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README.md

Configuration file

To run a NiftyNet application or a customised application which implements BaseApplication, a configuration file needs to be provided, for example, by creating a user_configuration.ini file and using the file via python net_gan.py --conf user_configuration.ini.

This folder presents a few examples of configuration files for different applications. All files should have four sections:

  • [SYSTEM]
  • [NETWORK]
  • [TRAINING]
  • [INFERENCE]

These describes common options and hyperparameters for all applications.

Additionally, an application specific section is required for each application:

  • [GAN] for generative adversirial networks
  • [SEGMENTATION] for segmentation networks
  • [AUTOENCODER] for autoencoder networks

The user parameter parser tries to match the section names. All other section names will be treated as [input data source specifications](##Input data source section).

Input data source section

This section will be used by ImageReader to generate a list of input images objects. For example:

[T1Image]  
path_to_search = ./example_volumes/image_folder
filename_contain = ('T1', 'subject') 
filename_not_contain = ('T1c', 'T2')
spatial_window_size = (128, 128, 1)
pixdim = (1.0, 1.0, 1.0)
axcodes=(A, R, S)
interp_order = 3

Specifies a set of images (currently supports NIfTI format) from ./example_volumes/image_folder, with filnames contain both T1 and subject, but not contain T1c and T2. These images will be read into memory and transformed into "A, R, S" orientation (using NiBabel). The images will also be transformed to have voxel size (1.0, 1.0, 1.0) with an interpolation order of 3.

This input source can be used alone, as a T1 MRI input to an application. It can also be used along with other modalities, a multi-modality example can be find at here.

Currently image data in nifty format (extension .nii or .nii.gz) are supported.

The following sections describe key parameters that can be specified in the configuration file.

[SYSTEM]

  • queue_length an integer specifies size of image window buffer used when sampling image windows from image volumes. Image window samplers fill the buffer and networks read the buffer.

[NETWORK]

Histogram normalisation

The histogram normalisation is performed using the method described. The following fields can be specified:

  • normalisation [True/False] Indicates if an histogram standardisation should be applied to the data
  • whitening [True/False] Indicates if the loaded image should be whitened I->(I-mean)/std
  • histogram_ref_file: Name of the file that contains the normalisation parameter if it has been trained before or where to save it
  • norm_type: type of landmarks used in the histogram for the matching (percentile or quartile)
  • cutoff: a list of two floats, inferior and superior cutoff of the histograms for the matching
  • foreground_type: to generate a foreground mask and the normalisation will be applied to foreground only. Choice between:
    • otsu_plus
    • otsu_minus
    • thresh_plus
    • thresh_minus
  • multimod_foreground_type: strategies applied to combine foreground masks of multiple modalities, can take one of the following:
    • or union of the available masks
    • and intersection of the available masks
    • all a different mask is applied to each modality

[TRAINING]

Augmentation at training

  • rotation_angle a tuple of two floats, indicating a random rotation operation should be applied to the volumes (This can be very slow depending on the input volume dimensionality)
  • scaling_percentage a tuple of two floats, indicating a random spatial scaling should be applied (This can be very slow depending on the input volume dimensionality)
  • lr Learning rate to be applied
  • loss_type. Loss function to be used
  • reg_type Regularisor to be used
  • save_every _n Frequency of model saving
  • max_iter Maximum number of training steps
  • volume_padding _size One side length of the receptive field affected by the network

[INFERENCE]

  • inference_iter an integer specifies the trained model to be used for inference. -1 or unspecified indicating the latest trained model.
  • spatial_window_size a tuple of integers indicating the size of input window at inference time, this overrides the spatial_window_size parameter in the input source sections.
  • border a tuple of integers specifying a border size used to crop (along both sides of each dimension) the network output image window. E.g., (3, 3, 3) will crop a (64, 64, 64) window to size (58, 58, 58).