RAMON ALFREDO MORENO

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Instituto do Coração, Hospital das Clínicas, Faculdade de Medicina
LIM/65, Hospital das Clínicas, Faculdade de Medicina

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  • article 4 Citação(ões) na Scopus
    Combining wavelets transform and Hu moments with self-organizing maps for medical image categorization
    (2011) SILVA, Leandro A.; DEL-MORAL-HERNANDEZ, Emilio; MORENO, Ramon A.; FURUIE, Sergio S.
    Images are fundamental sources of information in modern medicine. The images stored in a database and divided in categories are an important step for image retrieval. For an automatic categorization process, detailed analysis is done regarding image representation and generalization method. The baseline method for this process, in the medical image context, is using thumbnails and K-nearest neighbor (KNN), which is easily implemented and has had satisfactory results in literature. This work addresses an alter-native method for automatic categorization, which jointly uses discrete wavelet transform with Hu's moments for image representation and self-organizing maps (SOM) neural networks combined with the KNN classifier (SOM-KNN), for medical image categorization. Furthermore, extensive experiments are conducted, to define the best wavelet family and to select the best coefficients set, to consider the remaining wavelet coefficients set (not selected as the best ones) through their Hu's moments, and to carry out a contrastive study with other successful approaches for categorization. The categorization result from a database with 10,000 images in 116 categories yielded 81.8% of correct rate, which is much better than the 67.9% obtained by the baseline method; and the time consumed in classification processing with SOM-KNN is 100 times shorter than KNN. (C) 2011 SPIE and IS&T. [DOI: 10.1117/1.3645598]
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    Fully Automated Quantification of Cardiac Indices from Cine MRI Using a Combination of Convolution Neural Networks
    (2020) PEREIRA, Renato F.; REBELO, Marina S.; MORENO, Ramon A.; MARCO, Anderson G.; LIMA, Daniel M.; ARRUDA, Marcelo A. F.; KRIEGER, Jose E.; GUTIERREZ, Marco A.
    Cardiovascular magnetic resonance imaging (CMRI) is one of the most accurate non-invasive modalities for evaluation of cardiac function, especially the left ventricle (LV). In this modality, the manual or semi-automatic delineation of LV by experts is currently the standard clinical practice for chambers segmentation. Despite these efforts, global quantification of LV remains a challenge. In this work, a combination of two convolutional neural network (CNN) architectures for quantitative evaluation of the LV is described, which estimates the cavity and the myocardium areas, endocardial cavity dimensions in three directions, and the myocardium regional wall thickness in six radial directions. The method was validated in CMRI exams of 56 patients (LVQuan19 dataset) and evaluated by metrics Dice Index, Mean Absolute Error, and Correlation with superior performance compared to the state-of-the-art methods. The combination of the CNN architectures provided a simpler yet fully automated approach, requiring no specialist interaction.
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    Description of patellar movement by 3D parameters obtained from dynamic CT acquisition
    (2014) REBELO, Marina de Sa; MORENO, Ramon Alfredo; GOBBI, Riccardo Gomes; CAMANHO, Gilberto Luis; AVILA, Luiz Francisco Rodrigues de; DEMANGE, Marco Kawamura; PECORA, Jose Ricardo; GUTIERREZ, Marco Antonio
    The patellofemoral joint is critical in the biomechanics of the knee. The patellofemoral instability is one condition that generates pain, functional impairment and often requires surgery as part of orthopedic treatment. The analysis of the patellofemoral dynamics has been performed by several medical image modalities. The clinical parameters assessed are mainly based on 2D measurements, such as the patellar tilt angle and the lateral shift among others. Besides, the acquisition protocols are mostly performed with the leg laid static at fixed angles. The use of helical multi slice CT scanner can allow the capture and display of the joint's movement performed actively by the patient. However, the orthopedic applications of this scanner have not yet been standardized or widespread. In this work we present a method to evaluate the biomechanics of the patellofemoral joint during active contraction using multi slice CT images. This approach can greatly improve the analysis of patellar instability by displaying the physiology during muscle contraction. The movement was evaluated by computing its 3D displacements and rotations from different knee angles. The first processing step registered the images in both angles based on the femur's position. The transformation matrix of the patella from the images was then calculated, which provided the rotations and translations performed by the patella from its position in the first image to its position in the second image. Analysis of these parameters for all frames provided real 3D information about the patellar displacement.
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    IoT Medical Device Architecture to Estimate Non-invasive Arterial Blood Pressure
    (2022) MORENO, Ramon; DIAS, Felipe; ARRUDA, Marcelo; OLIVEIRA, Filipe; BULHOES, Thiago; KRIEGER, Jose; GUTIERREZ, Marco
    High blood pressure (BP) is the leading cause of death worldwide. Besides being a treatable condition, alongside medication and a healthy diet, it requires regular BP measurements to assess whether a patient is properly responding to treatment. There have been many attempts to use the photoplethysmography (PPG) signal to estimate BP continuously, but there has yet to be an effective solution. This work presents our efforts to develop a new method for estimating BP from PPG and infrastructure to collect, process, and store this information. PPG signal is measured from a smartband; our App reads the data from the smartband to a smartphone, processes them using a machine learning method, and estimates BP, which is sent to a server that stores and displays the data
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    Automated radiographic bone suppression with deep convolutional neural networks
    (2021) CARDENAS, Diego Armando Cardona; FERREIRA JUNIOR, Jose Raniery; MORENO, Ramon Alfredo; REBELO, Marina de Fatima de Sa; KRIEGER, Jose Eduardo; GUTIERREZ, Marco Antonio
    Dual-energy subtraction (DES) is a technique that separates soft tissue from bones in a chest radiograph (CR). As DES requires specialized equipment, we propose an automatic method based on convolutional neural networks (CNNs) to generate virtual soft tissue images. A dataset comprising 35 pairs of CR and its soft-tissue version split in training (28 image pairs) and testing (7 image pairs) sets were used with data augmentation. We tested two types of images: the lung region's cropped image and the segmented lung image. The ribs suppression was treated as a local problem, so each image was divided into 784 patches. The U-Net architecture was used to perform bone suppression. We tested two types of loss functions: mean squared error (L-mse) and L-sm, which combines L-mse with the structural similarity index measure (SSIM). Due to the patches overlapping, it was necessary to interpolate the gray levels on the reconstructed image from the predicted patches. Evaluations were based on SSIM and root mean square error (RMSE) over the reconstructed lung area. The combination that presented the best results used the loss L-sm and the segmented lung image as input to the U-Net (SSIM of 0.858 and RMSE of 0.033). We observed that the U-Net has poor performance when trained with cropped images containing all information from the chest cavity and how the loss using local information can improve CR rib bone suppression. Our results suggest that it is possible removing the rib bones accurately in CR using CNN and a patch-based approach.y
  • article 0 Citação(ões) na Scopus
    Siamese pyramidal deep learning network for strain estimation in 3D cardiac cine-MR
    (2023) GRAVES, Catharine V.; REBELO, Marina F. S.; MORENO, Ramon A.; DANTAS-JR, Roberto N.; JR, Antonildes N. Assuncao; NOMURA, Cesar H.; GUTIERREZ, Marco A.
    Strain represents the quantification of regional tissue deformation within a given area. Myocardial strain has demonstrated considerable utility as an indicator for the assessment of cardiac function. Notably, it exhibits greater sensitivity in detecting subtle myocardial abnormalities compared to conventional cardiac function indices, like left ventricle ejection fraction (LVEF). Nonetheless, the estimation of strain poses considerable challenges due to the necessity for precise tracking of myocardial motion throughout the complete cardiac cycle. This study introduces a novel deep learning-based pipeline, designed to automatically and accurately estimate myocardial strain from three-dimensional (3D) cine-MR images. Consequently, our investigation presents a comprehensive pipeline for the precise quantification of local and global myocardial strain. This pipeline incorporates a supervised Convolutional Neural Network (CNN) for accurate segmentation of the cardiac muscle and an unsupervised CNN for robust left ventricle motion tracking, enabling the estimation of strain in both artificial phantoms and real cine-MR images. Our investigation involved a comprehensive comparison of our findings with those obtained from two commonly utilized commercial software in this field. This analysis encompassed the examination of both intra- and inter-user variability. The proposed pipeline exhibited demonstrable reliability and reduced divergence levels when compared to alternative systems. Additionally, our approach is entirely independent of previous user data, effectively eliminating any potential user bias that could influence the strain analyses.
  • article 0 Citação(ões) na Scopus
    Artificial Intelligence-Driven Screening System for Rapid Image-Based Classification of 12-Lead ECG Exams: A Promising Solution for Emergency Room Prioritization
    (2023) DIAS, Felipe Meneguitti; RIBEIRO, Estela; MORENO, Ramon Alfredo; RIBEIRO, Adele Helena; SAMESIMA, Nelson; PASTORE, Carlos Alberto; KRIEGER, Jose Eduardo; GUTIERREZ, Marco Antonio
    The electrocardiogram (ECG) serves as a valuable diagnostic tool, providing crucial information about life-threatening cardiac conditions such as atrial fibrillation and myocardial infarction. A prompt and efficient assessment of ECG exams in environments such as Emergency Rooms (ERs) can significantly enhance the chances of survival for high-risk patients. Despite the presence of numerous works on ECG classification, most of these studies have concentrated on one-dimensional ECG signals, which are commonly found in publicly available ECG datasets. Nevertheless, the practical relevance of such methods is limited in hospital settings, where ECG exams are usually stored as images. In this study, we have developed an artificial intelligence-driven screening system specifically designed to analyze 12-lead ECG images. Our proposed method has been trained on an extensive dataset comprising 99,746 12-lead ECG exams collected from the ambulatory section of a tertiary hospital. The primary goal was to precisely classify the exams into three classes: Normal (N), Atrial Fibrillation (AFib), and Other (O). The evaluation of our approach yielded AUROC scores of 93.2%, 99.2%, and 93.1% for N, AFib, and O, respectively. To further validate our approach, we conducted evaluations using the 2018 China Physiological Signal Challenge (CPSC) database. In this evaluation, we achieved AUROC scores of 91.8%, 97.5%, and 70.4% for the classes N, AFib, and O, respectively. Additionally, we assessed our method using 1,074 exams acquired in the ER and obtained AUROC values of 98.3%, 98.0%, and 97.7% for the classes N, AFib, and O, respectively. Furthermore, we developed and deployed a system with a trained model within the ER of a tertiary hospital for research purposes. This system automatically retrieves newly captured ECG chart images from the Picture Archiving and Communication System (PACS) within the ER. These images undergo necessary preprocessing steps and serve as input for our proposed classification method. This comprehensive approach established an efficient and versatile end-to-end framework for ECG classification. The results of our study highlight the potential of leveraging artificial intelligence in the screening of ECG exams, offering a promising solution for the rapid assessment and prioritization of patients in the ER.
  • article
    Telecardiology guideline in Patient Care with Acute Coronary Syndrome and Other Respiratory Diseases
    (2015) OLIVEIRA JUNIOR, Mucio Tavares de; CANESIN, Manoel Fernandes; MARCOLINO, Milena Soriano; RIBEIRO, Antonio Luiz Pinho; CARVALHO, Antonio Carlos de Camargo; REDDY, Shankar; SANTOS, Adson Roberto Franca dos; FERNANDES, Alfredo Manoel da Silva; AMARAL, Amaury Zatorre; REZENDE, Ana Carolina de; NECHAR JUNIOR, Antonio; NASCIMENTO, Bruno Ramos do; PASTORE, Carlos Alberto; WEN, Chao Lung; GUALANDRO, Danielle Menosi; NAPOLI, Domingos Guilherme; FRANCA, Francisco Faustino A. C.; FEITOSA-FILHO, Gilson Soares; SAAD, Jamil Abdalla; PILLI, Jeanne; PAULA, Leonardo Jorge Cordeiro de; LODI-JUNQUEIRA, Lucas; CESAR, Luis Antonio Machado; BODANESE, Luiz Carlos; GUTIERREZ, Marco Antonio; ALKMIM, Maria Beatriz Moreira; NUNES, Mauricio Batista; MEDEIROS, Orlando Otavio de; MORENO, Ramon Alfredo; GUNDIM, Rosangela Simoes; MONTENEGRO, Sergio Tavares; NAZIMA, Willyan Issamu
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    Cardiac Motion Estimation using Pyramid, Warping, and Cost Volume Neural Network
    (2021) GRAVES, Catharine V.; MORENO, Ramon A.; REBELO, Marina F. S.; BORDIGNOM, Adriano; NOMURA, Cesar H.; GUTIERREZ, Marco A.
    Cardiac motion quantification in magnetic resonance (MR) images provides vital information to diagnose and evaluate cardiovascular diseases. Motion quantification can be obtained from routinely acquired MR images. However, the methods available for motion estimation present many sources of inconsistencies, thus creating constraints to use it as a reliable diagnostic technique. Recently, convolutional neural networks (CNNs) have demonstrated to be a powerful tool for many different imaging tasks, including optical flow estimation, a technique widely used for motion estimation. In this work, we evaluate the suitability of a compact and powerful CNN architecture based on Pyramid, Warping, and Cost Volume (PWC) for motion estimation in synthetic cardiac resonance images. The synthetic images were generated using the extended cardiac-torso (XCAT) and MRXCAT software, which generates temporal series of highly detailed MR images and their corresponding ground-truth motion field, which would be impossible to obtain in real-life data. The CNN training was unsupervised, simulating real data. The ground-truth provided by the synthetic images was used to evaluate the PWC performance, determining its reliability. The CNN achieved an average end-point-error of 0.61 +/- 0.25 pixel and a mean absolute error of 0.38 +/- 0.15 pixel in the test set, surpassing state-of-the-art methods. The results obtained in this work indicate a high potential of the unsupervised PWC network for future applications in real cardiac images.
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    A general fully automated deep-learning method to detect cardiomegaly in chest x-rays
    (2021) FERREIRA-JUNIOR, Jose Raniery; CARDENAS, Diego Armando Cardona; MORENO, Ramon Alfredo; REBELO, Marina de Fdtima de Sa; KRIEGER, Jose Eduardo; GUTIERREZ, Marco Antonio
    Cardiomegaly is a medical condition that leads to an increase in cardiac size. It can be manually assessed using the cardiothoracic ratio from chest x-rays (CXRs). However, as that task can be challenging in such limited examinations, we propose the fully automated cardiomegaly detection in CXR. For this, we first trained convolutional networks (ConvNets) to classify the CXR as positive or negative to cardiomegaly and then evaluated the generalization potential of the trained ConvNets on independent cohorts. This work used frontal CXR images from a public dataset for training/testing and another public and one private dataset to test the models' generalization externally. Training and testing were performed using images cropped with a previously developed U-Net model. Experiments were performed with five topologically different ConvNets, data augmentation techniques, and a 50-50 class-weighing strategy to improve performance and reduce the possibility of bias to the majority class. The receiver operating characteristic curve assessed the performance of the models. DenseNet yielded the highest area under the curve (AUC) on testing (0.818) and external validation (0.809) datasets. Moreover, DenseNet obtained the highest sensitivity overall, yielding up to 0.971 on the private dataset with patients from our hospital. Therefore, DenseNet had a statistically higher potential to identify cardiomegaly. The proposed models, especially those trained with DenseNet convolutional core, automatically detected cardiomegaly with high sensitivity. To the best of our knowledge, this was the first work to design a novel general model for classifying specific deep-learning patterns of cardiomegaly in CXRs.