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16 results on '"Gouveia Nogueira, Marcelo Fábio"'

2. Distinct Sources of a Bovine Blastocyst Digital Image Do not Produce the Same Classification by a Previously Trained Software Using Artificial Neural Network

Author

Guilherme, Vitória Bertogna, Pronunciate, Micheli, dos Santos, Priscila Helena, de Souza Ciniciato, Diego, Takahashi, Maria Beatriz, Rocha, José Celso, Gouveia Nogueira, Marcelo Fábio, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Holzinger, Andreas, editor, Silva, Hugo Plácido, editor, and Helfert, Markus, editor

Published
2019
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5. EMBRYOLOGISTS VERSUS ARTIFICIAL INTELLIGENCE: PREDICTING CLINICAL PREGNANCY OUT OF A TRANSFERRED EMBRYO WHO PERFORMS IT BETTER?

Author

Jacobs, Catherine K., primary, Satoshi Ferreira, André Satoshi, additional, Chéles, Dóris, additional, Barreto, Mariana, additional, Erberelli, Renata, additional, Alegretti, Jose Roberto, additional, Motta, Eduardo LA., additional, Gouveia Nogueira, Marcelo Fábio, additional, Rocha, José Celso, additional, and Lorenzon, Aline R., additional

Published
2022
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7. Modulating the lipid profile of blastocyst cell membrane with DPPC multilamellar vesicles

Author

De Rossi, Hugo, primary, Bortoliero Costa, Camila, additional, Rodrigues-Rossi, Luana Teixeira, additional, Barros Nunes, Giovana, additional, Spinosa Chéles, Dóris, additional, Maran Pereira, Isabella, additional, Rocha, Daniele F. O., additional, Feitosa, Eloi, additional, Colnaghi Simionato, Ana Valéria, additional, Zoccal Mingoti, Gisele, additional, Benites Aoki, Pedro Henrique, additional, and Gouveia Nogueira, Marcelo Fábio, additional

Published
2022
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8. An image processing protocol to extract variables predictive of human embryo fitness for assisted reproduction

Author

Chéles, Dóris Spinosa, primary, Ferreira, André Satoshi, additional, de Jesus, Isabela Sueitt, additional, Fernandez, Eleonora Inácio, additional, Pinheiro, Gabriel Martins, additional, Dal Molin, Eloiza Adriane, additional, Alves, Wallace, additional, de Souza, Rebeca Colauto Milanezi, additional, Bori, Lorena, additional, Meseguer, Marcos, additional, Rocha, José Celso, additional, and Gouveia Nogueira, Marcelo Fábio, additional

Published
2022
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9. NON-INVASIVE METHOD FOR BLASTOCYST PLOIDY PREDICTION BASED ON ARTIFICIAL INTELLIGENCE, TIME-LAPSE IMAGES AND PATIENT PARAMETERS

Author

Lorenzon, Aline R., primary, Jacobs, Catherine, additional, Milanezi de Souza, Rebeca Colauto, additional, Nicolielo, Mariana, additional, Erberelli, Renata, additional, Barros, Bruna, additional, Leme de Marchi, Patricia, additional, Alegretti, Jose Roberto, additional, Motta, Eduardo L.A., additional, Gouveia Nogueira, Marcelo Fábio, additional, and Rocha, Jose Celso, additional

Published
2021
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16. Artificial Intelligence-Based Grading Quality of Bovine Blastocyst Digital Images: Direct Capture with Juxtaposed Lenses of Smartphone Camera and Stereomicroscope Ocular Lens.

Author

Gouveia Nogueira, Marcelo Fábio, Bertogna Guilherme, Vitória, Pronunciate, Micheli, dos Santos, Priscila Helena, Lima Bezerra da Silva, Diogo, and Rocha, José Celso

Subjects
*ARTIFICIAL intelligence, *BLASTOCYST, *DIGITAL image processing, *ARTIFICIAL neural networks, *IMAGE analysis
Abstract

In this study, we developed an online graphical and intuitive interface connected to a server aiming to facilitate professional access worldwide to those facing problems with bovine blastocysts classification. The interface Blasto3Q, where 3Q refers to the three qualities of the blastocyst grading, contains a description of 24 variables that were extracted from the image of the blastocyst and analyzed by three Artificial Neural Networks (ANNs) that classify the same loaded image. The same embryo (i.e., the biological specimen) was submitted to digital image capture by the control group (inverted microscope with 40× magnification) and the experimental group (stereomicroscope with maximum of magnification plus 4× zoom from the cell phone camera). The images obtained from the control and experimental groups were uploaded on Blasto3Q. Each image from both sources was evaluated for segmentation and submitted (only if it could be properly or partially segmented) for automatic quality grade classification by the three ANNs of the Blasto3Q program. Adjustments on the software program through the use of scaling algorithm software were performed to ensure the proper search and segmentation of the embryo in the raw images when they were captured by the smartphone, since this source produced small embryo images compared with those from the inverted microscope. With this new program, 77.8% of the images from smartphones were successfully segmented and from those, 85.7% were evaluated by the Blasto3Q in agreement with the control group. [ABSTRACT FROM AUTHOR]

Published
2018
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