Download RDF PackageThis model was trained to segment mitochondria in EM. It predicts foreground and boundary probabilities. Training The network was trained on data from the VNC dataset and trained using torch_em. Training Data Imaging modality: Electron Microscopy Dimensionality: 2D Source: http://dx.doi.org/10.6084/m9.figshare.856713 Recommended Validation 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. Training Schedule 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 Contact 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

Segmentation of mitochondria in EM images. (Uploaded via https://bioimage.io)