3D Mutil-model segmentation algorithm

Installation

1. First, clone this repository and create the environment.

conda create -n moe python=3.10.13
conda activate moe

Note: Before proceeding to the next step, please ensure that CUDA-11.8 is installed and its path has been added to the environment variable.

# Check CUDA version
nvcc -V  # Should return CUDA version 11.8

2. Install dependencies and the mamba package.

# Install PyTorch with CUDA 11.8
pip install torch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 --index-url https://download.pytorch.org/whl/cu118
pip install torch==2.7.0 torchvision==0.22.0 torchaudio==2.7.0 --index-url https://download.pytorch.org/whl/cu126



# Install dependencies
pip install -r requirements.txt

Usage

Main directories and files are listed below:

.
├── configs  # Training-related configurations
│   ├── datasets  # Dataset configurations
│   ├── models  # Model configurations
│   └── trainers  # Training hyperparameters
├── networks  # Comparison models
│   ├── ...
│   └── unetr.py
├── runs  # Logs and checkpoints save root
├── scripts  # Training/testing/parameter calculation scripts
│   ├── ...
│   ├── calc_params_flops.sh
│   ├── test_synapse.sh
│   └── train_synapse.sh
├── train.py  
├── test.py  
└── main.py  

It's easy to train and test using the provided scripts and configurations.

# Example command to run the script
sh scripts/train_synapse.sh
# or
sh scripts/test_synapse.sh

sh scripts/train_DLCBL.sh sh scripts/train_auto_lym.sh sh scripts/train_HL.sh

5-fold cross validation (nnUNet style)

For datasets with all labeled cases listed in dataset.json (training and/or validation), you can run one fold with:

python main.py --data_config Dataset800_HL --model_config maanet --trainer_config sgd_trainer_1_batch --fold 0

• --fold: fold index in [0, 4]
• --num_folds: default 5
• --splits_file: default splits_final.json (auto-generated in dataset folder if not found)

Run all folds by looping in shell:

for fold in 0 1 2 3 4; do
	python main.py --data_config Dataset800_HL --model_config maanet --trainer_config sgd_trainer_1_batch --fold "${fold}" --logdir hl_maanet_5fold
done

'''仅当前仓库生效：git config pull.rebase true
全局生效：git config --global pull.rebase true'''

Overview

Acknowledgment