BTS_DP_MRI
Brain Tumor Segmentation Using Diffusion and Perfusion MRI
MIT License
Authors
- Development: Milad Sadeghi DM
Sample Pipeline
Pre-processing:
Desired dataset structure: Input data shape: 64x64x10
.
Train
100
mask_100.nii
MD_100.nii
rCBV_100.nii
101
mask_101.nii
MD_101.nii
rCBV_101.nii
Do preprocessing on dataset:
python preprocessing.py --verbose --dataset <path to dataset directory>
This will create a new directory called npy_files, then creates specific folders for each sample we have in dataset and saves image.npy and mask.py files in these folders.
- Output data shape:
- Image: 64x64x16x2
- Mask: 64x64x16x3
npy_files/
0
mask_0.npy
image_0.npy
1
mask_1.npy
image_1.npy
Training the model:
python train.py --verbose --dataset <path to npy_files> --learning_rate 0.0001 --batch_size 2 --epochs 100 --save ./
After completing training process, command above will save entire trained model as a .pb file , save training process history details as a .csv file and plot training process diagrams.
Build report file:
As we mentioned earlier, train script saves training process history details. The reporter.py file drops unnecessary rows and columns and calculates f1_score value for both training and validation.
python reporter.py -v --file_path <path to log_file.csv> --save <path to save new .csv file>
Results
Train:
| Fold | Accuracy | IOU Score | Loss | Precision | Recall | F1-Score |
|---|---|---|---|---|---|---|
| 1 | 0.968775 | 0.897440 | -1.903993 | 0.968788 | 0.968766 | 0.968777 |
| 2 | 0.970905 | 0.903380 | -1.730110 | 0.970907 | 0.970904 | 0.970905 |
| 3 | 0.972417 | 0.907598 | -1.769276 | 0.972422 | 0.972415 | 0.972419 |
| 4 | 0.972910 | 0.910098 | -1.937584 | 0.972916 | 0.972903 | 0.972909 |
| 5 | 0.973350 | 0.912430 | -1.882656 | 0.973355 | 0.973342 | 0.973349 |
| 6 | 0.972398 | 0.907724 | -1.793769 | 0.972407 | 0.972395 | 0.972401 |
| 7 | 0.973002 | 0.905900 | -1.848724 | 0.973010 | 0.972997 | 0.973004 |
| 8 | 0.971803 | 0.906942 | -1.939411 | 0.971807 | 0.971801 | 0.971804 |
Validation:
| Fold | Accuracy | IOU Score | Loss | Precision | Recall | F1-Score |
|---|---|---|---|---|---|---|
| 1 | 0.912868 | 0.674202 | -1.214981 | 0.912912 | 0.912853 | 0.912882 |
| 2 | 0.908874 | 0.599501 | -0.614096 | 0.908877 | 0.908874 | 0.908876 |
| 3 | 0.900650 | 0.637642 | -0.932565 | 0.900656 | 0.900642 | 0.900649 |
| 4 | 0.903141 | 0.668193 | -1.140507 | 0.903154 | 0.903133 | 0.903143 |
| 5 | 0.890136 | 0.610992 | -0.862575 | 0.890166 | 0.890125 | 0.890145 |
| 6 | 0.893863 | 0.612036 | -0.745553 | 0.893863 | 0.893863 | 0.893863 |
| 7 | 0.895732 | 0.613254 | -0.769792 | 0.895738 | 0.895725 | 0.895732 |
| 8 | 0.885597 | 0.618338 | -0.897051 | 0.885608 | 0.885578 | 0.885593 |
Making a prediction
First you have to do preprocess on your sample different modalities and generate image.nii and mask.npy and then run predict.py script:
python predict.py --model <path to saved model directory> --image <path to preprocessed .npy image file>
--mask <path to preprocessed .npy mask file> --verbose --save_plot --slice 6
Sample output:
License
This project licensed under the MIT License - see the LICENSE file for more details.