MicroNucML: A deep learning approach to micronuclei detection and segmentation
收藏Zenodo2025-04-30 更新2026-05-26 收录
下载链接：
https://zenodo.org/doi/10.5281/zenodo.15312291
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资源简介：
Here we presented a benchmark dataset for micronuclei detection and segmentation. We have 1829 GFP-H2B images and 308 mCherry images with all micronuclei inside labeled. The images was collected in tif format and annotations are done using label studio. The format convert code can be found here: https://github.com/kew6688/MicroNuclei_Detection/tree/main/tools
For mnMask (GFP-H2B): The masks used for training the model are under /final_masksFor mCherry: Each image under /x_images will have a corresponding mask under /x_masks.
Dataset Format
The masks are saved in numpy arrayEach mask have format [n, W, H], n is the number of mn, each mn has a binary mask over the image.
提供机构：
Zenodo
创建时间：
2025-04-30
