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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
Gouveia Nogueira, Marcelo Fibio [1, 2] ; Guilherme, Vitria Bertogna [1] ; Pronunciate, Micheli [1, 2] ; dos Santos, Priscila Helena [1, 2] ; Bezerra da Silva, Diogo Lima [3] ; Rocha, Jos Celso [3]
Total Authors: 6
Affiliation:
[1] Sao Paulo State Univ UNESP, Sch Sci & Languages, Dept Biol Sci, Lab Embryon Micromanipulat, BR-19806900 Assis, SP - Brazil
[2] Sao Paulo State Univ UNESP, Biosci Inst, Dept Pharmacol, Multiuser Facil FitoFarmaTec, BR-18618689 Botucatu, SP - Brazil
[3] Sao Paulo State Univ UNESP, Sch Sci & Languages, Dept Biol Sci, Lab Appl Math, BR-19806900 Assis, SP - Brazil
Total Affiliations: 3
Document type: Journal article
Source: SENSORS; v. 18, n. 12 DEC 2018.
Web of Science Citations: 0
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 x magnification) and the experimental group (stereomicroscope with maximum of magnification plus 4 x 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. (AU)

FAPESP's process: 17/19323-5 - Classification of human embryos using the techniques of time-lapse, digital image processing and Artificial Intelligence.
Grantee:José Celso Rocha
Support Opportunities: Regular Research Grants
FAPESP's process: 12/50533-2 - GIFT: genomic improvement of fertilization traits in Danish and Brazilian cattle
Grantee:Marcelo Fábio Gouveia Nogueira
Support Opportunities: Research Projects - Thematic Grants