This is the official PyTorch implementation of the paper - "Multi-Aperture Fusion of Transformer-Convolutional Network (MFTC-Net) for 3D Medical Image Segmentation and Visualization".

python3.10 -m venv MFTCNet_env 
source MFTCNet_env/bin/activate 
pip install -r requirements.txt

Download data from: https://www.synapse.org/#!Synapse:syn3193805/wiki/89480

data/
 |---imagesTr/
     |---img0001.nii.gz
     |---img0002.nii.gz
 |---labelsTr/
     |---label0001.nii.gz
     |---label0002.nii.gz
 |---dataset.json

This repository is built upon the foundational work provided in Synapse.

Before training the configs.json file should be filled:

The following settings can be adjusted in the config.py to configure the model training and data management:

• data_dir: Set the directory path for dataset storage.
• saved_model_dir: Set the directory path where trained models and checkpoints will be saved.
• num_samples: Define the number of samples used in training process.
• num_classes: Specify the number of target classes in the dataset + background.
• input_size: Set the size of the input images or data.
• input_channels: Define the number of input channels for the data (e.g., grayscale=1, RGB=3).
• feature_size: Set the dimension of the feature vectors extracted by the model.
• use_checkpoint: Enable or disable the use of model checkpoints for training continuation.
• learning_rate: Set the initial learning rate for the optimizer.
• weight_decay: Define the weight decay (L2 penalty) rate for regularization.
• max_iterations: Specify the maximum number of iterations (or training steps).
• eval_num: Set the frequency of evaluations (every eval_num iterations) during training.

python3.10 main.py  

If any part of this code is used, please give appropriate citations to our paper.

If you have any questions, please email [email protected] to discuss with the authors.