Published February 23, 2025 | Version 1.0.0
Dataset Open

A deep learning-based computational pipeline predicts developmental outcome in retinal organoids: classification images [sum projections]

Authors/Creators

  • 1. EDMO icon University of Heidelberg
  • 2. ROR icon Boston Children's Hospital
  • 3. ROR icon Broad Institute

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:

```python

from orgAInoid.classification import OrganoidDataset

data = 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.

```

 

Files

Files (24.2 GB)

Name Size
md5:e2a3bc46c4f9de317abe7cffb523ef79
2.3 GB Download
md5:263340cbb5a334acf14b31f07d0a396d
765.3 MB Download
md5:835568a042b83bab9a42d350d524f3c5
2.8 GB Download
md5:b79700a1f400d06f119b38639255acca
2.7 GB Download
md5:585192b976827283b99c1f5fac718b47
2.7 GB Download
md5:25c4b79f1067d4a0ef31c82c41227ab5
720.3 MB Download
md5:fea254dfe6e97c621a4c3cf5f0a3bc52
2.6 GB Download
md5:198f5de7b0866996cf893c84bdd7da34
2.6 GB Download
md5:e261488f24c4648397b238ead8cb321f
2.7 GB Download
md5:f7c4bf6df4138d02fbe059dee3865732
2.6 GB Download
md5:0be1843b988a487f315aa479cea51f5e
1.8 GB Download

Additional details

Dates

Submitted
2025-02-23
bioRxiv preprint

Software

Repository URL
https://github.com/TarikExner/orgAInoid/