Welcome to the OrganoIDNetData repository. This public dataset is a significant step forward in cancer research, particularly in the study of Pancreatic Ductal Adenocarcinoma (PDAC). It comprises phase-contrast images of murine and patient-derived tumor organoids co-cultured with immune cells. With 190 images and 33,906 organoids, OrganoIDNetData serves as a potential benchmark for organoid segmentation models in oncological research. The publication based on this work can be found here.
- Type of Cancer: Pancreatic Ductal Adenocarcinoma
- Images: 190 phase-contrast images
- Organoids Count: 33,906
- Culturing: Co-cultured with immune cells
- Focus: Tumor organoids
The primary objective of OrganoIDNetData is to address the challenges in organoid research, particularly:
- Efficient and reliable segmentation of organoid images
- Quantification of organoid growth, regression, and response to treatments
- Prediction of organoid system behaviors
This dataset is intended for use in developing and testing algorithms for:
- Object detection and segmentation in organoid images
- Machine learning models in oncology research
- Benchmarking against other organoid segmentation models
We welcome contributions to OrganoIDNetData! If you have suggestions or improvements, please fork the repository and submit a pull request.