Awesome

Neurite

A neural networks toolbox with a focus on medical image analysis in tensorflow/keras for now.

To use the Neurite library, either clone this repository and install the requirements listed in setup.py or install directly with pip.

pip install neurite

layers: various network layers, sparse operations (e.g. SpatiallySparse_Dense), and LocallyConnected3D currently not included in keras
utils: various utilities, including interpn: N-D gridded interpolation, and several nonlinearities
- model: stack_models: keras model stacking
- vae: tools for analyzing (V)AE style models
- seg: segmentation tools
models: flexible models (many parameters to play with) particularly useful in medical image analysis, such as UNet/hourglass model, convolutional encoders and decoders
generators: generators for medical image volumes and various combinations of volumes, segmentation, categorical and other output
callbacks: a set of callbacks for keras training to help with understanding your fit, such as Dice measurements and volume-segmentation overlaps
dataproc: a set of tools for processing medical imaging data for preparation for training/testing
metrics: metrics (most of which can be used as loss functions), such as Dice or weighted categorical crossentropy
plot: plotting tools, mostly for debugging models

If you use this code, please cite:

Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation
Adrian V. Dalca, John Guttag, Mert R. Sabuncu
CVPR 2018.
[ PDF | arxiv | bibtex ]

If you are using any of the sparse/imputation functions, please cite:

Unsupervised Data Imputation via Variational Inference of Deep Subspaces
Adrian V. Dalca, John Guttag, Mert R. Sabuncu
Arxiv preprint 2019
[ arxiv | bibtex ]

Please open an issue [preferred] or contact Adrian Dalca at adalca@csail.mit.edu for question related to neurite.

Parts of neurite were used in VoxelMorph and brainstorm, which we encourage you to check out!