SegSRGAN 1.1.4

Segmentation and super resolution GAN network

SegSRGAN

This algorithm is based on the method proposed by Chi-Hieu Pham in 2019.

Installation

pip install SegSRGAN

Perform a segmentation

from SegSRGAN.SegSRGAN.Function_for_application_test_python3 import segmentation

segmentation(input_file_path, step, NewResolution, path_output_cortex, path_output_HR, weights_path, patch=None, spline_order=3, by_batch=False, is_conditional=False)

Where:

• input_file_path is the path of the image to be super resolved and segmented
• step is the shifting step for the patches
• NewResolution is the new z-resolution we want for the output image
• path_output_cortex output path of the segmented cortex
• path_output_HR output path of the super resolution output image
• weights_path is the path of the file which contains the pre-trained weights for the neural network
• patch is the size of the patches
• spline_order for the interpolation
• by_batch is to enable the by-batch processing
• is_conditional to perform a conditional GAN on the LR image resolution

Segmentation of a set of images with several step and patch values

In order to facilitate the segmentation of several images, you can run SegSRGAN/SegSRGAN/job_model.py:

python job_model.py --path --patch --step --result_folder_name --weights_relative_path --is_conditional

The list of the paths of the images to be processed must be stored in a csv file.

Where:

• path Path of the csv file
• patch list of patch sizes
• step list of steps
• result_folder_name Name of the folder containing the results
• is_conditional Boolean to perform a conditional neural network with a condition on z-resolution

Example of syntax for step and patch setting:

--patch 64 128

--step 32 64,64 128

In this example we run steps 32 and 64 for patch 64 and steps 64 and 128 for patch 128. Be careful to respect the exact same spaces.