MnDINO

--- license: mit language: - en base_model: - facebook/dinov2-base pipeline_tag: image-segmentation tags: - micronuclei-segmentation --- # mnDINO: Accurate and robust segmentation of micronuclei with vision transformer networks This repository provides the pre-trained mnDINO model for our paper: [mnDINO: Accurate and robust segmentation of micronuclei with vision transformer networks](https://www.biorxiv.org/content/10.64898/2026.03.09.710648v2). 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). 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 pip install mndino ``` ### Load the model ```python import torch from mndino import mnmodel from huggingface_hub import hf_hub_download model_path = hf_hub_download(repo_id="CaicedoLab/mnDINO", filename="mnDINO_v1.pth") device = "cuda" if torch.cuda.is_available() else "cpu" model = mnmodel.MicronucleiModel(device=device) model.load(model_path) ``` ### Make predictions ```python import skimage import numpy as np STEP = 32 # recommended value PREDICTION_BATCH = 4 THRESHOLD = 0.5 im = skimage.io.imread(your_image_path) im = np.array((im - np.min(im))/(np.max(im) - np.min(im)), dtype="float32") # normalize image probabilities = model.predict(im, stride=1, step=STEP, batch_size=PREDICTION_BATCH) mn_predictions = probabilities[0,:,:] > THRESHOLD nuclei_predictions = probabilities[1,:,:] > THRESHOLD ``` ### Evaluation ```python import skimage from mndino import evaluation mn_gt = skimage.io.imread(your_annotated_image_path) precision, recall = evaluation.segmentation_report(predictions=mn_predictions, gt=mn_gt, intersection_ratio=0.1, wandb_mode=False) ```