Channel prior convolutional attention for medical image segmentation

by Hejun Huang, Zuguo Chen*, Ying Zou, Ming Lu, Chaoyang Chen

Introduction

This repository is for our paper 'Channel prior convolutional attention for medical image segmentation'.

Installation

git clone https://github.com/Cuthbert-Huang/CPCANet
cd CPCANet
conda env create -f environment.yml
source activate CPCANet
pip install -e .

Data-Preparation

CPCANet is a 2D based network, and all data should be expressed in 2D form with .nii.gz format. You can download the organized dataset from the link or download the original data from the link below. If you need to convert other formats (such as .jpg) to the .nii.gz, you can look up the file and modify the file based on your own datasets.

Dataset I ACDC

Dataset II ISIC2016, PH2

The dataset should be finally organized as follows:

./DATASET/
  ├── nnUNet_raw/
      ├── nnUNet_raw_data/
          ├── Task01_ACDC/
              ├── imagesTr/
              ├── imagesTs/
              ├── labelsTr/
              ├── labelsTs/
              ├── dataset.json
              ├── evaulate.py

          ├── Task02_Isic/
              ├── imagesTr/
              ├── imagesTs/
              ├── labelsTr/
              ├── labelsTs/
              ├── dataset.json
              ├── evaulate.py              
          ......
      ├── nnUNet_cropped_data/
  ├── nnUNet_trained_models/
  ├── nnUNet_preprocessed/

One thing you should be careful of is that folder imagesTr contains both training set and validation set, and correspondingly, the value of numTraining in dataset.json equals the case number in the imagesTr. The division of the training set and validation set will be done in the network configuration located at nnunet/network_configuration/config.py.

The evaulate.py is used for calculating the evaulation metrics and can be found in the link of the organized datasets or you can write it by yourself. The existing of evaulate.py will not affect the data preprocessing, training and testing.

Data-Preprocessing

nnUNet_convert_decathlon_task -i path/to/nnUNet_raw_data/Task01_ACDC

This step will convert the name of folder from Task01 to Task001, and make the name of each nifti files end with '_000x.nii.gz'.

nnUNet_plan_and_preprocess -t 1

Where -t 1 means the command will preprocess the data of the Task001_ACDC. Before this step, you should set the environment variables to ensure the framework could know the path of nnUNet_raw, nnUNet_preprocessed, and nnUNet_trained_models. The detailed construction can be found in nnUNet.

Training

If you want to train CPCANet on ACDC.

bash train_cpcanet_acdc.sh

If you want to train CPCANet on ISIC.

bash train_cpcanet_isic.sh

Testing

The trained model is placed at this link for model testing.

If you want to test CPCANet on ACDC.

bash test_cpcanet_acdc.sh

If you want to test CPCANet on ISIC.

bash test_cpcanet_isic.sh

Acknowledgements

Our code is origin from nnUNet and UNET-2022. Thanks to these authors for their excellent work.