OoCount: A Machine-Learning Based Approach to Mouse Ovarian Follicle Counting and Classification Article Swipe
Lillian Folts
,
Anthony S. Martinez
,
Corey Bunce
,
Blanche Capel
,
Jennifer McKey
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.05.13.593993
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.05.13.593993
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 tissue-clearing and spinning disk confocal-based imaging protocol to obtain 3D images of whole mount perinatal and adult mouse ovaries. Fluorescently labeled oocytes from 3D images of ovaries were manually annotated in Napari to develop a machine learning training dataset. This dataset was used to retrain StarDist using a CNN within DL4MicEverywhere to automatically label all oocytes in the ovary. In a second phase, we utilize Accelerated Pixel and Object Classification, a Napari plugin, to classify labeled oocytes and sort them into growth stages. Here, we provide an end-to-end protocol for producing high-quality 3D images of the perinatal and adult mouse ovary, obtaining follicle counts and staging. We also demonstrate how to customize OoCount to fit images produced in any lab. 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. Graphical Abstract Summary sentence This protocol introduces OoCount, a high-throughput, open-source method for automatic oocyte segmentation and classification from fluorescent 3D microscopy images of whole mouse ovaries using a machine learning-based approach.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.05.13.593993
- https://www.biorxiv.org/content/biorxiv/early/2024/05/14/2024.05.13.593993.full.pdf
- OA Status
- green
- Cited By
- 2
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396936343
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4396936343Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2024.05.13.593993Digital Object Identifier
- Title
-
OoCount: A Machine-Learning Based Approach to Mouse Ovarian Follicle Counting and ClassificationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-14Full publication date if available
- Authors
-
Lillian Folts, Anthony S. Martinez, Corey Bunce, Blanche Capel, Jennifer McKeyList of authors in order
- Landing page
-
https://doi.org/10.1101/2024.05.13.593993Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2024/05/14/2024.05.13.593993.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2024/05/14/2024.05.13.593993.full.pdfDirect OA link when available
- Concepts
-
Artificial intelligence, Computer science, Convolutional neural network, Deep learning, Ovary, Pattern recognition (psychology), Biology, EndocrinologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2Per-year citation counts (last 5 years)
- References (count)
-
43Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4396936343 |
|---|---|
| doi | https://doi.org/10.1101/2024.05.13.593993 |
| ids.doi | https://doi.org/10.1101/2024.05.13.593993 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/38798456 |
| ids.openalex | https://openalex.org/W4396936343 |
| fwci | 2.69153226 |
| type | preprint |
| title | OoCount: A Machine-Learning Based Approach to Mouse Ovarian Follicle Counting and Classification |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10364 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.9973000288009644 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2739 |
| topics[0].subfield.display_name | Public Health, Environmental and Occupational Health |
| topics[0].display_name | Reproductive Biology and Fertility |
| topics[1].id | https://openalex.org/T11970 |
| topics[1].field.id | https://openalex.org/fields/13 |
| topics[1].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[1].score | 0.9492999911308289 |
| topics[1].domain.id | https://openalex.org/domains/1 |
| topics[1].domain.display_name | Life Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1312 |
| topics[1].subfield.display_name | Molecular Biology |
| topics[1].display_name | Molecular Biology Techniques and Applications |
| topics[2].id | https://openalex.org/T11324 |
| topics[2].field.id | https://openalex.org/fields/13 |
| topics[2].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[2].score | 0.9465000033378601 |
| topics[2].domain.id | https://openalex.org/domains/1 |
| topics[2].domain.display_name | Life Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1304 |
| topics[2].subfield.display_name | Biophysics |
| topics[2].display_name | Spectroscopy Techniques in Biomedical and Chemical Research |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C154945302 |
| concepts[0].level | 1 |
| concepts[0].score | 0.6630023121833801 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[0].display_name | Artificial intelligence |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.5989471077919006 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C81363708 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5668796896934509 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[2].display_name | Convolutional neural network |
| concepts[3].id | https://openalex.org/C108583219 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5250349044799805 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[3].display_name | Deep learning |
| concepts[4].id | https://openalex.org/C2778324911 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5068479180335999 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q9631 |
| concepts[4].display_name | Ovary |
| concepts[5].id | https://openalex.org/C153180895 |
| concepts[5].level | 2 |
| concepts[5].score | 0.41477152705192566 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[5].display_name | Pattern recognition (psychology) |
| concepts[6].id | https://openalex.org/C86803240 |
| concepts[6].level | 0 |
| concepts[6].score | 0.3085368275642395 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[6].display_name | Biology |
| concepts[7].id | https://openalex.org/C134018914 |
| concepts[7].level | 1 |
| concepts[7].score | 0.0 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q162606 |
| concepts[7].display_name | Endocrinology |
| keywords[0].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[0].score | 0.6630023121833801 |
| keywords[0].display_name | Artificial intelligence |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.5989471077919006 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[2].score | 0.5668796896934509 |
| keywords[2].display_name | Convolutional neural network |
| keywords[3].id | https://openalex.org/keywords/deep-learning |
| keywords[3].score | 0.5250349044799805 |
| keywords[3].display_name | Deep learning |
| keywords[4].id | https://openalex.org/keywords/ovary |
| keywords[4].score | 0.5068479180335999 |
| keywords[4].display_name | Ovary |
| keywords[5].id | https://openalex.org/keywords/pattern-recognition |
| keywords[5].score | 0.41477152705192566 |
| keywords[5].display_name | Pattern recognition (psychology) |
| keywords[6].id | https://openalex.org/keywords/biology |
| keywords[6].score | 0.3085368275642395 |
| keywords[6].display_name | Biology |
| language | en |
| locations[0].id | doi:10.1101/2024.05.13.593993 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306402567 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| locations[0].source.host_organization | https://openalex.org/I2750212522 |
| locations[0].source.host_organization_name | Cold Spring Harbor Laboratory |
| locations[0].source.host_organization_lineage | https://openalex.org/I2750212522 |
| locations[0].license | cc-by-nc-nd |
| locations[0].pdf_url | https://www.biorxiv.org/content/biorxiv/early/2024/05/14/2024.05.13.593993.full.pdf |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.1101/2024.05.13.593993 |
| locations[1].id | pmid:38798456 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | bioRxiv : the preprint server for biology |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/38798456 |
| locations[2].id | pmh:oai:pubmedcentral.nih.gov:11118501 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S2764455111 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | PubMed Central |
| locations[2].source.host_organization | https://openalex.org/I1299303238 |
| locations[2].source.host_organization_name | National Institutes of Health |
| locations[2].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | bioRxiv |
| locations[2].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/11118501 |
| indexed_in | crossref, pubmed |
| authorships[0].author.id | https://openalex.org/A5088463984 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-5832-9410 |
| authorships[0].author.display_name | Lillian Folts |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I51713134 |
| authorships[0].affiliations[0].raw_affiliation_string | University of Colorado Anschutz Medical Campus |
| authorships[0].institutions[0].id | https://openalex.org/I51713134 |
| authorships[0].institutions[0].ror | https://ror.org/03wmf1y16 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I51713134 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | University of Colorado Anschutz Medical Campus |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Lillian Folts |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | University of Colorado Anschutz Medical Campus |
| authorships[1].author.id | https://openalex.org/A5102010056 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Anthony S. Martinez |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I51713134 |
| authorships[1].affiliations[0].raw_affiliation_string | University of Colorado Anschutz Medical Campus |
| authorships[1].institutions[0].id | https://openalex.org/I51713134 |
| authorships[1].institutions[0].ror | https://ror.org/03wmf1y16 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I51713134 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | University of Colorado Anschutz Medical Campus |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Anthony S. Martinez |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | University of Colorado Anschutz Medical Campus |
| authorships[2].author.id | https://openalex.org/A5085588789 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-1348-8135 |
| authorships[2].author.display_name | Corey Bunce |
| authorships[2].countries | AT |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I1632182 |
| authorships[2].affiliations[0].raw_affiliation_string | Konrad Lorenz Institute for Evolution and Cognition Research |
| authorships[2].institutions[0].id | https://openalex.org/I1632182 |
| authorships[2].institutions[0].ror | https://ror.org/01agk4b09 |
| authorships[2].institutions[0].type | other |
| authorships[2].institutions[0].lineage | https://openalex.org/I1632182 |
| authorships[2].institutions[0].country_code | AT |
| authorships[2].institutions[0].display_name | Konrad Lorenz Institute for Evolution and Cognition Research |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Corey Bunce |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Konrad Lorenz Institute for Evolution and Cognition Research |
| authorships[3].author.id | https://openalex.org/A5064061881 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-6587-0969 |
| authorships[3].author.display_name | Blanche Capel |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I4210126298 |
| authorships[3].affiliations[0].raw_affiliation_string | Duke University Medical Center |
| authorships[3].institutions[0].id | https://openalex.org/I4210126298 |
| authorships[3].institutions[0].ror | https://ror.org/03njmea73 |
| authorships[3].institutions[0].type | healthcare |
| authorships[3].institutions[0].lineage | https://openalex.org/I4210126298, https://openalex.org/I4210144876 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | Duke Medical Center |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Blanche Capel |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Duke University Medical Center |
| authorships[4].author.id | https://openalex.org/A5058065376 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-2640-1502 |
| authorships[4].author.display_name | Jennifer McKey |
| authorships[4].countries | US |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I51713134 |
| authorships[4].affiliations[0].raw_affiliation_string | University of Colorado Anschutz Medical Campus |
| authorships[4].institutions[0].id | https://openalex.org/I51713134 |
| authorships[4].institutions[0].ror | https://ror.org/03wmf1y16 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I51713134 |
| authorships[4].institutions[0].country_code | US |
| authorships[4].institutions[0].display_name | University of Colorado Anschutz Medical Campus |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Jennifer McKey |
| authorships[4].is_corresponding | True |
| authorships[4].raw_affiliation_strings | University of Colorado Anschutz Medical Campus |
| has_content.pdf | True |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.biorxiv.org/content/biorxiv/early/2024/05/14/2024.05.13.593993.full.pdf |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | OoCount: A Machine-Learning Based Approach to Mouse Ovarian Follicle Counting and Classification |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10364 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.9973000288009644 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2739 |
| primary_topic.subfield.display_name | Public Health, Environmental and Occupational Health |
| primary_topic.display_name | Reproductive Biology and Fertility |
| related_works | https://openalex.org/W2731899572, https://openalex.org/W4226493464, https://openalex.org/W3215138031, https://openalex.org/W3133861977, https://openalex.org/W3009238340, https://openalex.org/W4360585206, https://openalex.org/W4321369474, https://openalex.org/W4285208911, https://openalex.org/W2951211570, https://openalex.org/W3103566983 |
| cited_by_count | 2 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| locations_count | 3 |
| best_oa_location.id | doi:10.1101/2024.05.13.593993 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306402567 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| best_oa_location.source.host_organization | https://openalex.org/I2750212522 |
| best_oa_location.source.host_organization_name | Cold Spring Harbor Laboratory |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I2750212522 |
| best_oa_location.license | cc-by-nc-nd |
| best_oa_location.pdf_url | https://www.biorxiv.org/content/biorxiv/early/2024/05/14/2024.05.13.593993.full.pdf |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.1101/2024.05.13.593993 |
| primary_location.id | doi:10.1101/2024.05.13.593993 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306402567 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| primary_location.source.host_organization | https://openalex.org/I2750212522 |
| primary_location.source.host_organization_name | Cold Spring Harbor Laboratory |
| primary_location.source.host_organization_lineage | https://openalex.org/I2750212522 |
| primary_location.license | cc-by-nc-nd |
| primary_location.pdf_url | https://www.biorxiv.org/content/biorxiv/early/2024/05/14/2024.05.13.593993.full.pdf |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.1101/2024.05.13.593993 |
| publication_date | 2024-05-14 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W4234792992, https://openalex.org/W2085679334, https://openalex.org/W3089539977, https://openalex.org/W2224205296, https://openalex.org/W2161437953, https://openalex.org/W1890466331, https://openalex.org/W4297249504, https://openalex.org/W1979660812, https://openalex.org/W2237612852, https://openalex.org/W3186370494, https://openalex.org/W4384662954, https://openalex.org/W3197163957, https://openalex.org/W2069232438, https://openalex.org/W2600753645, https://openalex.org/W3001950540, https://openalex.org/W4361004185, https://openalex.org/W4390444718, https://openalex.org/W2963607229, https://openalex.org/W3198842780, https://openalex.org/W2946901414, https://openalex.org/W4388814182, https://openalex.org/W3156859314, https://openalex.org/W3111521801, https://openalex.org/W2806530968, https://openalex.org/W4285387962, https://openalex.org/W3021714323, https://openalex.org/W6609187118, https://openalex.org/W4206192747, https://openalex.org/W4296685787, https://openalex.org/W3160984047, https://openalex.org/W2773012129, https://openalex.org/W2051624773, https://openalex.org/W2916274480, https://openalex.org/W2167279371, https://openalex.org/W4399499779, https://openalex.org/W2296335794, https://openalex.org/W4314931592, https://openalex.org/W3125298061, https://openalex.org/W2892989873, https://openalex.org/W1974231851, https://openalex.org/W4213440591, https://openalex.org/W4393215761, https://openalex.org/W3099064910 |
| referenced_works_count | 43 |
| abstract_inverted_index.a | 13, 98, 118, 128, 164, 177, 190, 200, 286, 306 |
| abstract_inverted_index.3D | 110, 139, 153, 222, 298 |
| abstract_inverted_index.In | 78, 189 |
| abstract_inverted_index.We | 91, 126, 236 |
| abstract_inverted_index.an | 216 |
| abstract_inverted_index.in | 8, 29, 160, 186, 247, 259, 263 |
| abstract_inverted_index.is | 54 |
| abstract_inverted_index.of | 5, 16, 27, 43, 49, 61, 70, 113, 141, 155, 224, 257, 301 |
| abstract_inverted_index.to | 34, 74, 89, 95, 137, 162, 173, 181, 203, 240, 243, 272 |
| abstract_inverted_index.we | 193, 214, 252 |
| abstract_inverted_index.CNN | 178 |
| abstract_inverted_index.The | 1, 59 |
| abstract_inverted_index.all | 184 |
| abstract_inverted_index.and | 3, 19, 46, 57, 87, 106, 131, 145, 197, 207, 227, 234, 266, 276, 294 |
| abstract_inverted_index.any | 248 |
| abstract_inverted_index.can | 253 |
| abstract_inverted_index.fit | 244 |
| abstract_inverted_index.for | 23, 66, 102, 219, 290 |
| abstract_inverted_index.has | 31, 64 |
| abstract_inverted_index.how | 239 |
| abstract_inverted_index.our | 270 |
| abstract_inverted_index.the | 32, 44, 67, 187, 225, 264 |
| abstract_inverted_index.was | 171 |
| abstract_inverted_index.(3D) | 25 |
| abstract_inverted_index.This | 169, 282 |
| abstract_inverted_index.also | 237 |
| abstract_inverted_index.disk | 133 |
| abstract_inverted_index.each | 9, 260 |
| abstract_inverted_index.fast | 129 |
| abstract_inverted_index.from | 108, 152, 296 |
| abstract_inverted_index.have | 83 |
| abstract_inverted_index.into | 210 |
| abstract_inverted_index.lab. | 249 |
| abstract_inverted_index.more | 85 |
| abstract_inverted_index.size | 45 |
| abstract_inverted_index.sort | 208 |
| abstract_inverted_index.them | 209 |
| abstract_inverted_index.time | 55 |
| abstract_inverted_index.toto | 30 |
| abstract_inverted_index.used | 92, 172 |
| abstract_inverted_index.were | 157 |
| abstract_inverted_index.(CNN) | 123 |
| abstract_inverted_index.Here, | 213 |
| abstract_inverted_index.Pixel | 196 |
| abstract_inverted_index.Using | 250 |
| abstract_inverted_index.adult | 146, 228, 267 |
| abstract_inverted_index.based | 124 |
| abstract_inverted_index.label | 183 |
| abstract_inverted_index.mount | 143 |
| abstract_inverted_index.mouse | 115, 147, 229, 303 |
| abstract_inverted_index.rapid | 68 |
| abstract_inverted_index.stage | 11, 262 |
| abstract_inverted_index.study | 273 |
| abstract_inverted_index.these | 50, 81, 93 |
| abstract_inverted_index.tools | 94 |
| abstract_inverted_index.using | 117, 176, 305 |
| abstract_inverted_index.whole | 114, 142, 302 |
| abstract_inverted_index.Napari | 161, 201 |
| abstract_inverted_index.Object | 198 |
| abstract_inverted_index.advent | 60 |
| abstract_inverted_index.become | 84 |
| abstract_inverted_index.counts | 233, 256 |
| abstract_inverted_index.create | 96 |
| abstract_inverted_index.growth | 10, 211, 261 |
| abstract_inverted_index.health | 18 |
| abstract_inverted_index.images | 112, 140, 154, 223, 245, 300 |
| abstract_inverted_index.method | 101, 289 |
| abstract_inverted_index.nature | 48 |
| abstract_inverted_index.neural | 121 |
| abstract_inverted_index.number | 2 |
| abstract_inverted_index.obtain | 138, 254 |
| abstract_inverted_index.oocyte | 104, 292 |
| abstract_inverted_index.ovary, | 230, 268 |
| abstract_inverted_index.ovary. | 188 |
| abstract_inverted_index.phase, | 192 |
| abstract_inverted_index.recent | 79 |
| abstract_inverted_index.second | 191 |
| abstract_inverted_index.total, | 36 |
| abstract_inverted_index.within | 179 |
| abstract_inverted_index.years, | 80 |
| abstract_inverted_index.OoCount | 242 |
| abstract_inverted_index.Summary | 280 |
| abstract_inverted_index.ability | 271 |
| abstract_inverted_index.allowed | 65 |
| abstract_inverted_index.analyze | 75 |
| abstract_inverted_index.because | 42 |
| abstract_inverted_index.counts. | 40 |
| abstract_inverted_index.dataset | 170 |
| abstract_inverted_index.develop | 163 |
| abstract_inverted_index.images, | 51 |
| abstract_inverted_index.images. | 77 |
| abstract_inverted_index.imaging | 26, 135 |
| abstract_inverted_index.labeled | 150, 205 |
| abstract_inverted_index.machine | 165, 307 |
| abstract_inverted_index.methods | 73 |
| abstract_inverted_index.network | 122 |
| abstract_inverted_index.oocytes | 53, 151, 185, 206, 258 |
| abstract_inverted_index.ovarian | 6, 17, 38, 274 |
| abstract_inverted_index.ovaries | 28, 116, 156, 304 |
| abstract_inverted_index.plugin, | 202 |
| abstract_inverted_index.provide | 215 |
| abstract_inverted_index.readout | 15 |
| abstract_inverted_index.retrain | 174 |
| abstract_inverted_index.stages. | 212 |
| abstract_inverted_index.uncover | 35 |
| abstract_inverted_index.utilize | 194 |
| abstract_inverted_index.Abstract | 0, 279 |
| abstract_inverted_index.However, | 41 |
| abstract_inverted_index.OoCount, | 97, 251, 285 |
| abstract_inverted_index.StarDist | 175 |
| abstract_inverted_index.accurate | 37, 255 |
| abstract_inverted_index.classify | 204 |
| abstract_inverted_index.counting | 52 |
| abstract_inverted_index.dataset. | 168 |
| abstract_inverted_index.follicle | 39, 232 |
| abstract_inverted_index.function | 275 |
| abstract_inverted_index.holistic | 47 |
| abstract_inverted_index.learning | 166 |
| abstract_inverted_index.manually | 158 |
| abstract_inverted_index.ovaries. | 148 |
| abstract_inverted_index.produced | 246 |
| abstract_inverted_index.protocol | 136, 218, 283 |
| abstract_inverted_index.provides | 12 |
| abstract_inverted_index.reliable | 14 |
| abstract_inverted_index.sentence | 281 |
| abstract_inverted_index.spinning | 132 |
| abstract_inverted_index.staging. | 235 |
| abstract_inverted_index.training | 167 |
| abstract_inverted_index.Graphical | 278 |
| abstract_inverted_index.annotated | 159 |
| abstract_inverted_index.approach. | 125, 309 |
| abstract_inverted_index.automated | 72 |
| abstract_inverted_index.automatic | 103, 291 |
| abstract_inverted_index.consuming | 56 |
| abstract_inverted_index.customize | 241 |
| abstract_inverted_index.developed | 127 |
| abstract_inverted_index.follicles | 7 |
| abstract_inverted_index.function. | 20 |
| abstract_inverted_index.improving | 269 |
| abstract_inverted_index.obtaining | 231 |
| abstract_inverted_index.perinatal | 144, 226, 265 |
| abstract_inverted_index.pipelines | 82 |
| abstract_inverted_index.potential | 33 |
| abstract_inverted_index.producing | 220 |
| abstract_inverted_index.Leveraging | 21 |
| abstract_inverted_index.accessible | 88 |
| abstract_inverted_index.algorithms | 63 |
| abstract_inverted_index.difficult. | 58 |
| abstract_inverted_index.end-to-end | 217 |
| abstract_inverted_index.fertility. | 277 |
| abstract_inverted_index.introduces | 284 |
| abstract_inverted_index.microscopy | 76, 111, 299 |
| abstract_inverted_index.techniques | 22 |
| abstract_inverted_index.Accelerated | 195 |
| abstract_inverted_index.demonstrate | 238 |
| abstract_inverted_index.development | 69 |
| abstract_inverted_index.fluorescent | 109, 297 |
| abstract_inverted_index.open-source | 100, 288 |
| abstract_inverted_index.ultra-fast, | 71 |
| abstract_inverted_index.distribution | 4 |
| abstract_inverted_index.high-quality | 221 |
| abstract_inverted_index.segmentation | 105, 293 |
| abstract_inverted_index.Fluorescently | 149 |
| abstract_inverted_index.automatically | 182 |
| abstract_inverted_index.convolutional | 120 |
| abstract_inverted_index.deep-learning | 62, 119 |
| abstract_inverted_index.user-friendly | 86 |
| abstract_inverted_index.classification | 107, 295 |
| abstract_inverted_index.confocal-based | 134 |
| abstract_inverted_index.learning-based | 308 |
| abstract_inverted_index.Classification, | 199 |
| abstract_inverted_index.tissue-clearing | 130 |
| abstract_inverted_index.DL4MicEverywhere | 180 |
| abstract_inverted_index.high-throughput, | 99, 287 |
| abstract_inverted_index.non-specialists. | 90 |
| abstract_inverted_index.three-dimensional | 24 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| corresponding_author_ids | https://openalex.org/A5058065376 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I51713134 |
| citation_normalized_percentile.value | 0.80791705 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |