CHANGELOG.md

Changelog

All notable changes to NiftyNet are documented in this file.

The format is based on Keep a Changelog and this project adheres to Semantic Versioning.

Unreleased

0.4.0 - 2018-09-13

Added

• niftynet.layer: new layers
  • Tversky loss function for image segmentation
  • Random affine augmentation layer
  • Random bias field augmentation layer
  • Group normalisation layer
  • Squeeze and excitation blocks
• Documentation
  • Image reader and window sampler demo
  • 2D U-net demo
  • Dense CRF layer demo
  • FAQ list
• Misc.
  • Subject id from filename with regular expression
  • Versioning with python-versioneer
  • Tested with TensorFlow 1.10

Changed

• niftynet.engine: improved core functions
  • IO modules based on tf.data.Dataset (breaking changes)
  • Decoupled the engine and event handlers
• Migrated the code repository, model zoo, and niftynet.io source code to github.com/niftk.

0.3.0 - 2018-05-15

Added

• Support for 2D image loading optionally using skimage, pillow, or simpleitk
• Image reader and sampler with tf.data.Dataset
• Class-balanced image window sampler
• Random deformation as data augmentation with SimpleITK
• Segmentation loss with dense labels (multi-channel binary labels)
• Experimental features:
  • learning-based registration
  • image classification
  • model evaluation
  • new engine design with observer pattern

Deprecated

• Deprecating the CmicLab repository in favour of GitHub

0.2.2 - 2018-01-30

Added

• Improvements for running validation iterations during training

Fixed

• Bugs when running validation iterations during training
• Minor bugs in loss function modules, histogram standardisation, user parameter parsing

0.2.1 - 2017-12-14

Added

• Support for custom network / application as external modules
• Unified workspace directory via global configuration functionalities
• Model zoo for network / data sharing
• Automatic training / validation / test sets splitting
• Validation iterations during training
• Regression application
• 2D / 3D resampler layer
• Versioning functionality for better issue tracking
• Academic paper release: "NiftyNet: a deep-learning platform for medical imaging"
• How-to guides and a new theme for the API and examples documentation

0.2.0 - 2017-09-08

Added

• Support for unsupervised learning networks, including GANs and auto-encoders
• An application engine for managing low-level operations required by different types of high-level applications
• NiftyNet is now available on the Python Package Index: pip install niftynet
• NiftyNet website up and running: http://niftynet.io
• API reference published online: http://niftynet.rtfd.io/en/dev/py-modindex.html
• NiftyNet source code mirrored on GitHub: https://github.com/NifTK/NiftyNet
• 5 new network implementations:
  1. DenseVNet
  2. HolisticNet
  3. SimpleGAN
  4. SimulatorGAN
  5. VariationalAutoencoder (VAE)

Fixed

• Bugs (30+ issues resolved)

0.1.1 - 2017-08-08

Added

• Source code open sourced (CMICLab, GitHub)
• Initial PyPI package release
• Refactored sub-packages including engine, application, layer, network
• Command line entry points
• NiftyNet logo

Fixed

• Bugs in data augmentation, I/O, sampler