M. Bento

发表

Automatic identification of atherosclerosis subjects in a heterogeneous MR brain imaging data set.

R. Frayne, Yunyan Zhang, M. Bento, 2019, Magnetic resonance imaging.

Harmonization of multi-scanner in vivo magnetic resonance spectroscopy: ENIGMA consortium task group considerations

Brian A. Taylor, Friederike I. Tam, S. Ehrlich, 2023, Frontiers in Neurology.

Supervised domain adaptation approach on heterogenous, multi-center MR imaging datasets

Richard Frayne, Mariana Bento, Justin C. Park, 2021, Symposium on Medical Information Processing and Analysis.

Reliability of computer-aided diagnosis tools with multi-center MR datasets: impact of training protocol

Richard Frayne, Marina Salluzzi, M. Bento, 2019, Medical Imaging.

Data-Driven Corpus Callosum Parcellation Method Through Diffusion Tensor Imaging

Simone Appenzeller, Leticia Rittner, Giovana S. Cover, 2017, IEEE Access.

Analysis of Brain White Matter Hyperintensities using Image Processing and Pattern Recognition Techniques

R. Lotufo, M. Bento, S. Appenzeller, 2013 .

Normal Brain Aging: Prediction of Age, Sex and White Matter Hyperintensities Using a MR Image-Based Machine Learning Technique

Richard Frayne, Mariana P. Bento, Roberto Souza, 2018, ICIAR.

V-Net and U-Net for Ischemic Stroke Lesion Segmentation in a Small Dataset of Perfusion Data

Letícia Rittner, Mariana P. Bento, Gustavo Retuci Pinheiro, 2018, BrainLes@MICCAI.

Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge

Simon Andermatt, Simon Pezold, Sang Hyun Park, 2019, IEEE Transactions on Medical Imaging.

Deep Learning in Large and Multi-Site Structural Brain MR Imaging Datasets

Justin C. Park, R. Frayne, M. Bento, 2022, Frontiers in Neuroinformatics.

Dual-domain Cascade of U-nets for Multi-channel Magnetic Resonance Image Reconstruction

Richard Frayne, Roberto Souza, Mariana Bento, 2019, Magnetic resonance imaging.

Corpus Callosum Shape Signature for Segmentation Evaluation

Leticia Rittner, William G. Herrera, M. Bento, 2019, XXVI Brazilian Congress on Biomedical Engineering.

A framework for quality control of corpus callosum segmentation in large-scale studies

Mariana Bento, Leticia Rittner, Mariana Pereira, 2020, Journal of Neuroscience Methods.

Automatic callosal fiber convergence plane computation through DTI-based divergence map

Letícia Rittner, Mariana P. Bento, Gustavo R. Pinheiro, 2018, Medical Imaging.

WMH Segmentation Challenge: A Texture-Based Classification Approach

Richard Frayne, Roberto de Alencar Lotufo, Letícia Rittner, 2017, BrainLes@MICCAI.

Probabilistic Segmentation of Brain White Matter Lesions Using Texture-Based Classification

Richard Frayne, Roberto de Alencar Lotufo, Letícia Rittner, 2017, ICIAR.

Web-based platform for collaborative medical imaging research

Roberto de Alencar Lotufo, Leticia Rittner, Rubens C. Machado, 2015, Medical Imaging.

Multicenter Imaging Studies: Automated Approach to Evaluating Data Variability and the Role of Outliers

Richard Frayne, Mariana P. Bento, Roberto Souza, 2018, 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI).

Computational methods for corpus callosum segmentation on MRI: A systematic literature review

Letícia Rittner, Mariana P. Bento, Simone Appenzeller, 2018, Comput. Methods Programs Biomed..

Quality Control Framework for Large MR Datasets: Automated Approaches to Outlier Detection

Richard Frayne, Mariana P. Bento, Roberto Souza, 2019, XXVI Brazilian Congress on Biomedical Engineering.

Automatic MR image quality evaluation using a Deep CNN: A reference-free method to rate motion artifacts in neuroimaging

Roberto de Alencar Lotufo, Letícia Rittner, Mariana P. Bento, 2021, Comput. Medical Imaging Graph..

Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge

Sang Hyun Park, W. M. van der Flier, Matt Berseth, 2019, IEEE Transactions on Medical Imaging.

Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities; Results of the WMH Segmentation Challenge

Sang Hyun Park, Matt Berseth, S. Ourselin, 2019 .

Reframing the Brain Age Prediction Problem to a More Interpretable and Quantitative Approach

M. Bento, Roberto Souza, Neha Gianchandani, 2023, ArXiv.

Computational methods for corpus callosum segmentation on MRI: A systematic literature review

Giovana S. Cover, M. Bento, L. Rittner, 2018, Comput. Methods Programs Biomed..

Analysis of brain white matter hyperintensities using pattern recognition techniques

Roberto de Alencar Lotufo, Letícia Rittner, Mariana P. Bento, 2013, Medical Imaging.