base.py 文件源码

def test_scan(model, test_x_data, options, save_nifti= True, candidate_mask = None):
    """
    Test data based on one model 
    Input: 
    - test_x_data: a nested dictionary containing training image paths: 
            train_x_data['scan_name']['modality'] = path_to_image_modality
    - save_nifti: save image segmentation 
    - candidate_mask: a binary masks containing voxels to classify

    Output:
    - test_scan = Output image containing the probability output segmetnation 
    - If save_nifti --> Saves a nifti file at specified location options['test_folder']/['test_scan']
    """

    # get_scan name and create an empty nifti image to store segmentation
    scans = test_x_data.keys()
    flair_scans = [test_x_data[s]['FLAIR'] for s in scans]
    flair_image = load_nii(flair_scans[0])
    seg_image = np.zeros_like(flair_image.get_data())


    # compute lesion segmentation in batches of size options['batch_size'] 
    for batch, centers in load_test_patches(test_x_data, options['patch_size'], options['batch_size'], candidate_mask):
        y_pred = model.predict_proba(np.squeeze(batch))
        [x, y, z] = np.stack(centers, axis=1)
        seg_image[x, y, z] = y_pred[:, 1]

    if save_nifti:
        out_scan = nib.Nifti1Image(seg_image, affine=flair_image.affine)
        out_scan.to_filename(os.path.join(options['test_folder'], options['test_scan'], options['experiment'], options['test_name']))
        #out_scan.to_filename(os.path.join(test_folder, scan, options['experiment'], options['test_name']))

    return seg_image