Dr. Mariana Pinheiro Bento Profile

Dr. Mariana Pinheiro Bento

Assistant Professor at Univ of Calgary

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Publications (8)

Proceedings Article | 4 April 2022 Poster + Paper

Automated quality check of corpus callosum segmentation using deep learning

William Herrera, Simone Appenzeller, Fabiano Reis, Danilo Rodrigues Pereira, Mariana Pinheiro Bento, Letícia Rittner

Proceedings Volume 12032, 120322Q (2022) https://doi.org/10.1117/12.2612835

KEYWORDS: Image segmentation, Tumors, Magnetic resonance imaging, Machine learning, Brain, Convolutional neural networks

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Proceedings Article | 10 December 2021 Paper

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

Justin Park, Richard Frayne, Mariana Bento

Proceedings Volume 12088, 120880U (2021) https://doi.org/10.1117/12.2605994

KEYWORDS: Data modeling, Pathology, Convolutional neural networks, Magnetic resonance imaging, Alzheimer's disease, Magnetism, Scanners, Neural networks, Image classification, Image segmentation

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Deep learning neural networks are a common tool in medical imaging and frequently used to solve a variety of complex problems. Magnetic resonance (MR) images are frequently employed to develop these networks because of their high spatial resolution and user selectable image contrast between tissues. More advanced deep learning models are being developed, which when combined with improvements in MR image acquisition techniques, will allow for image analysis techniques that are more efficient yet able to solve increasingly challenging problems. A current significant disadvantage of deep learning networks is that they are extremely sensitive to the distribution of the data used for training, therefore, network implementation can be challenging in clinical applications with heterogeneous images. The main problem is that, in a clinical environment, data distributions of target datasets can vary from subject-to-subject due to differences in scanner vendor, magnetic field strength, and the setting of specific MR acquisition parameters. These variations create inherent scan variability that diversifies the data distributions of different datasets. This effect can result in the model becoming inaccurate and producing undesirable outcomes. Thus, to improve model generalizability, we explored a supervised domain-adaptation approach. To test this method, we created a convolutional neural network model that performed a classification task and was composed of three components: (1) a feature extractor, (2) a pathology classifier, and (3) a domain classifier. In a single, unified training process, the pathology classifier was trained by minimizing the pathology loss function and the domain classifier was trained by maximizing the domain loss function. This procedure allows the model to penalize learning of features specific to the domain, and thus attempts to produce a domaininvariant feature vector. The performance of this domain-adapted model was compared to the same model but without domain classification (i.e., a baseline traditional model consisting of a feature extractor and a pathology classifier). We found that the domain-adapted model achieved a higher accuracy rate in predicting images from both source and target datasets.

Proceedings Article | 13 March 2019 Presentation + Paper

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

M. Bento , R. Souza, M. Salluzzi, R. Frayne

Proceedings Volume 10950, 1095008 (2019) https://doi.org/10.1117/12.2512819

KEYWORDS: Computer aided diagnosis and therapy, Magnetic resonance imaging, Data acquisition, Scanners, Computer aided design, Data modeling, Reliability, Brain, Alzheimer's disease, Data centers

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Proceedings Article | 12 March 2018 Presentation + Paper

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

Gustavo Pinheiro, Giovana Cover, Mariana Bento, Leticia Rittner

Proceedings Volume 10578, 1057815 (2018) https://doi.org/10.1117/12.2293381

KEYWORDS: Diffusion tensor imaging, Brain, Diffusion, Structured optical fibers, Magnetic resonance imaging, Brain mapping, Data conversion, Statistical analysis, Neuroimaging, Shape analysis

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Proceedings Article | 24 March 2016 Paper

3D texture-based classification applied on brain white matter lesions on MR images

Mariana Leite, David Gobbi, Marina Salluzi, Richard Frayne, Roberto Lotufo, Letícia Rittner

Proceedings Volume 9785, 97852N (2016) https://doi.org/10.1117/12.2216285

KEYWORDS: Magnetic resonance imaging, 3D image processing, Brain, Neuroimaging, 3D acquisition, Image processing, Databases, Algorithm development, Statistical analysis, Image classification

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Proceedings Article | 17 March 2015 Paper

Web-based platform for collaborative medical imaging research

Leticia Rittner, Mariana Bento, André Costa, Roberto Souza, Rubens Machado, Roberto Lotufo

Proceedings Volume 9418, 94180B (2015) https://doi.org/10.1117/12.2081697

KEYWORDS: Medical imaging, Image processing, Image segmentation, Medical research, Brain, Clouds, Internet, Analytical research, Inspection, Image analysis

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SPIE Journal Paper | 19 February 2015

Etiology-based classification of brain white matter hyperintensity on magnetic resonance imaging

Mariana Leite, Leticia Rittner, Simone Appenzeller, Heloísa Helena Ruocco, Roberto Lotufo

JMI, Vol. 2, Issue 01, 014002, (February 2015) https://doi.org/10.1117/12.10.1117/1.JMI.2.1.014002

KEYWORDS: Magnetic resonance imaging, Feature selection, Brain, Neuroimaging, Principal component analysis, Quantization, Image segmentation, Tissues, Feature extraction, Image classification

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Proceedings Article | 13 March 2013 Paper

Analysis of brain white matter hyperintensities using pattern recognition techniques

Mariana Bento, Letícia Rittner, Simone Appenzeller, Aline Lapa, Roberto Lotufo

Proceedings Volume 8669, 86693P (2013) https://doi.org/10.1117/12.2006924

KEYWORDS: Brain, Feature selection, Magnetic resonance imaging, Image segmentation, Pattern recognition, Neuroimaging, Tissues, Image classification, Medical imaging, Databases

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Showing 5 of 8 publications

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