pseudo-histology synthesis training from corresponding micro-CT slices on manually registered samples
Documentation: https://gitlab.desy.de/MDLMA/histology
Source: https://gitlab.desy.de/MDLMA/histology
Author: C.Lucas (Hereon)
Jupyter: available as a jupyter kernel (pytorch) on https://max-jhub.desy.de
Maxwell: mamba activate /software/jupyter/.conda/latest/pytorch
Module: module load maxwell mdlma/histology
Scripts: the scripts are available on Maxwell at /software/mdlma/histology and include in the module-path
Description:
This module contains the scripts to run the pseudo-histology synthesis training from corresponding micro-CT slices on manually registered samples.
It uses the cycleGAN implementation from Erik Linder-Norén and further adds an extra reconstruction loss on the B modality fake into the generator loss formula.
The module consists of:
- train.py main training script to be run on the "train_dir" data (see settings.py)
- settings.py contains the training parameters to be defined beforehand
- model.py contains the ResNet-based generator and discriminator model definitions
- data.py contains the data loader for training and test
- pytorch_train_maskOutInnerScrew contains all manually registered 8 cases with large parts of the screw area masked out to cope with the imbalance of tissue prevalence in the images and weight down the screw area dominance
- pytorch_train_noMask contains all 8 cases with with only the background being masked out
- test.py test script to load the models and test them on the "test_dir" data
- test-volume.ipynb ipython notebook to test on original volume data, and not the manually registered - thus it includes several manual corrections due to the different value range / data type of the training data
- utils.py utility functions required for training or visualization