A robust deep learning-based multiclass segmentation method for analyzing human metaphase II oocyte images

Firuzinia, Sedighe; Afzali, Seyed Mahmoodreza; Ghasemian, Fatemeh; Mirroshandel, Seyed Abolghasem

doi:10.1016/j.cmpb.2021.105946

Cited by 21 publications

(11 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We tested several configurations around this architecture (number of layers, activation function, variation of the architecture (Falk et al, 2019; Gadosey et al, 2020; Ibtehaz and Rahman, 2020)) and opted for a classical U-Net architecture, shown in Figure S2A. Note that we could have trained a single network to segment at the same time the zona pellucida and the cortex, which can slightly improve the performance of the network (Firuzinia et al, 2021). However, we preferred to keep two independent networks for more flexibility.…”

Section: Methodsmentioning

confidence: 99%

An interpretable and versatile machine learning approach for oocyte phenotyping

Letort

Eichmuller

Silva

et al. 2022

Preprint

View full text Add to dashboard Cite

Meiotic maturation is a crucial step of oocyte development allowing its potential fertilization and embryo development. Elucidating this process is important both for fundamental research and assisted reproductive technology. However, only few computational tools, based on non-invasive measurements, are currently available to characterize oocyte meiotic maturation. Here, we develop a computational framework to phenotype oocytes based on images or movies acquired exclusively in transmitted light. We first trained neural networks to segment the contours of oocytes and their zona pellucida using a diverse cohort of both mouse and human oocytes. We then defined a comprehensive set of morphological features to describe a single oocyte. We have implemented these steps in a versatile and user-friendly open source Fiji plugin available to the mouse and human oocyte community. Then, we present a machine learning pipeline based on selected features to automatically recognize oocyte populations and determine their morphological differences. Its first application is a novel approach to screen oocyte strains and automatically identify their morphological characteristics.We demonstrate its potential by phenotyping a well characterized genetically modified mouse oocyte strain. Its second application is to predict and characterize the maturation potential of oocytes. Importantly, we identify two new features to assess mouse oocyte maturation potential, consisting inthe texture of the zona pellucida and the cytoplasmic particles size. Eventually, we tested whether these mouse oocyte quality features were applicable to human oocyte's developmental potential.

show abstract

Section: Methodsmentioning

confidence: 99%

An interpretable and versatile machine learning approach for oocyte phenotyping

Letort

Eichmuller

Silva

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…A typical automatic smear analysis system comprises the following five stages: image acquisition, preprocessing, segmentation, feature extraction, and classification ( 43 ). AI technology is applied in the segmentation and classification stages for the automatic analysis of a smear, which is helpful to improve screening efficiency.…”

Section: Applications Of Ai In Early Screening Of Cervical Cancermentioning

confidence: 99%

Section: Applications Of Ai In Early Screening Of Cervical Cancermentioning

confidence: 99%

“…As the diagnostic criteria for cervical cytology are mainly based on nuclear and cytoplasm abnormalities, accurate segmentation is a prerequisite for screening solutions (44)(45)(46). Studies on the application of AI in cell segmentation have been carried out continuously and have shown good results in the segmentation of hepatoma cells (47), human metaphase II oocytes (43), and pluripotent stem cells (48). It has also been introduced for the automatic segmentation of cervical cells, and good results have been reported (49).…”

Section: Segmentation Of Cervical Cellsmentioning

confidence: 99%

“…Six different convolutional neural networks (CNNs) were used for the first time for the diagnosis of cervical precancerous lesions. The accuracy, sensitivity, and specificity of the integrated classifier were 0.989, 0.978, and 0.979, respectively (43). Shi et al proposed a method of cervical cell classification based on a graph convolution network to explore the potential relationship between cervical cell images and improvement of classification performance.…”

Section: Classification Of Cervical Cellsmentioning

confidence: 99%

See 2 more Smart Citations

Artificial Intelligence in Cervical Cancer Screening and Diagnosis

Hou

Shen

Zhou³

et al. 2022

Front. Oncol.

View full text Add to dashboard Cite

Cervical cancer remains a leading cause of cancer death in women, seriously threatening their physical and mental health. It is an easily preventable cancer with early screening and diagnosis. Although technical advancements have significantly improved the early diagnosis of cervical cancer, accurate diagnosis remains difficult owing to various factors. In recent years, artificial intelligence (AI)-based medical diagnostic applications have been on the rise and have excellent applicability in the screening and diagnosis of cervical cancer. Their benefits include reduced time consumption, reduced need for professional and technical personnel, and no bias owing to subjective factors. We, thus, aimed to discuss how AI can be used in cervical cancer screening and diagnosis, particularly to improve the accuracy of early diagnosis. The application and challenges of using AI in the diagnosis and treatment of cervical cancer are also discussed.

show abstract

Quality of IVM ovarian tissue oocytes: impact of clinical, demographic, and laboratory factors

Kashutina,

Obosyan,

Bunyaeva

et al. 2024

J Assist Reprod Genet

View full text Add to dashboard Cite

Purpose To determine how clinical, demographic, and laboratory characteristics influence ovarian tissue oocyte quality. Methods Immature cumulus-oocyte complexes were isolated from removed ovaries and cultured for 48–52 h in either monophasic standard or biphasic CAPA media for fertility preservation. A total of 355 MII oocytes from 53 patients were described for intracytoplasmic and extracytoplasmic anomalies. Multiple clinical, laboratory, and demographic characteristics were analyzed. Statistically significant differences between independent groups in qualitative variables were identified using Pearson’s χ2 and Fisher’s exact tests. The diagnostic value of quantitative variables was assessed using the ROC curve analysis. Factors associated with the development of dysmorphism, taking patient age into account, were identified using the binary logistic regression analysis. Results Dysmorphisms were observed in 245 oocytes (69.0%), with a median number of dysmorphisms of 2. Oocyte dysmorphisms were found to be 2.211 times more likely to be detected in patients with ovarian cancer, while the presence of dark-colored cytoplasm was associated with gynecologic surgery in the anamnesis (p = 0.002; OR 16.652; 95% CI, 1.977–140.237; Cramer’s V 0.187). Small polar bodies developed 2.717 times more often (95% CI, 1.195–6.18) in patients older than 35. In the case of ovarian transportation on ice at 4 ℃, the chances of development of cytoplasmic granularity increased 2.569 times (95% CI, 1.301–5.179). The use of biphasic CAPA IVM media contributed to a decrease in the probability of large polar body formation (p = 0.034) compared to the standard monophasic IVM media. Conclusions Both patients’ characteristics and laboratory parameters have an impact on the quality of IVM ovarian tissue oocytes.

show abstract

A robust deep learning-based multiclass segmentation method for analyzing human metaphase II oocyte images

Cited by 21 publications

References 18 publications

An interpretable and versatile machine learning approach for oocyte phenotyping

An interpretable and versatile machine learning approach for oocyte phenotyping

Artificial Intelligence in Cervical Cancer Screening and Diagnosis

Quality of IVM ovarian tissue oocytes: impact of clinical, demographic, and laboratory factors

Contact Info

Product

Resources

About