AI-DCNN-IP / Stroke_lesion_Seg

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Stroke-Lesion-Segmentation

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)

Requirement:

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

File Description:

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"