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README.md

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 # mnDINO: Accurate and robust segmentation of micronuclei with vision transformer networks
-Here we present a segmentation model, mnDINO, to segment micronuclei in DNA stained images under diverse experimental conditions with very high efficiency and accuracy. To train this model, we collected a heterogeneous set of images with more than five thousand annotated micronuclei. Trained with this diverse resource, the mnDINO model improves the accuracy of MN segmentation, and exhibits strong generalization across microscopes and cell lines. The dataset, code, and pre-trained model are made publicly available to facilitate future research in MN biology.
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 This repository provides the pre-trained mnDINO model for our paper: [mnDINO: Accurate and robust segmentation of micronuclei with vision transformer networks](). The official PyTorch source code is publicly available on [GitHub](https://github.com/CaicedoLab/micronuclei-detection), and the annotated micronuclei dataset can be downloaded through the [Bioimage Archive](https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD2809). 
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+The mnDINO model is specifically designed for highly efficient and accurate micronuclei segmentation in DNA-stained images across diverse experimental conditions. The model outputs both micronuclei and nuclei segmentation masks simultaneously. To accelerate future research in micronucleus (MN) biology. The dataset, code, and pre-trained model are made publicly available to facilitate future research in micronucleus (MN) biology.
 # Usage
 ### Install Package
 ```bash