Exemple #1
0
def muti_bundle_registration(paths_file, pts=12):
    """
    muti-bundle registration and consolidation
    Parameters
    ----------
    paths_file: list; muti_bundle file path
    pts: each streamline is divided into sections

    Return
    ------
    new header: include id of each streamline that comes from different subjects
    registration and consolidation bundle
    """
    fas = Fasciculus(paths_file[0])
    # print fas.get_header()
    bundle_header = {'fasciculus_id': None}
    sub1 = fas.get_data()
    bundle_header['fasciculus_id'] = len(sub1) * [
        int(paths_file[0].split('/')[9])
    ]
    sub2 = Fasciculus(paths_file[1]).get_data()
    subj2_aligned = bundle_registration(sub1, sub2, pts=pts)
    bundle = fas.fib_merge(sub1, subj2_aligned)
    bundle_header['fasciculus_id'] += (
        len(bundle) - len(sub1)) * [int(paths_file[1].split('/')[9])]
    # print bundle_header
    # print len(bundle)
    for index in range(len(paths_file))[2:]:
        # print paths_file[index]
        sub = Fasciculus(paths_file[index]).get_data()
        sub_aligned = bundle_registration(sub1, sub, pts=pts)
        lenth = len(bundle)
        bundle = fas.fib_merge(bundle, sub_aligned)
        bundle_header['fasciculus_id'] += (
            len(bundle) - lenth) * [int(paths_file[index].split('/')[9])]

    fas.update_header(bundle_header)
    fas.set_data(nibas.ArraySequence(bundle))

    return fas
Exemple #2
0
def cc_seg_pipeline(tck_path, mask, geo_path, thr=5., roi_id=None, step=0):
    """
    Extract occipital streamlines and separate hemisphere streamlines
    Parameters
    ----------
    tck_path: list; streamline file path (.tck)
    mask: surface mask (xh.aparc.annot)
    geo_path: geometry data path (subject_id.L.white.native.surf.gii)
    thr: distance of streamline terminus to surface

    Return
    ------
    occipital streamlines
    """
    # load data
    fasciculus = Fasciculus(tck_path)

    # extract streamlines from left to right hemisphere
    fibselection = FibSelection(fasciculus)
    L_temp_need0 = fibselection.endpoint_dissimilarity()
    fasciculus.set_data(L_temp_need0)
    L_temp_need1 = fibselection.single_point_mid_sag()
    fasciculus.set_data(L_temp_need1)
    del L_temp_need0, L_temp_need1
    gc.collect()

    # separation of lr_seed fib
    fib = fasciculus.hemi_fib_separation()

    # choose the mask: Occipital
    mask_fib = []
    for i in range(len(fib)):
        vertices, colortable, label = nib.freesurfer.read_annot(mask[i])
        gii_data = nib.load(geo_path[i]).darrays
        coords, faces = gii_data[0].data, gii_data[1].data

        label_value = np.array(len(coords) * [0])
        if not roi_id:
            label_value[vertices == 11] = 11  # lateraloccipital
            label_value[vertices == 13] = 13  # lingual
            label_value[vertices == 5] = 5  # cuneus
            label_value[vertices == 21] = 21  # pericalcarine
        else:
            for value in roi_id:
                label_value[vertices == value] = value

        # extract fib using mask
        streamlines = fib[i]
        streamlines = fasciculus.sort_streamlines(streamlines)

        if i == 0:
            s0 = [s[0] for s in streamlines]
        else:
            s0 = [s[-1] for s in streamlines]

        stream_terminus = np.array(s0)
        dist = cdist(coords[label_value > 0], stream_terminus)
        stream_index = np.array(len(streamlines) * [False])
        for j in range(len(dist[:])):
            temp_index = np.array(dist[j] <= thr)
            stream_index += temp_index

        rois_streamlines = streamlines[stream_index]
        mask_fib.append(rois_streamlines)

    # lr fib merge
    fib_merge = fasciculus.fib_merge(mask_fib[0], mask_fib[1])
    del mask_fib
    gc.collect()
    fib_hemi = fasciculus.separation_fib_to_hemi(fib_merge)
    del fib_merge
    gc.collect()

    return fib_hemi