A lightweight asymmetric U-Net based framework to leverage acute ischemic stroke lesion segmentation in CT and CTP images
"The Presented work is trained as well as tested on ISLES2018 challenge dataset"
Dataset: ISLES2018 Challange Dataset (https://www.smir.ch/ISLES/Start2018)
System: Graphics Enable
Environment: Anaconda--> Spyder(Python3.8)
Library: 1. Tensorflow 2.3
2. Tensorboard 2.3
3. numpy 1.18.5
4. skimage 0.16.2
5. h5py 2.10.0
6. glob 0.7
1. Training_model.py: Proposed model file
Called By: Prediction.py
Training.py
2. pre_processinig.py: Function required fro pre_processing the data before training and prediction
Called By: Prediction.py
Training_Data.py
3.Training_Data.py: It will generate training dataset From ISLES2018 Training dataset(Change Line
no 11 accordining to training data directory) and save it as:-
1. "GT_Whole_RN16_ISLES2018_F0.hdf5"--> Fold0
2. "GT_Whole_RN16_ISLES2018_F1.hdf5"--> Fold1
3. "GT_Whole_RN16_ISLES2018_F2.hdf5"--> Fold2
4. "GT_Whole_RN16_ISLES2018_F3.hdf5"--> Fold3
5. "GT_Whole_RN16_ISLES2018_F4.hdf5"--> Fold4
4. Training.py: For training, Training Weight will be saved in folder "Module_Weight" folder for each fold.
5. Prediction.py: For prediction: change
line no: 16 for Training/Testing Dataset path
line no: 17 for Destination path of predicted data on Training/Testining.
Trained weight is available in folder "pre_trained_weight" and "Module_Weight"
Download the Dataset from the link provided in Dataset part by completing the registration process and place it in the current directory"