test.md

Model preidiciton of the L3 top slice

The selection model is tested with the test_selection.py script in the 'src' directory. The script takes a NIFTI image and output in csv the predicted number of the L3 slice. You can run the basic test routine by passing the two required arguments:

data_dir -- Directory in which the test ct images are stored. Default path is '../data/test/input'

model_dir -- Directory in which trained model are stored. Default model is 'model/test/L3_Top_Selection_Model_Weight.h5'

For example:

$ python test_selection.py 

We can check the selection performance by overlaping the prediction slice into the input CT series, in this script

Model segmentaion of the L3 top slice

The segmentation model is tested with the test_segmentation.py script in 'src' directory. The script takes a NIFTI image and L3 top slice and output the segmented CT scanin NIFTI format. You can run the basic test routine by passing required arguments:

data_dir -- Directory in which the test ct images, labels and automatic segmentations are stored. Default path is '../data/test/input'

model_dir -- Directory in which well-trained model are stored. Default model is 'model/test/L3_Top_Segmentation_Model_Weight.hdf5'

For example:

$ python test_segmentation.py 

We can check the segmentation performance by overlaping the model segmentation into the input L3 slice, in this script

Data Structure for testing models

Before test the model you must prepare the data in NIFTI format. The files should be placed within data directory with structure below :

- data/

|- test/

|  |- input/
|  |  |- test-volume-11.nii.gz
|  |  |- test-volume-8.nii.gz

|  |- output_segmentation/
|  |  |- test-volume-11_AI_seg_L3.nii.gz
|  |  |- test-volume-8_AI_seg_L3.nii.gz


|  |- output_csv/

|  |  |- L3_Top_Slice_Prediction.csv
|  |  |- L3_body_comp_area_density.csv

Example Data Source

https://competitions.codalab.org/competitions/17094#learn_the_details-overview

Under licence of https://creativecommons.org/licenses/by-nc-nd/4.0/