Operations Sequence¶

This algorithm allows to run operations from the ML plugin on an datasets. Operations are pre- (resp. post-) processing steps that are applied to a machine learning model input (resp. output) during both training and inference. Such operations are typically automatically applied in ML algorithms, so this algorithm mostly serves exploratory purposes.

Input¶

An image (or a set of images), and optionally a corresponding label map (or set of label maps).

Output¶

A processed version of the input images, and optionally the corresponding processed label maps.

Description¶

The algorithm can be configured via three approaches (which can be selected by clicking the corresponding tab in the controller):

Single Operation Allows to configure and execute a single operation. The parameters depend on the selected operation but some parameters are always included:
- device Computing device, defaulting to gpu_if_opengl. Use force_cpu to run operation on the CPU.
- process_label Decides on which input data the operation must be run (0 for everything except label maps, 1 for everything, 2 for only labels maps).
From File Allows to run the pre or post-processing sequence of a model from its YAML file. More information on the YAML format can be found in our tutorial here.

From Text Allows to specify a sequence of operation formatted as a YAML list, for instance:

- MakeFloat: {} # First convert to float
- Resample:
    resolution: 1.5
- NormalizePercentile:
    min: 0.001
    max: 0.999