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

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. ```本数据集已上传本团队发表论文中用于预测青鳉类器官（medaka organoids）组织发育的源图像，相关论文详见：https://www.biorxiv.org/content/10.1101/2025.02.19.639061v1。 所有图像均采用本团队自定义的数据集格式存储。如需读取该数据集，请先克隆对应代码仓库：https://github.com/TarikExner/orgAInoid。 数据集导入示例代码如下： python from orgAInoid.classification import OrganoidDataset data = OrganoidDataset.read_classification_dataset("./path/to/file.cds") // 可通过.metadata属性访问数据集元数据，该元数据表中存储了对应图像数组的索引信息。 data.metadata // 图像数组存储于.X属性，标签数组存储于.y[readout]属性，其中readout的可选取值为"RPE_Final"、"Lens_Final"、"RPE_classes"及"Lens_classes"。 更多详细信息请参阅该GitHub代码仓库。