PyTorch implementation of several neural network segmentaion models (UNet, FusionNet, DialatedConvolution) for cell image segmentation. The trained models from this repository are used for the segmentation plugin segmentify for Napari
| Original | Segmentation |
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The following datasets were used to train and test the different cell segmentatoin models:
After downloading the dataset from the links above, each dataset can be parsed and converted to a HDF5 file using scripts from ./process_data. For example:
python ./process_data/hpa_create_hdf5.py --input_dir PATH_TO_DATA --output_dir PATH_TO_OUTPUT
Example script can be found at ./process_data/create_hdf5.sh
This repository includes several simple segmentation networks, including:
The same segmentation models can be trained for a wide range of tasks. These different training procedures are done by altering the output target for the training data, as implemented in the ./dataset folder.
As expected, these segmentation models can be used for simple segmentation tasks such as segmenting out the Cell's nuclei:
| Dataset | Original | Segmentation |
|---|---|---|
| Neuro |  |
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| Nuclei |  |
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The above simple nuclei segmentation can be trained by running train.py and set the dataset argument to nuclei or neuron.
The segmentation models can also be used to simultaniously decompose microscopy images of cells into it's cellular structures, such as Nucleus, Microtubules and ER:
| Original | ER | Nucleus | Microtubules |
|---|---|---|---|
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The above cell structure decomposition model can be trained by running train.py and set the dataset argument to hpa as well as the desired target channels (e.g 0,2,3).
The segmentation models can also be used to restore missing pixels in an image:
| Original | Restoration | Ground Truth |
|---|---|---|
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The above missing pixels restoration model can be trained by running train.py and set the dataset argument to hpa_single.
To train any models, run the train.py with the desired configurations. Example configurations for each training modes can be found in the train_*.sh files. The output trained model will be based on the experiment_name parameter.
You can visualize the trianing loss and IoU during training, as well as the segmentation images, using Tensorboard. While the model is training, run tensorboard --logdir=logs --port=1234 and your Tensorboard can be opened in your browser with the link http://localhost:1234
If you are training your model on a remote server, you will need to forward the port on your local machine using:
ssh -N -f -L localhost:1234:localhost:5678 <YOUR_SSH_LOGIN>
Now you can view tensorboard on your local browser with the link http://localhost:5678