Train and Test Dataset for a Neural Network for the task of RSA Segmentation in the context of a Master thesis 2024

The data corresponds to the work of the master thesis "3D Segmentation of Plant Roots from MRI Images for Enhanced Automated Root Tracing using Deep Neural Networks" by Daniel Weißen from the 24/07/2024

train_data.tar
- contains the training and validation data used for the DNN
  - data_gmm contains the soil noise data based on a Gaussian Mixture Model
  - data_FS contains the soil noise data based on the Fourier Synthesis approach described in the thesis

test_data.tar
- contains the test data which is manually labeled

segmentations.tar
- contains the segmentations of the trained SwinUNETR-V2+ on the real test MRIs
  - fs_model_output: contains the Fourier Synthesis trained NNs segmentation
  - fmm_model_output: contains the GMM trained NNs segmentation
  - threshold_output: contains the Dice optimized threshold labeling

FS_checkpoint.ckpt, GMM_checkpoint.ckpt
- contains the checkpoint for the trained SwinUNETR-V2+ on the Fourier synthesis, and GMM soil noise respectively