Patch-base analysis

NiftyNet is designed to facilitate patch-based medical image analysis. This is implemented with two types of extensible modules:

  • Window sampler: sampling image windows from volumes.
  • Window aggregator: decoding network’s output windows and the relevant location information, and assembling the volume-level outputs accordingly.

Image window sampling and aggregation.

The relevant configuration parameters are:

  • spatial_window_size,
  • volume_padding_size,
  • border.

The following sections describes the interaction of these parameters using a slice of fetal MRI as an example.

The code to generate these visualisations is available: NiftyNet/demos.

spatial window size

spatial_window_size defines the spatial size of the input of a convolutional network (The full shape is jointly determined by batch size, spatial window size, and number of modalities/channels of the input volume). The value of spatial_window_size should be less or equal than the spatial size of all the input files.

original uniform0 grid0

The above figure shows image window sampling locations with:

# in the input source section
spatial_window_size = (100, 100)

from the 574 x 437-pixel MRI, using a uniform window sampler and grid window sampler respectively.

The uniform window sampler first computes a set of all feasible spatial locations (so that the windows are always within the image) and randomly draws samples from the set.

The grid window sampler extracts all windows from the volume, with minimal window overlap when the window size is not divisible by the volume size. In other words, it is running a 100 x 100-pixel sliding window with a step size of 100 in both spatial dimensions.

volume padding size

volume_padding_size could be specified at the training and/or inference stage so that the input volumes are padded and image windows could be sampled at the borders of the input volume. This is also useful when some of the input volumes are smaller than the spatial window size.

The follow figure shows image window sampling with the configurations:

# in the input source section
spatial_window_size = (100, 100)

# in the [NETWORK] section
volume_padding_size = (50, 50)
volume_padding_mode = 'constant'

Note that with padding size equals to 50, 50-pixel will be added to both ends of each dimension, making the new image size: 674 x 537 from (the original size 574 x 437).

original50 uniform50 grid50


This parameter is useful at the inference stage, where a “sliding window” approach is applied to make dense predictions.

If the input and output image windows of a network have the same spatial size, there is no need for this parameter (`border = 0`) – the window sampler generates image windows and the corresponding spatial coordinates, the aggregator reads the coordinate information and assigns the output window to the output volumes.

However, if by design of the network architecture, a network generates a 76 x 76-pixel prediction when the input size is 100 x 100, we have to adjust the sampling step of the grid window sampler so that all input spatial locations get evaluated by the network.


  1. for the grid window sampler, we would like to have 100 x 100-pixel window generated with a step size of 76 in both directions;
  2. for the window aggregation, the spatial coordinates of the 76 x 76-pixel window should be adjusted, for example, from the input’s window coordinates [0, 0, 100, 100] to the output coordinates [12, 12, 88, 88], so that the output window will be concentric with the corresponding input window.

In NiftyNet, both are achieved by setting border size to 12, 12.

The follow figure shows input image window sampling with the configurations:

# in the input source section
spatial_window_size = (100, 100)

# in the [NETWORK] section
volume_padding_size = (50, 50)
volume_padding_mode = 'constant'

# in the [INFERENCE] section
border = (12, 12)

original50_12 grid50_12

In the above figure, two of the grid window samples are highlighted (blue) to show the overlap.

The following figure shows how the 76 x 76 output windows are assigned to the output volume by the window aggregator.


To confirm the sampling strategy, we visualise the input (blue) and output (pink) window grids together. The windows are concentric; there is overlap for the 100 x 100 input windows, but not for the 76 x 76 output windows; the thickness of the border in between input and output windows is (100 - 76) / 2 = 12 pixels.


Technically, it is possible to automatically determine border when the network design is known at TF graph construction time. This feature is not implemented yet (as of Sept. 2018). So the border parameter should be set manually in the configuration. The formula to compute border is floor((input size - output size) / 2) for each spatial dimension.

Other features

2.5D windows

Examples in the previous section are in 2-D, but the idea generalises to 3D input:

spatial_window_size = (h, w, d)

will make the sampler to generate 3D windows.

Setting the spatial window size to:

spatial_window_size = (h, w, 1)

will generate 2.5D windows. Using this configuration with random flipping layer during training will effectively use 2.5D input in different views.

Image as window

If all input volume have a consistent size h x w x d, and in the configuration:

# in the input source section
spatial_window_size = (h, w, d)

The input image will be used as window, i.e., there’s no randomisation when this configuration is used with the uniform sampler, and only one window sample per volume when used with the grid sampler.

Multi-channel inputs

The window samples are generated according to the spatial dimensions only. spatial_window_size, volume_padding_size, and border accept array values up to three elements. For multi-channel input images with sizes such as height x width x depth x num_modalities, the shape of the windows will be window height x window width x window depth x num_modalities.