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CHANGELOG.md

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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

0.6.0 - 2019-10-09

Added

  • isotropic random scaling option
  • volume padding with user-specified constant
  • subpixel layer for superresolution
  • various loss functions for regression (smooth L1 loss, cosine loss etc.)
  • handler for early stopping mechanism
  • aggregator with multiple outputs including labels in CSV
  • nnUNet, an improved version of UNet3D
  • data augmentation with mixup and mixmatch
  • documentation contents
  • demo for learning rate scheduling
  • demo for deep boosted regression
  • initial integration of NiftyReg Resampler
  • initial integration of CSV reader

Fixed

  • issue of loading binary values of NIfTI file
  • various fixes in CI tests
  • prefix name for aggregators
  • various improvements in error messages
  • issue of batch indices in the conditional random field
  • issue of location selection in the weighted sampler
  • model zoo: compatibility upgrade
  • model zoo: new decathlon hippocampus dataset

Changed

  • feature normalisation types options: instance norm, group norm, batch norm
  • convolution with padding option
  • various documentation and docstrings
  • defaulting to remove length one dimensions when saving a 5D volume

0.5.0 - 2019-02-04

Added

  • Version controlled model zoo with git-lfs
  • Dice + entropy loss function
  • Antialiasing when randomly scaling input images during training
  • Support of multiple optimisers and gradients in applications

Fixed

  • An issue of rounding image sizes when pixdim is specified
  • An issue of incorrect Dice when image patch does not include every class
  • Numerous documentation issues

Changed

  • Tested with TensorFlow 1.12

0.4.0 - 2018-09-13

Added

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

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

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