base.py 文件源码

def select_voxels_from_previous_model(model, train_x_data, options):
    """
    Select training voxels from image segmentation masks 

    """

    # get_scan names and number of modalities used 
    scans = train_x_data.keys()
    modalities = train_x_data[scans[0]].keys()

    # select voxels for training. Discard CSF and darker WM in FLAIR. 
    # flair_scans = [train_x_data[s]['FLAIR'] for s in scans]
    # selected_voxels = select_training_voxels(flair_scans, options['min_th'])

    # evaluate training scans using the learned model and extract voxels with probability higher than 0.5
    seg_mask  = [test_scan(model, dict(train_x_data.items()[s:s+1]), options, save_nifti = False) > 0.5 for s in range(len(scans))]

    # check candidate segmentations:
    # if no voxels have been selected, return candidate voxels on FLAIR modality > 2
    flair_scans = [train_x_data[s]['FLAIR'] for s in scans]
    images = [load_nii(name).get_data() for name in flair_scans]
    images_norm = [(im.astype(dtype=np.float32) - im[np.nonzero(im)].mean()) / im[np.nonzero(im)].std() for im in images]
    seg_mask = [im > 2 if np.sum(seg) == 0 else seg for im, seg in zip(images_norm, seg_mask)]

    return seg_mask