Classic Data Processing

alt text PlantSeg includes essential utilities for data pre-processing and post-processing.

Pre-Processing

The input for this widget can be either a "raw" image or a "prediction" image. Input formats allowed are tiff and h5, while output is always h5.

Save Directory can be used to define the output directory.
The most critical setting is the Rescaling. It is important to rescale the image to match the resolution of the data used for training the Neural Network. This operation can be done automatically by clicking on the GUI on Guided. Be careful to use this function only in case of data considerably different from the reference resolution.
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As an example:
  - if your data has the voxel size of 0.3 x 0.1 x 0.1 (ZYX).
  - and the networks was trained on 0.3 x 0.2 x 0.2 data (reference resolution).

The required voxel size can be obtained by computing the ratio between your data and the
reference train dataset. In the example the rescaling factor = 1 x 2 x 2.
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The Interpolation field controls the interpolation type (0 for nearest neighbors, 1 for linear spline, 2 for quadratic).
The last field defines a Filter operation. Implemented there are:
1. Gaussian Filtering: The parameter is a float and defines the sigma value for the gaussian smoothing. The higher, the wider is filtering kernel.
2. Median Filtering: Apply median operation convolutionally over the image. The kernel is a sphere of size defined in the parameter field.

Post-Processing

A post-processing step can be performed after the CNN-Predictions and the Segmentation. The post-processing options are: * Converting the output to the tiff file format (default is h5).

Casting the CNN-Predictions output to data_uint8 drastically reduces the memory footprint of the output file.

Additionally, the post-processing will scale back your outputs to the original voxels resolutions.