Published February 23, 2025 | Version 1.0.0
Dataset Open

A deep learning-based computational pipeline predicts developmental outcome in retinal organoids: classification images [maximum 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 (24.3 GB)

Name Size
md5:8facc28498da8ad6ec6413e5994d98a6
2.3 GB Download
md5:5ac4473ce54ca41b0082abb424637a7c
763.1 MB Download
md5:ffea36eb8ddfda2f102eef7c4c7232e3
2.8 GB Download
md5:3b77264cce85ae4cb795cd9af2ac2384
2.7 GB Download
md5:f1b27c8bf7405eb904705cc45e56157e
2.7 GB Download
md5:5dce245af56e067a987e3f06c7ba1d4d
724.7 MB Download
md5:d3a41e44e7c3a1c4af769eda312c0761
2.6 GB Download
md5:57c09706af604c98b74c3dded6d78bd7
2.6 GB Download
md5:66c7a218e3a23d84ca83f0570a3f37c0
2.7 GB Download
md5:4d393c8ff93187cb1f63b9ef8a52c212
2.6 GB Download
md5:437838380808446cc7c65d2e9b2890b3
1.8 GB Download

Additional details

Dates

Submitted
2025-02-23
bioRxiv preprint

Software

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