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10 results for “follicle counting”
Follicle counting in mouse ovarian images
<div>Our dataset is composed of 643 cut slices of ovaries coming from 92 mice (each mouse have between 6 and 8 cuts). Ovaries were fixed in Bouin’s solution and subsequently embedded in paraffin blocks. The entire ovaries were serially sectioned into 4μm slices using a microtome. Every fifth section was mounted on microscope slides and stained with Hematoxylin and Eosin. Ovaries have been digitized on a Panoramic 250 Flash, Slide Scanner (3DHISTECH Ltd. HUNGARY). Each section was analyzed using Calopix Viewer®. Follicles were classified according to Pedersen’s classification. Briefly, follicles were categorized as follows: primordial follicles contained an oocyte surrounded by a partial or complete layer of squamous granulosa cells; primary follicles had an oocyte sur- rounded by a single layer of cuboidal granulosa cells; and secondary follicles presented with at least two layers of granulosa cells. The size of those cuts is approximately 20000 × 20000</div>
Data from: OoCount: A machine-learning based approach to mouse ovarian follicle counting and classification
<p>The number and distribution of ovarian follicles in each growth stage provides a reliable readout of ovarian health and function. Leveraging techniques for three-dimensional (3D) imaging of ovaries in toto has the potential to uncover total, accurate ovarian follicle counts. However, because of the size and holistic nature of these images, counting oocytes is time consuming and difficult. The advent of deep-learning algorithms has allowed for the rapid development of ultra-fast, automated methods to analyze microscopy images. In recent years, these pipelines have become more user-friendly and accessible to non-specialists. We used these tools to create OoCount, a high-throughput, open-source method for automatic oocyte segmentation and classification from fluorescent 3D microscopy images of whole mouse ovaries using a deep-learning convolutional neural network (CNN) based approach. We developed a fast clearing and spinning disk confocal-based imaging protocol to obtain 3D images of whole mount perinatal and adult mouse ovaries. Then, fluorescently labeled oocytes from 3D images of ovaries were manually annotated to develop a machine learning training dataset. This dataset was used to train a CNN to automatically label all oocytes in the ovary. In a second phase, we trained another CNN to classify labeled oocytes and sort them into growth stages. Using OoCount, we can obtain accurate counts of oocytes in each growth stage in the perinatal and adult ovary, improving our ability to study ovarian function and fertility. Here, we provide an end-to-end protocol for developing high quality 3D images of the perinatal and adult mouse ovary, obtaining follicle counts and stages, and how to customize OoCount to fit images produced in any lab.</p>
Data from: OoCount: A machine-learning based approach to mouse ovarian follicle counting and classification
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A New Tool for Automatic Ovarian Follicle Count and it Abilities to Evaluate Ovarian Reserve
ClinicalTrials.gov study NCT01480115. IPD Sharing: Not stated. Countries: 1. Publications: 10.
IVM Versus IVF in High Antral Follicle Count Patients
ClinicalTrials.gov study NCT03405701. IPD Sharing: NO. Countries: 1. Publications: 3.
Antimullerian Hormone Versus Antral Follicle Count for Determination of Gonadotrophin Dosing in IVF
ClinicalTrials.gov study NCT02739269. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Study of Follicle Stimulating Hormone (FSH) Receptor in Women With Low Antral Follicle Count
ClinicalTrials.gov study NCT00231504. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Anti-Mullerian Hormone (AMH) and Antral Follicle Count as Markers of Ovarian Reserve- Prospective Followup of Young Cancer Patients
ClinicalTrials.gov study NCT00930501. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Anti-Mullerian Hormone (AMH) Versus Antral Follicle Count (AFC) Guided rFSH Dosing Study
ClinicalTrials.gov study NCT01783301. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Study to Evaluate the Baseline Follicle Stimulating Hormone, Ovarian Volume and Antral Follicle Count as Prognostic Factors of the Outcome of In-vitro Fertilisation/Intracytosolic Sperm Injection in
ClinicalTrials.gov study NCT01196143. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.