Nobrainer models

This repository contains pre-trained models for 3D neuroimaging data processing. These models can be used for their original purpose or for transfer learning on a new task. For example, a pre-trained brain extraction network can be trained on a tumor-labeling task. models are included based on <org-name>/<model-name>/<version>.

Neuronets organization

These models were trained using the Nobrainer framework, which wraps TensorFlow/Keras.

• brainy: 3D U-Net brain extraction model
• ams: automated meningioma segmentation model
• kwyk: bayesian neural network for brain parcellation and uncertainty estimation
• braingen: progressive generation of T1-weighted brain MR scans

The folder inside the model names shows the released versions of the model.

UCL organization

• SynthSeg: 3D brain MRI segmentation model
• SynthSR: 3D brain MRI (& CT) super resolution model

DDIG Organization

• SynthMorph: contrast agnostic registration model
• VoxelMorph: learning based registration model

Downloading models

This repo is a datalad dataset. To get the models you need datalad and datalad-osf. First datalad clone the repo and then run datalad get -s osf-storage . to get the whole content.

datalad clone https://github.com/neuronets/trained-models
cd trained-models
datalad get -s osf-storage .

to get a specific model you can pass the path of the model to the datalad get.

datalad get -s osf-storage neuronets/ams/0.1.0/meningioma_T1wc_128iso_v1.h5

datalad get -s osf-storage neuronets/braingen

Using models for inference or training

You can use the Nobrainer-zoo toolbox for easy inference and re-training of the models without installing any additional model dependencies.

Loading models for training with python and tensorflow/keras

All models are available for re-training or transfer learning purposes except the kwyk model. The kwyk model weights are not available in a tf2 keras format (We are working to make it available in near future). The kwyk models can be loaded with tf.keras.models.load_model.

import tensorflow as tf

model = tf.keras.models.load_model("neuronets/brainy/0.1.0/brain-extraction-unet-128iso-model.h5")
model.fit(...)

You can see a transfer learning example here, and an example of brain MRI generation using braingen models can be find here.

For an example of inference using kwyk model, please see this notebook.