基于深度学习的心脏分割和心脏疾病诊断内含数据集和环境运行说明.zip

# Automated-Cardiac-Segmentation-and-Disease-Diagnosis ## Introduction This repository contains the reference implementation for automated cardiac segmentation and diasease classification introduced in the following paper: "Fully Convolutional Multi-scale Residual DenseNets for Cardiac Segmentation and Automated Cardiac Diagnosis using Ensemble of Classifiers" https://doi.org/10.1016/j.media.2018.10.004 ### Citation If you find this reference implementation useful in your research, please consider citing: ``` @article{khened2019fully, title={Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers}, author={Khened, Mahendra and Kollerathu, Varghese Alex and Krishnamurthi, Ganapathy}, journal={Medical image analysis}, volume={51}, pages={21--45}, year={2019}, publisher={Elsevier} } ``` ## Usage ### ACDC Data Preparation 1. Register and download ACDC-2017 dataset from https://www.creatis.insa-lyon.fr/Challenge/acdc/index.html 2. Create a folder outside the project with name **ACDC_DataSet** and copy the dataset. 3. From the project folder open file data_preprocess/acdc_data_preparation.py. 4. In the file, set the path to ACDC training dataset is pointed as: ```complete_data_path = '../../ACDC_DataSet/training' ```. 5. Run the script acdc_data_preparation.py. 6. The processed data for training is generated outside the project folder named *processed_acdc_dataset*. ### Steps to train the model: 1. From the project folder open file estimators/train.py and configure the network hyper-parameters. 2. From the project folder open file estimators/config.py and configure the training hyper-parameters. 3. Run the script train.py. 4. Outside the project in the folder named *trained_models/ACDC/* the model weights and tensorboard summary are saved. 5. While training the training summary can be accessed running: ```tensorboard --logdir='path_to/trained_models/ACDC/FCRD_ACDC/summary' ```. ### Steps to test the model: 1. From the project folder open file estimators/test.py and configure the testing hyper-parameters, path to trained model weights and ACDC-2017 testing dataset. 2. Run the script test.py. 3. The predictions are saved in the path *trained_models/ACDC/FCRD_ACDC/predictions_TIMESTAMP* ### Cardiac Diagnosis 1. Extract Features from the training dataset by running: generate_cardiac_features_train.py 2. Extract Features from the testing dataset by running: generate_cardiac_features_test.py 3. Run the scripts stage_1_diagnosis.py and stage_2_diagnosis.py in sequence 4. The final cardiac disease prediction results on the test set are generated in the *prediction* folder