Version 1.0.0

orgAInoid classification images [maximum projections]

Afting, Cassian;Bhatti, Norin;Schlagheck, Christina;Sánchez Salvador, Encarnación;Herrera, María Laura;Agarwal, Rashi;Suzuki, Risa;Hackert, Nicolaj Sebastian;Lorenz, Hanns-Martin;Zilova, Lucie;Wittbrodt, Joachim;Exner, Tarik

Description

Deposited are the source images as used in our publication to predict the tissue development of medaka organoids (https://www.biorxiv.org/content/10.1101/2025.02.19.639061v1). The images are stored in our custom dataset format. In order to read it, you will need to clone the repo: https://github.com/TarikExner/orgAInoid The code to import is the following:pythonfrom orgAInoid.classification import OrganoidDatasetdata = OrganoidDataset.read_classification_dataset("./path/to/file.cds")// metadata are accessed via the .metadata attribute. The respective image array index is stored within this table.data.metadata//image arrays are stored at the .X and .y[readout] attribute, where attribute is one of "RPE_Final", "Lens_Final", "RPE_classes" and "Lens_classes" For further information refer to the github repository.

Citations (0)

Mentions (0)

Metrics

Dataset Index

0.5

FAIR Score

79%

Citations

0

Mentions

0

Metrics Over Time

Publication Details

DOI

Publisher

Zenodo

License

Creative Commons Attribution 4.0 International

Copyright (C) The Authors 2025

Assigned Domain

Subfield

Artificial Intelligence

Field

Computer Science

Domain

Physical Sciences

Confidence Score

64%

Source

Open Alex

Keywords

retinal organoidsretinal pigmented epitheliumlensWntdeep learningmachine learningartificial intelligence

Normalization Factors

FT

57.69

CTw

1.00

MTw

1.00