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https://github.com/neuronets/nobrainer

A framework for developing neural network models for 3D image processing.
https://github.com/neuronets/nobrainer
machine-learning medical-imaging neuroimaging semantic-segmentation tensorflow
Last synced: 3 days ago
JSON representation
A framework for developing neural network models for 3D image processing.
Host: GitHub
URL: https://github.com/neuronets/nobrainer
Owner: neuronets
License: other
Created: 2017-06-16T23:38:47.000Z (over 7 years ago)
Default Branch: master
Last Pushed: 2024-08-11T13:45:16.000Z (3 months ago)
Last Synced: 2024-08-11T23:20:01.817Z (3 months ago)
Topics: machine-learning, medical-imaging, neuroimaging, semantic-segmentation, tensorflow
Language: Python
Homepage:
Size: 2.42 MB
Stars: 154
Watchers: 11
Forks: 46
Open Issues: 62
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
- Citation: CITATION
Awesome Lists containing this project

README

        # Nobrainer

![Build status](https://github.com/neuronets/nobrainer/actions/workflows/ci.yml/badge.svg)

_Nobrainer_ is a deep learning framework for 3D image processing. It implements

several 3D convolutional models from recent literature, methods for loading and

augmenting volumetric data that can be used with any TensorFlow or Keras model,

losses and metrics for 3D data, and simple utilities for model training, evaluation,

prediction, and transfer learning.

_Nobrainer_ also provides pre-trained models for brain extraction, brain segmentation,

brain generation and other tasks. Please see the [_Trained_ models](https://github.com/neuronets/trained-models)

repository for more information.

The _Nobrainer_ project is supported by NIH RF1MH121885 and is distributed under

the Apache 2.0 license. It was started under the support of NIH R01 EB020470.

## Table of contents

- [Implementations](#implementations)

  - [Models](#models)

  - [Dropout and additional layers](#dropout-and-regularization-layers)

  - [Losses](#losses)

  - [Metrics](#metrics)

  - [Augmentation methods](#augmentation-methods)

- [Guide Jupyter Notebooks](#guide-jupyter-notebooks-)

- [Installation](#installation)

  - [Container](#container)

    - [GPU support](#gpu-support)

    - [CPU only](#cpu-only)

  - [pip](#pip)

- [Using pre-trained networks](#using-pre-trained-networks)

  - [Predicting a brainmask for a T1-weighted brain scan](#predicting-a-brainmask-for-a-t1-weighted-brain-scan)

  - [Generating a synthetic T1-weighted brain scan](#generating-a-synthetic-t1-weighted-brain-scan)

  - [Transfer learning](#transfer-learning)

- [Data augmentation](#data-augmentation)

  - [Random rigid transformation](#random-rigid-transformation)

- [Package layout](#package-layout)

- [Questions or issues](#questions-or-issues)

## Implementations

### Models

| Model | Type | Application |

|:-----------|:-------------:|:-------------:|

|[**Highresnet**](https://github.com/neuronets/nobrainer/blob/master/nobrainer/models/highresnet.py) [(source)](https://arxiv.org/abs/1707.01992)| supervised  | segmentation/classification |

|[**Unet**](https://github.com/neuronets/nobrainer/blob/master/nobrainer/models/unet.py) [(source)](https://arxiv.org/abs/1606.06650)| supervised | segmentation/classification |

|[**Vnet**](https://github.com/neuronets/nobrainer/blob/master/nobrainer/models/vnet.py) [(source)](https://arxiv.org/pdf/1606.04797.pdf)| supervised  | segmentation/classification |

|[**Meshnet**](https://github.com/neuronets/nobrainer/blob/master/nobrainer/models/meshnet.py) [(source)](https://arxiv.org/abs/1612.00940)| supervised  | segmentation/clssification |

|[**Bayesian Meshnet**](https://github.com/neuronets/nobrainer/blob/master/nobrainer/models/bayesian-meshnet.py) [(source)](https://www.frontiersin.org/articles/10.3389/fninf.2019.00067/full)| bayesian supervised | segmentation/classification |

|[**Bayesian Vnet**](https://github.com/neuronets/nobrainer/blob/master/nobrainer/models/bayesian_vnet.py) | bayesian supervised | segmentation/classification |

|[**Semi_Bayesian Vnet**](https://github.com/neuronets/nobrainer/blob/master/nobrainer/models/bayesian_vnet.py) | semi-bayesian supervised | segmentation/classification |

|[**DCGAN**](https://github.com/neuronets/nobrainer/blob/master/nobrainer/models/dcgan.py) | self supervised | generative model |

|[**Progressive GAN**](https://github.com/neuronets/nobrainer/blob/master/nobrainer/models/progressivegan.py) | self supervised | generative model |

|[**3D Autoencoder**](https://github.com/neuronets/nobrainer/blob/master/nobrainer/models/autoencoder.py) | self supervised | knowledge representation/dimensionality reduction |

|[**3D Progressive Autoencoder**](https://github.com/neuronets/nobrainer/blob/master/nobrainer/models/progressiveae.py) | self supervised | knowledge representation/dimensionality reduction |

|[**3D SimSiam**](https://github.com/neuronets/nobrainer/blob/master/nobrainer/models/brainsiam.py) [(source)](https://arxiv.org/abs/2011.10566)| self supervised | Siamese Representation Learning |

### Dropout and regularization layers

[Bernouli dropout layer](https://github.com/neuronets/nobrainer/blob/80d8a47a7f2bf4fe335bdf194c0be19044223629/nobrainer/layers/dropout.py#L15), [Concrete dropout layer](https://github.com/neuronets/nobrainer/blob/80d8a47a7f2bf4fe335bdf194c0be19044223629/nobrainer/layers/dropout.py#L71), [Gaussian dropout](https://github.com/neuronets/nobrainer/blob/80d8a47a7f2bf4fe335bdf194c0be19044223629/nobrainer/layers/dropout.py#L204), [Group normalization layer](), [Costom padding layer]()

### Losses

[Dice](), [Jaccard](), [Tversky](), [ELBO](), [Wasserstien](), [Gradient Penalty]()

### Metrics

[Dice](), [Generalized Dice](), [Jaccard](), [Hamming](), [Tversky]()

### Augmentation methods

#### Spatial Transforms

[Center crop](), [Spatial Constant Padding](), [Random Crop](), [Resize](), [Random flip (left and right)]()

#### Intensity Transforms

[Add gaussian noise](), [Min-Max intensity scaling](), [Costom intensity scaling](), [Intensity masking](), [Contrast adjustment]()

#### Affine Transform

Afifine transformation including rotation, translation, reflection.

## Guide Jupyter Notebooks [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuronets/nobrainer-book/blob/master/)

Please refer to the Jupyter notebooks in the [guide](https://github.com/neuronets/nobrainer-book/blob/master/docs/nobrainer-guides/notebooks/) directory to get

started with _Nobrainer_. [Try them out](https://colab.research.google.com/github/neuronets/nobrainer-book/blob/master/) in Google Colaboratory!

## Installation

### Container

We recommend using the official _Nobrainer_ Docker container, which includes all

of the dependencies necessary to use the framework. Please see the available images

on [DockerHub](https://hub.docker.com/r/neuronets/nobrainer)

#### GPU support

The _Nobrainer_ containers with GPU support use the Tensorflow jupyter GPU containers. Please

check the containers for the version of CUDA installed. Nvidia drivers are not included in

the container.

```

$ docker pull neuronets/nobrainer:latest-gpu

$ singularity pull docker://neuronets/nobrainer:latest-gpu

```

#### CPU only

This container can be used on all systems that have Docker or Singularity and

does not require special hardware. This container, however, should not be used

for model training (it will be very slow).

```

$ docker pull neuronets/nobrainer:latest-cpu

$ singularity pull docker://neuronets/nobrainer:latest-cpu

```

### pip

_Nobrainer_ can also be installed with pip.

```

$ pip install nobrainer

```

## Using pre-trained networks

Pre-trained networks are available in the [_Trained_ models](https://github.com/neuronets/trained-models)

repository. Prediction can be done on the command-line with `nobrainer predict`

or in Python. Similarly, generation can be done on the command-line with

`nobrainer generate` or in Python.

### Predicting a brainmask for a T1-weighted brain scan

![Model's prediction of brain mask](https://github.com/neuronets/trained-models/blob/master/images/brain-extraction/unet-best-prediction.png?raw=true)

![Model's prediction of brain mask](https://github.com/neuronets/trained-models/blob/master/images/brain-extraction/unet-worst-prediction.png?raw=true)

_{__Figure__: In the first column are T1-weighted brain scans, in the middle

are a trained model's predictions, and on the right are binarized FreeSurfer

segmentations. Despite being trained on binarized FreeSurfer segmentations,

the model outperforms FreeSurfer in the bottom scan, which exhibits motion

distortion. It took about three seconds for the model to predict each brainmask

using an NVIDIA GTX 1080Ti. It takes about 70 seconds on a recent CPU.}

In the following examples, we will use a 3D U-Net trained for brain extraction and

documented in [_Trained_ models](https://github.com/neuronets/trained-models#brain-extraction).

In the base case, we run the T1w scan through the model for prediction.

```bash

# Get sample T1w scan.

wget -nc https://dl.dropbox.com/s/g1vn5p3grifro4d/T1w.nii.gz

docker run --rm -v $PWD:/data neuronets/nobrainer \

  predict \

    --model=/models/neuronets/brainy/0.1.0/brain-extraction-unet-128iso-model.h5 \

    --verbose \

    /data/T1w.nii.gz \

    /data/brainmask.nii.gz

```

For binary segmentation where we expect one predicted region, as is the case with

brain extraction, we can reduce false positives by removing all predictions not

connected to the largest contiguous label.

```bash

# Get sample T1w scan.

wget -nc https://dl.dropbox.com/s/g1vn5p3grifro4d/T1w.nii.gz

docker run --rm -v $PWD:/data neuronets/nobrainer \

  predict \

    --model=/models/neuronets/brainy/0.1.0/brain-extraction-unet-128iso-model.h5 \

    --largest-label \

    --verbose \

    /data/T1w.nii.gz \

    /data/brainmask-largestlabel.nii.gz

```

Because the network was trained on randomly rotated data, it should be agnostic

to orientation. Therefore, we can rotate the volume, predict on it, undo the

rotation in the prediction, and average the prediction with that from the original

volume. This can lead to a better overall prediction but will at least double the

processing time. To enable this, use the flag `--rotate-and-predict` in

`nobrainer predict`.

```bash

# Get sample T1w scan.

wget -nc https://dl.dropbox.com/s/g1vn5p3grifro4d/T1w.nii.gz

docker run --rm -v $PWD:/data neuronets/nobrainer \

  predict \

    --model=/models/neuronets/brainy/0.1.0/brain-extraction-unet-128iso-model.h5 \

    --rotate-and-predict \

    --verbose \

    /data/T1w.nii.gz \

    /data/brainmask-withrotation.nii.gz

```

Combining the above, we can usually achieve the best brain extraction by using

`--rotate-and-predict` in conjunction with `--largest-label`.

```bash

# Get sample T1w scan.

wget -nc https://dl.dropbox.com/s/g1vn5p3grifro4d/T1w.nii.gz

docker run --rm -v $PWD:/data neuronets/nobrainer \

  predict \

    --model=/models/neuronets/brainy/0.1.0/brain-extraction-unet-128iso-model.h5 \

    --largest-label \

    --rotate-and-predict \

    --verbose \

    /data/T1w.nii.gz \

    /data/brainmask-maybebest.nii.gz

```

### Generating a synthetic T1-weighted brain scan

![Model's generation of brain (sagittal)](https://github.com/neuronets/trained-models/blob/master/images/brain-generation/progressivegan_generation_sagittal.png?raw=true)

![Model's generation of brain (axial)](https://github.com/neuronets/trained-models/blob/master/images/brain-generation/progressivegan_generation_axial.png?raw=true)

![Model's generation of brain (coronal)](https://github.com/neuronets/trained-models/blob/master/images/brain-generation/progressivegan_generation_coronal.png?raw=true)

_{__Figure__: Progressive generation of T1-weighted brain MR scan starting

from a resolution of 32 to 256 (Left to Right: 32³, 64³,

128³, 256³). The brain scans are generated using the same

latents in all resolutions. It took about 6 milliseconds for the model to generate

the 256³ brainscan using an NVIDIA TESLA V-100.}

In the following examples, we will use a Progressive Generative Adversarial Network

trained for brain image generation and documented in

[_Trained_ models](https://github.com/neuronets/trained-models#brain-extraction).

In the base case, we generate a T1w scan through the model for a given resolution.

We need to pass the directory containing the models `(tf.SavedModel)` created

while training the networks.

```bash

docker run --rm -v $PWD:/data neuronets/nobrainer \

  generate \

    --model=/models/neuronets/braingen/0.1.0 \

    --output-shape=128 128 128 \

    /data/generated.nii.gz

```

We can also generate multiple resolutions of the brain image using the same

latents to visualize the progression

```bash

# Get sample T1w scan.

docker run --rm -v $PWD:/data neuronets/nobrainer \

  generate \

    --model=/models/neuronets/braingen/0.1.0 \

    --multi-resolution \

    /data/generated.nii.gz

```

In the above example, the multi resolution images will be saved as

`generated_res_{resolution}.nii.gz`

### Transfer learning

The pre-trained models can be used for transfer learning. To avoid forgetting

important information in the pre-trained model, you can apply regularization to

the kernel weights and also use a low learning rate. For more information, please

see the _Nobrainer_ guide notebook on transfer learning.

As an example of transfer learning, [@kaczmarj](https://github.com/kaczmarj)

re-trained a brain extraction model to label meningiomas in 3D T1-weighted,

contrast-enhanced MR scans. The original model is publicly available and was

trained on 10,000 T1-weighted MR brain scans from healthy participants. These

were all research scans (i.e., non-clinical) and did not include any contrast

agents. The meningioma dataset, on the other hand, was composed of relatively

few scans, all of which were clinical and used gadolinium as a contrast agent.

You can observe the differences in contrast below.

![Brain extraction model prediction](https://github.com/neuronets/trained-models/blob/master/images/brain-extraction/unet-best-prediction.png?raw=true)

![Meningioma extraction model prediction](https://user-images.githubusercontent.com/17690870/55470578-e6cb7800-55d5-11e9-991f-fe13c03ab0bd.png)

Despite the differences between the two datasets, transfer learning led to a much

better model than training from randomly-initialized weights. As evidence, please

see below violin plots of Dice coefficients on a validation set. In the left plot

are Dice coefficients of predictions obtained with the model trained from

randomly-initialized weights, and on the right are Dice coefficients of predictions

obtained with the transfer-learned model. In general, Dice coefficients are higher

on the right, and the variance of Dice scores is lower. Overall, the model on the

right is more accurate and more robust than the one on the left.









## Package layout

- `nobrainer.io`: input/output methods

- `nobrainer.layers`: custom layers, which conform to the Keras API

- `nobrainer.losses`: loss functions for volumetric segmentation

- `nobrainer.metrics`: metrics for volumetric segmentation

- `nobrainer.models`: pre-defined Keras models

- `nobrainer.training`: training utilities (supports training on single and multiple GPUs)

- `nobrainer.transform`: random rigid transformations for data augmentation

- `nobrainer.volume`: `tf.data.Dataset` creation and data augmentation utilities

-

## Citation

If you use this package, please [cite](https://github.com/neuronets/nobrainer/blob/master/CITATION) it.

## Questions or issues

If you have questions about _Nobrainer_ or encounter any issues using the framework,

please [submit a GitHub issue](https://github.com/neuronets/helpdesk/issues/new/choose).