scholarly journals Semantic segmentation of human oocyte images using deep neural networks

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Anna Targosz ◽  
Piotr Przystałka ◽  
Ryszard Wiaderkiewicz ◽  
Grzegorz Mrugacz
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Semantic Segmentation ◽  
Machine Learning Techniques ◽  
In Vitro Fertilisation ◽  
Human Oocyte ◽  
Automatic Assessment ◽  
Neural Models ◽  
Developmental Potential

AbstractBackgroundInfertility is a significant problem of humanity. In vitro fertilisation is one of the most effective and frequently applied ART methods. The effectiveness IVF depends on the assessment and selection of gametes and embryo with the highest developmental potential. The subjective nature of morphological assessment of oocytes and embryos is still one of the main reasons for seeking effective and objective methods for assessing quality in automatic manner. The most promising methods to automatic classification of oocytes and embryos are based on image analysis aided by machine learning techniques. The special attention is paid on deep neural networks that can be used as classifiers solving the problem of automatic assessment of the oocytes/embryos.MethodsThis paper deals with semantic segmentation of human oocyte images using deep neural networks in order to develop new version of the predefined neural networks. Deep semantic oocyte segmentation networks can be seen as medically oriented predefined networks understanding the content of the image. The research presented in the paper is focused on the performance comparison of different types of convolutional neural networks for semantic oocyte segmentation. In the case study, the merits and limitations of the selected deep neural networks are analysed.Results71 deep neural models were analysed. The best score was obtained for one of the variants of DeepLab-v3-ResNet-18 model, when the training accuracy (Acc) reached about 85% for training patterns and 79% for validation ones. The weighted intersection over union (wIoU) and global accuracy (gAcc) for test patterns were calculated, as well. The obtained values of these quality measures were 0,897 and 0.93, respectively.ConclusionThe obtained results prove that the proposed approach can be applied to create deep neural models for semantic oocyte segmentation with the high accuracy guaranteeing their usage as the predefined networks in other tasks.

scholarly journals Semantic segmentation of citrus-orchard using deep neural networks and multispectral UAV-based imagery

2021 ◽  
Author(s):  
Lucas Prado Osco ◽  
Keiller Nogueira ◽  
Ana Paula Marques Ramos ◽  
Mayara Maezano Faita Pinheiro ◽  
Danielle Elis Garcia Furuya ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Semantic Segmentation ◽  
Citrus Orchard

scholarly journals Deep neural networks for quantum circuit mapping

2021 ◽  
Author(s):  
Giovanni Acampora ◽  
Roberto Schiattarella
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Quantum Algorithms ◽  
Quantum Algorithm ◽  
Quantum Circuit ◽  
Machine Learning Techniques ◽  
Quantum Computers ◽  
Physical Constraints ◽  
Proper Mapping ◽  
Correct Execution

AbstractQuantum computers have become reality thanks to the effort of some majors in developing innovative technologies that enable the usage of quantum effects in computation, so as to pave the way towards the design of efficient quantum algorithms to use in different applications domains, from finance and chemistry to artificial and computational intelligence. However, there are still some technological limitations that do not allow a correct design of quantum algorithms, compromising the achievement of the so-called quantum advantage. Specifically, a major limitation in the design of a quantum algorithm is related to its proper mapping to a specific quantum processor so that the underlying physical constraints are satisfied. This hard problem, known as circuit mapping, is a critical task to face in quantum world, and it needs to be efficiently addressed to allow quantum computers to work correctly and productively. In order to bridge above gap, this paper introduces a very first circuit mapping approach based on deep neural networks, which opens a completely new scenario in which the correct execution of quantum algorithms is supported by classical machine learning techniques. As shown in experimental section, the proposed approach speeds up current state-of-the-art mapping algorithms when used on 5-qubits IBM Q processors, maintaining suitable mapping accuracy.

Effect and possible mechanisms of melatonin treatment on the quality and developmental potential of aged bovine oocytes

2017 ◽  
Vol 29 (9) ◽  
pp. 1821 ◽  
Author(s):  
Shuang Liang ◽  
Jing Guo ◽  
Jeong-Woo Choi ◽  
Nam-Hyung Kim ◽  
Xiang-Shun Cui
Keyword(s):  
In Vitro Fertilisation ◽  
Cortical Granule ◽  
Melatonin Treatment ◽  
Developmental Potential ◽  
Exogenous Melatonin ◽  
Atp Production ◽  
Quality Deterioration ◽  
Oocyte Aging ◽  
Bovine Oocytes

After reaching the metaphase II (MII) stage, unfertilised oocytes undergo a time-dependent process of quality deterioration referred to as oocyte aging. The associated morphological and cellular changes lead to decreased oocyte developmental potential. This study investigated the effect of exogenous melatonin supplementation on in vitro aged bovine oocytes and explored its underlying mechanisms. The levels of cytoplasmic reactive oxygen species and DNA damage response in bovine oocytes increased during in vitro aging. Meanwhile, maturation promoting factor activity significantly decreased and the proportion of morphologically abnormal oocytes significantly increased. Melatonin supplementation significantly decreased quality deterioration in aged bovine MII oocytes (P < 0.05). Additionally, it decreased the frequency of aberrant spindle organisation and cortical granule release during oocyte aging (P < 0.05). In the melatonin-supplemented group, mitochondrial membrane potential and ATP production were significantly increased compared with control. Furthermore, melatonin treatment significantly increased the speed of development of bovine oocytes to the blastocyst stage after in vitro fertilisation and significantly decreased the apoptotic rate in the blastocysts (P < 0.05). The expression of Bax and Casp3 in the blastocysts was significantly reduced after treatment with melatonin, whereas expression of Bcl2 significantly increased (P < 0.05). In conclusion, these findings suggest that supplementation of aged bovine oocytes with exogenous melatonin improves oocyte quality, thereby enhancing the developmental capacity of early embryos.

Semantic Segmentation of Mammograms Using Pre-Trained Deep Neural Networks

2021 ◽  
Author(s):  
Rodrigo Leite Prates ◽  
Wilfrido Gomez-Flores ◽  
Wagner Pereira
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Semantic Segmentation

Educational Software Based on Matlab GUIs for Neural Networks Courses

2016 ◽  
pp. 333-358 ◽  
Author(s):  
Pablo Díaz-Moreno ◽  
Juan José Carrasco ◽  
Emilio Soria-Olivas ◽  
José M. Martínez-Martínez ◽  
Pablo Escandell-Montero ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Educational Software ◽  
Machine Learning Techniques ◽  
Teaching Tool ◽  
Neural Models ◽  
Learning Techniques ◽  
The World ◽  
Programming Skills

Neural Networks (NN) are one of the most used machine learning techniques in different areas of knowledge. This has led to the emergence of a large number of courses of Neural Networks around the world and in areas where the users of this technique do not have a lot of programming skills. Current software that implements these elements, such as Matlab®, has a number of important limitations in teaching field. In some cases, the implementation of a MLP requires a thorough knowledge of the software and of the instructions that train and validate these systems. In other cases, the architecture of the model is fixed and they do not allow an automatic sweep of the parameters that determine the architecture of the network. This chapter presents a teaching tool for the its use in courses about neural models that solves some of the above-mentioned limitations. This tool is based on Matlab® software.

scholarly journals MOLI: multi-omics late integration with deep neural networks for drug response prediction

2019 ◽  
Vol 35 (14) ◽  
pp. i501-i509 ◽  
Author(s):  
Hossein Sharifi-Noghabi ◽  
Olga Zolotareva ◽  
Colin C Collins ◽  
Martin Ester
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Prediction Accuracy ◽  
Drug Response ◽  
Deep Neural Networks ◽  
Response Prediction ◽  
Supplementary Information ◽  
Precision Oncology

Abstract Motivation Historically, gene expression has been shown to be the most informative data for drug response prediction. Recent evidence suggests that integrating additional omics can improve the prediction accuracy which raises the question of how to integrate the additional omics. Regardless of the integration strategy, clinical utility and translatability are crucial. Thus, we reasoned a multi-omics approach combined with clinical datasets would improve drug response prediction and clinical relevance. Results We propose MOLI, a multi-omics late integration method based on deep neural networks. MOLI takes somatic mutation, copy number aberration and gene expression data as input, and integrates them for drug response prediction. MOLI uses type-specific encoding sub-networks to learn features for each omics type, concatenates them into one representation and optimizes this representation via a combined cost function consisting of a triplet loss and a binary cross-entropy loss. The former makes the representations of responder samples more similar to each other and different from the non-responders, and the latter makes this representation predictive of the response values. We validate MOLI on in vitro and in vivo datasets for five chemotherapy agents and two targeted therapeutics. Compared to state-of-the-art single-omics and early integration multi-omics methods, MOLI achieves higher prediction accuracy in external validations. Moreover, a significant improvement in MOLI’s performance is observed for targeted drugs when training on a pan-drug input, i.e. using all the drugs with the same target compared to training only on drug-specific inputs. MOLI’s high predictive power suggests it may have utility in precision oncology. Availability and implementation https://github.com/hosseinshn/MOLI. Supplementary information Supplementary data are available at Bioinformatics online.

Putrescine supplementation during in vitro maturation of aged mouse oocytes improves the quality of blastocysts

2017 ◽  
Vol 29 (7) ◽  
pp. 1392 ◽  
Author(s):  
Dandan Liu ◽  
Guolong Mo ◽  
Yong Tao ◽  
Hongmei Wang ◽  
X. Johné Liu
Keyword(s):  
Oocyte Maturation ◽  
In Vitro Maturation ◽  
Inner Cell Mass ◽  
Cell Mass ◽  
In Vitro Fertilisation ◽  
Aged Mouse ◽  
Developmental Potential ◽  
The Impact

Mouse ovaries exhibit a peri-ovulatory rise of ornithine decarboxylase and its product putrescine concurrent with oocyte maturation. Older mice exhibit a deficiency of both the enzyme and putrescine. Peri-ovulatory putrescine supplementation in drinking water increases ovarian putrescine levels, reduces embryo resorption and increases live pups in older mice. However, it is unknown if putrescine acts in the ovaries to improve oocyte maturation. This study examined the impact of putrescine supplementation during oocyte in vitro maturation (IVM) on the developmental potential of aged oocytes. Cumulus–oocyte complexes from 9–12-month-old C57BL/6 mice were subjected to IVM with or without 0.5 mM putrescine, followed by in vitro fertilisation and culture to the blastocyst stage. Putrescine supplementation during IVM did not influence the proportion of oocyte maturation, fertilisation or blastocyst formation, but significantly increased blastocyst cell numbers (44.5 ± 1.9, compared with 36.5 ± 1.9 for control; P = 0.003). The putrescine group also had a significantly higher proportion of blastocysts with top-grade morphology (42.9%, compared with 26.1% for control; P = 0.041) and a greater proportion with octamer-binding transcription factor 4 (OCT4)-positive inner cell mass (38.3%, compared with 19.8% for control; P = 0.005). Therefore, putrescine supplementation during IVM improves egg quality of aged mice, providing proof of principle for possible application in human IVM procedures for older infertile women.

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