MitchondriaEMSegmentation2D

#Prediction Enhancer for Mitochondrion Segmentation in EM

This model was trained to segment mitochondria in EM. It predicts foreground and boundary probabilities.

The network was trained on data from the VNC dataset and trained using torch_em.

• Imaging modality: Electron Microscopy
• Dimensionality: 2D
• Source: http://dx.doi.org/10.6084/m9.figshare.856713

It is recommended to validate the instance segmentation obtained from this model using intersection-over-union. This model can be used in ilastik, deepimageJ or other software that supports the bioimage.io model format.

• n_epochs: 5
• batches_per_epoch: 500
• batch_size: 1
• loss_function: DiceLoss
• optimizer: Adam
• learning_rate: 0.0001
• n_train_images: None
• n_validation_images: None

For questions or issues with this models, please reach out by:

• opening a topic with tags bioimageio and mitchondriaemsegmentation2d on image.sc
• or creating an issue in https://github.com/constantinpape/torch-em