Grading of mammalian cumulus oocyte complexes using machine learning for in vitro embryo culture

Viswanath, P.; Weiser, Tobias; Chintala, Phalgun; Mandal, Sujit; Dutta, Rahul

doi:10.1109/bhi.2016.7455862

Cited by 10 publications

(5 citation statements)

References 8 publications

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“…We have also developed automated oocyte grading algorithms based on machine learning techniques [28,29], but full descriptions are beyond the scope of this account. In the final stage of the workflow, we attempted in vitro embryo culture of irradiated oocytes (figure 2.5) to ascertain viability.…”

Section: Experimental Methods and Resultsmentioning

confidence: 99%

Brilliant cresyl blue enhanced optoacoustic imaging enables non-destructive imaging of mammalian ovarian follicles for artificial reproduction

Dutta

Mandal

Lin

et al. 2020

J. R. Soc. Interface.

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In the field of reproductive biology, there is a strong need for a suitable tool capable of non-destructive evaluation of oocyte viability and function. We studied the application of brilliant cresyl blue (BCB) as an intra-vital exogenous contrast agent using multispectral optoacoustic tomography (MSOT) for visualization of porcine ovarian follicles. The technique provided excellent molecular sensitivity, enabling the selection of competent oocytes without disrupting the follicles. We further conducted in vitro embryo culture, molecular analysis (real-time and reverse transcriptase polymerase chain reaction) and DNA fragmentation analysis to comprehensively establish the safety of BCB-enhanced MSOT imaging in monitoring oocyte viability. Overall, the experimental results suggest that the method offers a significant advance in the use of contrast agents and molecular imaging for reproductive studies. Our technique improves the accurate prediction of ovarian reserve significantly and, once standardized for in vivo imaging, could provide an effective tool for clinical infertility management.

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Section: Experimental Methods and Resultsmentioning

confidence: 99%

Brilliant cresyl blue enhanced optoacoustic imaging enables non-destructive imaging of mammalian ovarian follicles for artificial reproduction

Dutta

Mandal

Lin

et al. 2020

J. R. Soc. Interface.

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“…Time-lapse analysis predicting the number of cells in embryos was proposed in [3]. Classification of swine cumulus oocyte complexes (i.e., before stripping away the cumulus cells, unlike in our data) was considered in [4]. The authors examined the number of cumulus cell layers and the homogeneity of the cytoplasm and used random forests for the automatic classification of oocytes.…”

Section: Previous Workmentioning

confidence: 99%

Automatic evaluation of human oocyte developmental potential from microscopy images

Baručić

Kybic

Teplá

et al. 2021

17th International Symposium on Medical Information Processing and Analysis

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Infertility is becoming an issue for an increasing number of couples. The most common solution, in vitro fertilization, requires embryologists to carefully examine light microscopy images of human oocytes to determine their developmental potential. We propose an automatic system to improve the speed, repeatability, and accuracy of this process. We first localize individual oocytes and identify their principal components using CNN (U-Net) segmentation. We calculate several descriptors based on geometry and texture. The final step is an SVM classifier. Both the segmentation and classification training are based on expert annotations. The presented approach leads to the classification accuracy of 70%.

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“…A majority of studies used GLCM analysis to reveal the localization pattern of fluorescence staining (Sutton-McDowall et al, 2015;Tan et al, 2016). A few studies further investigated the predictive values of these features when evaluating the developmental potential of oocytes or cumulus-oocyte complexes (Basile et al, 2010;Chavez-Badiola et al, 2020;Viswanath et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Identification of significant imaging features for sensing oocyte viability

Chen

Liu

Zuo

et al. 2022

Microscopy Res & Technique

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The evaluation of oocyte viability in the laboratory is limited to the morphological assessment by naked eyes, but the realization that most normal-appearing oocytes may conceal abnormalities prompts the search for automated approaches that can detect the abnormalities imperceptible to naked eyes. In this study, we developed an image processing pipeline applicable to bright-field microscope images to quantify the causal relationship between the quantitative imaging features and the developmental potential of oocytes. We acquired 19 imaging features of approximately 700 oocytes and determined two imaging subtypes, namely viable and nonviable subtypes that correlated closely with a viability fluorescence indicator and cleavage rates. The causal relationship between these imaging features and oocyte viability was derived from a viability-oriented Bayesian network that was developed based on the Bayesian information criterion and Tabu search. Our experimental results revealed that entropy with mean Gray Level Co-Occurrence Matrix energy describing the uniformity and texture roughness of cytoplasm were salient features for the automated selection of promising oocytes that exhibited excellent developmental potential.

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Grading of mammalian cumulus oocyte complexes using machine learning for in vitro embryo culture

Cited by 10 publications

References 8 publications

Brilliant cresyl blue enhanced optoacoustic imaging enables non-destructive imaging of mammalian ovarian follicles for artificial reproduction

Brilliant cresyl blue enhanced optoacoustic imaging enables non-destructive imaging of mammalian ovarian follicles for artificial reproduction

Automatic evaluation of human oocyte developmental potential from microscopy images

Identification of significant imaging features for sensing oocyte viability

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