Algorithms for automatic segmentation of bovine embryos produced <i>in vitro</i>

Melo, Dora; Nascimento, Marcelo Zanchetta do; Oliveira, Domingos Lucas Latorre de; Neves, Leandro Alves; Annes, Kelly

doi:10.1088/1742-6596/490/1/012125

Cited by 7 publications

(7 citation statements)

References 5 publications

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“…In ref. 8 , a non-supervised algorithm for the automatic segmentation of bovine embryos was proposed, but this algorithm did not use artificial intelligence or classify the embryo quality according to the criteria of the IETS. In ref.…”

Section: Discussionmentioning

confidence: 99%

“…Several methods have been or are being developed to provide a better solution for embryo classification, including a semi-automatized image segmentation process with the use of artificial intelligence (AI) for human embryos 7 , an automatic segmentation procedure of bovine embryos, but without the use of AI 8 , a semi-automatized grading method of human blastocyst using a support vector machine 9 , embryo metabolism analysis, cellular respiration measurements, the use of zona pellucida birefringence, microRNA profile determination, analysis based on logistic regression and evaluation by time-lapse video (reviewed by ref. 10 ).…”

Section: Introductionmentioning

confidence: 99%

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A Method Based on Artificial Intelligence To Fully Automatize The Evaluation of Bovine Blastocyst Images

Rocha

Passalia

Matos

et al. 2017

Sci Rep

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Morphological analysis is the standard method of assessing embryo quality; however, its inherent subjectivity tends to generate discrepancies among evaluators. Using genetic algorithms and artificial neural networks (ANNs), we developed a new method for embryo analysis that is more robust and reliable than standard methods. Bovine blastocysts produced in vitro were classified as grade 1 (excellent or good), 2 (fair), or 3 (poor) by three experienced embryologists according to the International Embryo Technology Society (IETS) standard. The images (n = 482) were subjected to automatic feature extraction, and the results were used as input for a supervised learning process. One part of the dataset (15%) was used for a blind test posterior to the fitting, for which the system had an accuracy of 76.4%. Interestingly, when the same embryologists evaluated a sub-sample (10%) of the dataset, there was only 54.0% agreement with the standard (mode for grades). However, when using the ANN to assess this sub-sample, there was 87.5% agreement with the modal values obtained by the evaluators. The presented methodology is covered by National Institute of Industrial Property (INPI) and World Intellectual Property Organization (WIPO) patents and is currently undergoing a commercial evaluation of its feasibility.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Method Based on Artificial Intelligence To Fully Automatize The Evaluation of Bovine Blastocyst Images

Rocha

Passalia

Matos

et al. 2017

Sci Rep

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“…Poor (Bó and Mapletoft, 2013). W ith time, several methods have been or are being developed to provide a better solution for embryo classification, including a semi-automatized image segmentation process with the use of artificial intelligence (AI) for human embryos (Manna et al, 2013), an automatic segmentation procedure of bovine embryos, but without the use of AI (Melo et al, 2014), embryo metabolism analysis, cellular respiration measurements, the use of zona pellucida birefringence, microRNA profile determination, analysis based on logistic regression and evaluation by time-lapse video (Rocha et al, 2016). However, all the above methods are totally ineffective, subj ective and old, visual morphological analysis is still widely used (Farin et al, 1995 andRichardson et al, 2015).…”

Section: Evaluation Of Animal Blastocyst Imagesmentioning

confidence: 99%

‘Artificial Intelligence’ as an Intelligent Tool for the Future Animal Production and Better Management: A Review

Priya

Dutt

Singh

et al. 2022

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With the tremendous increase in human population the demand of milk and meat is also increasing. This necessitates the centralization of animal husbandry sector both in developed and developing countries. More production has to be taken from fewer animals which seem to be possible only with the usage of advanced technologies. Earlier this seems to be impractical due to non-availability of such technologies to the farmers. Nowadays, computational power is very cheap and easily available. Artificial intelligence is one such field of computer science devoted to creating computing machines and systems that perform operations analogous to human learning and decision-making. Artificial intelligence includes the machine learning, deep learning and predictive analytics. With the implementation of various software and algorithms, the production has been increased from the farm animals along with better management and welfare. This paper illustrates the application domains and maneuvering of artificial intelligence in the animal husbandry sector.

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“…Other kinds of evaluation have been used in attempt of find a better solution to classify the embryo quality. As example, Melo et al [22] described an automatic segmentation procedure of bovine embryos, without the use of Artificial Intelligence (AI) and whose proposal was calculated the sensitivity, specificity and accuracy's method. The database used in this work was made up of 30 histological slides (staining with Sudan Black B) in different stage of development:…”

Section: 2mentioning

confidence: 99%

Distinct Sources of a Bovine Blastocyst Digital Image do not Produce the Same Classification by a Previously Trained Software using Artificial Neural Network

Guilherme

Pronunciate

Santos

et al. 2018

Preprint

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We develop an online graphical and intuitive interface connected to a server aiming to facilitate access to professionals worldwide that face problems with bovine blastocysts classification. The interface Blasto3Q (3Q is referred to the three qualities of the blastocyst grading) contains a description of 24 variables that are extracted from the image of the blastocyst and analyzed by three Artificial Neural Networks (ANNs) that classifies 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 40x of magnification) and to experimental group (stereomicroscope with maximum of magnification plus 4x zoom from the cell phone).The 36 images obtained from control and experimental groups were uploaded on the Blasto3Q. Each image from both sources was evaluated for segmentation and submitted (only if it could be properly or partially segmented) to the quality grade classification by the three ANNs of the Blasto3Q program. In the group control, all the images were properly segmented, whereas 38.9% (07/18) and 61.1% (11/18) of the images from the experimental group, respectively could not be segmented or were partially segmented. The percentage of agreement was calculated when the same blastocyst was evaluated by the same ANN from the two sources (control and experimental groups). On the 54 potential evaluations of the three ANNs (i.e., 18 images been evaluated by the three networks) from the experimental group only 22.2% agreed with evaluations of the control (12/54). Of the remaining 42 disagreed evaluations from experimental group, 21 were unable to be performed and 21 were wrongly processed when compared with control evaluation.

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Algorithms for automatic segmentation of bovine embryos produced in vitro

Cited by 7 publications

References 5 publications

A Method Based on Artificial Intelligence To Fully Automatize The Evaluation of Bovine Blastocyst Images

A Method Based on Artificial Intelligence To Fully Automatize The Evaluation of Bovine Blastocyst Images

‘Artificial Intelligence’ as an Intelligent Tool for the Future Animal Production and Better Management: A Review

Distinct Sources of a Bovine Blastocyst Digital Image do not Produce the Same Classification by a Previously Trained Software using Artificial Neural Network

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