neurite

Overview of the Neurite Project

The Neurite project is an innovative toolbox specifically designed for neural network applications, with a primary focus on medical image analysis using TensorFlow and Keras. Neurite is developed to cater to a niche in the deep learning landscape by providing specialized tools and utilities that are essential for handling and analyzing medical imaging data effectively.

Installation

Getting started with Neurite is straightforward. Users can either clone the Neurite repository from GitHub and manually install the required packages as listed in setup.py, or they can opt for the more convenient approach of using pip:

pip install neurite

This command allows for a seamless installation, ensuring that all necessary components are readily available for immediate use in projects.

Key Tools and Features

Neurite offers a rich set of tools and features tailored for medical image analysis:

• Layers: Includes custom network layers not typically found in Keras, such as SpatiallySparse_Dense and LocallyConnected3D, facilitating sparse operations.
• Utilities: A collection of utilities for tasks like N-D gridded interpolation (interpn) and various non-linear functions.
  • Models can be stacked using stack_models, and tools for analyzing variational autoencoder (VAE) models are included.
  • There are also resources for segmentation tasks.
• Models: Flexible model architectures like the UNet and hourglass models are available, alongside convolutional encoder-decoder structures. These are particularly beneficial for medical image processing tasks.
• Generators: Specialized generators for creating medical image volumes, and generating different output combinations such as segmentation and categorical data.
• Callbacks: Enhancements for Keras training that provide insights into model performance, including metrics like Dice measurements and volume-segmentation overlaps.
• Data Processing: Tools for preparing medical imaging datasets for training and testing purposes.
• Metrics: Various metrics, many of which serve dual purposes as loss functions, such as Dice and weighted categorical crossentropy, critical for evaluating model performance.
• Plotting: Debugging models is made easier with plotting tools tailored for visualization.

Research and Citations

Neurite is backed by academic research. Users employing Neurite in their work are encouraged to cite relevant publications:

1. Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation

   • Authors: Adrian V. Dalca, John Guttag, Mert R. Sabuncu
   • Source: CVPR 2018

2. Unsupervised Data Imputation via Variational Inference of Deep Subspaces

   • Authors: Adrian V. Dalca, John Guttag, Mert R. Sabuncu
   • Source: Arxiv preprint 2019

These references not only validate Neurite's capabilities but also guide users in leveraging its potential in unsupervised biomedical segmentation and data imputation tasks.

Contributions and Community Engagement

The Neurite team is open to community contributions. Prospective contributors are encouraged to adhere to the pep8 coding standards, with a few exceptions, to maintain code quality. The community can report issues via GitHub or reach out directly to Adrian Dalca for any inquiries.

Applications and Demonstrations

Neurite is not just a stand-alone tool; it has been integral to larger projects such as VoxelMorph and brainstorm. These projects demonstrate the practical applications of Neurite's capabilities, offering a vista into how these tools can be utilized in real-world scenarios.