional, Scharfstein, Júlio, additional, Moraes, João A., additional, Canetti, Claudio, additional, and Benjamim, Claudia F., additional

Published
2023
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24. Effect of high-intensity statin therapy on atherosclerosis (IBIS-4):Manual versus automated methods of IVUS analysis

Author

Bass, Ronald D., García-García, Héctor M., Ueki, Yasushi, Holmvang, Lene, Pedrazzini, Giovanni, Roffi, Marco, Koskinas, Konstantinos C., Shibutani, Hiroki, Losdat, Sylvain, Ziemer, Paulo G.P., Blanco, Pablo J., Levine, Molly B., Bourantas, Christos V., Räber, Lorenz, Bass, Ronald D., García-García, Héctor M., Ueki, Yasushi, Holmvang, Lene, Pedrazzini, Giovanni, Roffi, Marco, Koskinas, Konstantinos C., Shibutani, Hiroki, Losdat, Sylvain, Ziemer, Paulo G.P., Blanco, Pablo J., Levine, Molly B., Bourantas, Christos V., and Räber, Lorenz

Abstract

Aims Standard manual analysis of IVUS to study the impact of anti-atherosclerotic therapies on the coronary vessel wall is done by a core laboratory (CL), the ground truth (GT). Automatic segmentation of IVUS with a machine learning (ML) algorithm has the potential to replace manual readings with an unbiased and reproducible method. The aim is to determine if results from a CL can be replicated with ML methods. Methods This is a post-hoc, comparative analysis of the IBIS-4 (Integrated Biomarkers and Imaging Study-4) study (NCT00962416). The GT baseline and 13-month follow-up measurements of lumen and vessel area and percent atheroma volume (PAV) after statin induction were repeated by the ML algorithm. Results The primary endpoint was change in PAV. PAV as measured by GT was 43.95 % at baseline and 43.02 % at follow-up with a change of −0.90 % (p = 0.007) while the ML algorithm measured 43.69 % and 42.41 % for baseline and follow-up, respectively, with a change of −1.28 % (p < 0.001). Along the most diseased 10 mm segments, GT-PAV was 52.31 % at baseline and 49.42 % at follow-up, with a change of −2.94 % (p < 0.001). The same segments measured by the ML algorithm resulted in PAV of 51.55 % at baseline and 47.81 % at follow-up with a change of −3.74 % (p < 0.001). Conclusions PAV, the most used endpoint in clinical trials, analyzed by the CL is closely replicated by the ML algorithm. ML automatic segmentation of lumen, vessel and plaque effectively reproduces GT and may be used in future clinical trials as the standard. Abbreviations CADcoronary artery diseaseCLcore laboratoryCNNconvolutional neural networkGTground truthIBIS-4integrated biomarker imaging studyIRAinfarct-related arteriesIVUSintravascular ultrasoundMFCNNmulti-frame convolutional neural networkMLmachine learningPAVpercent atheroma volumePCIpercutaneous coronary interventionROIregion of interest, Aims: Standard manual analysis of IVUS to study the impact of anti-atherosclerotic therapies on the coronary vessel wall is done by a core laboratory (CL), the ground truth (GT). Automatic segmentation of IVUS with a machine learning (ML) algorithm has the potential to replace manual readings with an unbiased and reproducible method. The aim is to determine if results from a CL can be replicated with ML methods. Methods: This is a post-hoc, comparative analysis of the IBIS-4 (Integrated Biomarkers and Imaging Study-4) study (NCT00962416). The GT baseline and 13-month follow-up measurements of lumen and vessel area and percent atheroma volume (PAV) after statin induction were repeated by the ML algorithm. Results: The primary endpoint was change in PAV. PAV as measured by GT was 43.95 % at baseline and 43.02 % at follow-up with a change of −0.90 % (p = 0.007) while the ML algorithm measured 43.69 % and 42.41 % for baseline and follow-up, respectively, with a change of −1.28 % (p < 0.001). Along the most diseased 10 mm segments, GT-PAV was 52.31 % at baseline and 49.42 % at follow-up, with a change of −2.94 % (p < 0.001). The same segments measured by the ML algorithm resulted in PAV of 51.55 % at baseline and 47.81 % at follow-up with a change of −3.74 % (p < 0.001). Conclusions: PAV, the most used endpoint in clinical trials, analyzed by the CL is closely replicated by the ML algorithm. ML automatic segmentation of lumen, vessel and plaque effectively reproduces GT and may be used in future clinical trials as the standard.

Published
2023

31. An Anatomically Detailed Arterial-Venous Network Model. Cerebral and Coronary Circulation

Author

Muller, Lucas O., Watanabe, Sansuke M., Toro, Eleuterio F., Feijóo, Raúl A., and Blanco, Pablo J.

Subjects
haemodynamics, brain circulation, coronary circulation, wave propagation, arterial-venous system, haemodynamics, wave propagation, arterial-venous system, cardiovascular diseases, brain circulation, coronary circulation, cardiovascular diseases
Published
2023

36. Correction to: Towards a Glaucoma Risk Index Based on Simulated Hemodynamics from Fundus Images

Author

Orlando, José Ignacio, Barbosa Breda, João, van Keer, Karel, Blaschko, Matthew B., Blanco, Pablo J., Bulant, Carlos A., Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Frangi, Alejandro F., editor, Schnabel, Julia A., editor, Davatzikos, Christos, editor, Alberola-López, Carlos, editor, and Fichtinger, Gabor, editor

Published
2018
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40. Human vs. Machine vs. Core Lab in Lumen and Vessel Contour Segmentation With Intravascular Ultrasound

Author

Bass, Ronald, primary, Garcia-Garcia, Hector M., additional, Sanz-Sanchez, Jorge, additional, Ziemer, Paulo G.P., additional, Bulant, Carlos A., additional, Kuku, Kayode O., additional, Kahsay, Yirga A., additional, Beyene, Solomon, additional, Melaku, Gebremedhin, additional, Otsuka, Tatsuhiko, additional, Choi, JooHee, additional, Fernandez-Peregrina, Estefania, additional, Erdogan, Emrah, additional, Gonzalo, Nieves, additional, Bourantas, Christos V., additional, Blanco, Pablo J., additional, and Raber, Lorenz, additional

Published
2022
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42. Construction of hybrid 1D‐0D networks for efficient and accurate blood flow simulations.

Author

Ghitti, Beatrice, Blanco, Pablo J., Toro, Eleuterio F., and Müller, Lucas O.

Subjects
FLOW simulations, PHYSIOLOGY, HUMAN anatomical models, BLOOD flow, PULSATILE flow, PHYSIOLOGICAL models
Abstract

The one‐dimensional (1D) modeling of blood flow in complex networks of vessels and cardiovascular models can result in computationally expensive simulations. The complexity of such networks has significantly increased in the last years, in terms of both enhanced anatomical detail and modeling of physiological mechanisms and mechanical characteristics. To address such issue, the main goal of this work is to present a novel methodology to construct hybrid networks of coupled 1D and 0D vessels and to perform computationally efficient and accurate blood flow simulations in such networks. Departing from both the 1D and lumped‐parameter (0D) nonlinear models for blood flow, we propose high‐order numerical coupling strategies to solve the 1D, 0D, and hybrid coupling of vessels at junctions. To effectively construct hybrid networks, we explore different a‐priori model selection criteria focusing in obtaining the best possible trade‐off between computational cost of the simulations and accuracy of the computed solutions for the hybrid network with respect to the 1D network. The achievement of the expected order of accuracy is verified in several test cases. The novel methodology is applied to two different arterial networks, the 37‐artery network and the reduced ADAN56 model, where, in order to identify the best performing a‐priori model selection criteria, the quantitative assessment of CPU times and errors and the qualitative comparison between results are carried out and discussed. [ABSTRACT FROM AUTHOR]

Published
2023
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45. CRT-400.04 Human vs. Machine vs. Core Lab in Lumen and Vessel Contour Segmentation With Intravascular Ultrasound

Author

Bass, Ronald, primary, Garcia-Garcia, Hector M., additional, Sanz-Sanchez, Jorge, additional, Ziemer, Paulo GP., additional, Bulant, Carlos A., additional, Kuku, Kayode O., additional, Kahsay, Yirga A., additional, Beyene, Solomon, additional, Melaku, Gebremedhin, additional, Otsuka, Tatsuhiko, additional, Choi, JooHee, additional, Fernandez-Peregrina, Estefania, additional, Erdogan, Emrah, additional, Gonzalo, Nieves, additional, Bourantas, Christos V., additional, Blanco, Pablo J., additional, and Raber, Lorenz, additional

Published
2022
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48. Fully automated lumen and vessel contour segmentation in intravascular ultrasound datasets

Author

Blanco, Pablo J, Ziemer, Paulo G P, Bulant, Carlos A, Ueki, Yasushi, Bass, Ronald, R��ber, Lorenz, Lemos, Pedro A, and Garc��a-Garc��a, H��ctor M

Subjects
610 Medicine & health
Abstract

Segmentation of lumen and vessel contours in intravascular ultrasound (IVUS) pullbacks is an arduous and time-consuming task, which demands adequately trained human resources. In the present study, we propose a machine learning approach to automatically extract lumen and vessel boundaries from IVUS datasets. The proposed approach relies on the concatenation of a deep neural network to deliver a preliminary segmentation, followed by a Gaussian process (GP) regressor to construct the final lumen and vessel contours. A multi-frame convolutional neural network (MFCNN) exploits adjacency information present in longitudinally neighboring IVUS frames, while the GP regression method filters high-dimensional noise, delivering a consistent representation of the contours. Overall, 160 IVUS pullbacks (63 patients) from the IBIS-4 study (Integrated Biomarkers and Imaging Study-4, Trial NCT00962416), were used in the present work. The MFCNN algorithm was trained with 100 IVUS pullbacks (8427 manually segmented frames), was validated with 30 IVUS pullbacks (2583 manually segmented frames) and was blindly tested with 30 IVUS pullbacks (2425 manually segmented frames). Image and contour metrics were used to characterize model performance by comparing ground truth (GT) and machine learning (ML) contours. Median values (interquartile range, IQR) of the Jaccard index for lumen and vessel were 0.913, [0.882,0.935] and 0.940, [0.917,0.957], respectively. Median values (IQR) of the Hausdorff distance for lumen and vessel were 0.196mm, [0.146,0.275]mm and 0.163mm, [0.122,0.234]mm, respectively. Also, the mean value of lumen area predictions, and limits of agreement were -0.19mm2, [1.1,-1.5]mm2, while the mean value and limits of agreement of plaque burden were 0.0022, [0.082,-0.078]. The results obtained with the model developed in this work allow us to conclude that the proposed machine learning approach delivers accurate segmentations in terms of image metrics, contour metrics and clinically relevant variables, enabling its use in clinical routine by mitigating the costs involved in the manual management of IVUS datasets.

Published
2022
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