Published March 6, 2025
| Version v3
Model
Open
StarDist2D Model for Nuclei Segmentation (Synthetic sir-DNA Images)
- 1. Universidad Carlos III de Madrid Escuela Politécnica Superior
Description
This StarDist2D model segments nuclei from synthetic sir-DNA images, which were originally generated from Lifeact-RFP images using the Pix2Pix model. It forms the second step in a pipeline for image-to-image translation and segmentation.
This model is part of a use case from the paper:
Fuster-Barceló et al., 2024 – Bridging the gap: Integrating cutting-edge techniques into biological imaging with deepImageJ (Biological Imaging, 4, e14. doi:10.1017/S2633903X24000114).
It was fine-tuned using the ZeroCostDL4Mic notebook with a dataset from Zenodo and follows the BioImage Model Zoo format (bioimage.io) for compatibility with DeepImageJ.
- Fine-Tuning Notebook:StarDist Notebook
- Training Dataset: Zenodo Dataset
📌 For DeepImageJ users: Download the SyntheticsirDNA-StarDist2D.zip file for direct use, as Zenodo’s automatic zipping may cause issues.
📌 For DeepImageJ users: Download the SyntheticsirDNA-StarDist2D.zip file for direct use, as Zenodo’s automatic zipping may cause issues.
Files
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Additional details
Dates
- Available
-
2023-12-21
- Updated
-
2025-03-06
Software
- Repository URL
- https://github.com/deepimagej/case-studies
References
- Schmidt, U., Weigert, M., Broaddus, C., & Myers, G. (2018). Cell detection with star-convex polygons. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part II 11 (pp. 265-273). Springer International Publishing.
- von Chamier, L., Laine, R. F., Jukkala, J., Spahn, C., Krentzel, D., Nehme, E., ... & Henriques, R. (2021). Democratising deep learning for microscopy with ZeroCostDL4Mic. Nature communications, 12(1), 2276.
- Fuster-Barceló, C., García-López-de-Haro, C., Gómez-de-Mariscal, E., Ouyang, W., Olivo-Marin, J.-C., Sage, D., & Muñoz-Barrutia, A. (2024). Bridging the gap: Integrating cutting-edge techniques into biological imaging with deepImageJ. Biological Imaging, 4, e14. doi:10.1017/S2633903X24000114