Source code for nighres.brain.mgdm_segmentation

import numpy as np
import nibabel as nb
import os
import sys
import nighresjava
from import load_volume, save_volume
from ..utils import _output_dir_4saving, _fname_4saving, \
                    _check_topology_lut_dir, _check_atlas_file, \

def _get_mgdm_orientation(affine, mgdm):
    Transforms nibabel affine information into
    orientation and slice order that MGDM understands
    orientation = nb.aff2axcodes(affine)
    # set mgdm slice order
    if orientation[-1] == "I" or orientation[-1] == "S":
        sliceorder = mgdm.AXIAL
    elif orientation[-1] == "L" or orientation[-1] == "R":
        sliceorder = mgdm.SAGITTAL
        sliceorder = mgdm.CORONAL

    # set mgdm orientations
    if "L" in orientation:
        LR = mgdm.R2L
    elif "R" in orientation:
        LR = mgdm.L2R  # flipLR = True
    if "A" in orientation:
        AP = mgdm.P2A  # flipAP = True
    elif "P" in orientation:
        AP = mgdm.A2P
    if "I" in orientation:
        IS = mgdm.S2I  # flipIS = True
    elif "S" in orientation:
        IS = mgdm.I2S

    return sliceorder, LR, AP, IS

def _get_mgdm_intensity_priors(atlas_file):
    Returns a list of available as intensity priors
    in the MGDM atlas that you are using
    priors = []
    with open(atlas_file) as fp:
        for i, line in enumerate(fp):
            if "Structures:" in line:  # this is the beginning of the LUT
                lut_idx = i
                lut_rows = list(map(int, [line.split()[1]]))[0]
            if "Intensity Prior:" in line:
    return priors

[docs]def mgdm_segmentation(contrast_image1, contrast_type1, contrast_image2=None, contrast_type2=None, contrast_image3=None, contrast_type3=None, contrast_image4=None, contrast_type4=None, n_steps=5, max_iterations=800, topology='wcs', atlas_file=None, topology_lut_dir=None, adjust_intensity_priors=False, normalize_qmaps=True, compute_posterior=False, posterior_scale=5.0, diffuse_probabilities=False, save_data=False, overwrite=False, output_dir=None, file_name=None): """ MGDM segmentation Estimates brain structures from an atlas for MRI data using a Multiple Object Geometric Deformable Model (MGDM) Parameters ---------- contrast_image1: niimg First input image to perform segmentation on contrast_type1: str Contrast type of first input image, must be listed as a prior in used atlas(specified in atlas_file). Possible inputs by default are DWIFA3T, DWIMD3T, T1map9T, Mp2rage9T, T1map7T, Mp2rage7T, PV, Filters, T1pv, Mprage3T, T1map3T, Mp2rage3T, HCPT1w, HCPT2w, NormMPRAGE. contrast_image2: niimg, optional Additional input image to inform segmentation, must be in the same space as constrast_image1, requires contrast_type2 contrast_type2: str, optional Contrast type of second input image, must be listed as a prior in used atlas (specified in atlas_file). Possible inputs by default are the same as with parameter contrast_type1 (see above). contrast_image3: niimg, optional Additional input image to inform segmentation, must be in the same space as constrast_image1, requires contrast_type3 contrast_type3: str, optional Contrast type of third input image, must be listed as a prior in used atlas (specified in atlas_file). Possible inputs by default are the same as with parameter contrast_type1 (see above). contrast_image4: niimg, optional Additional input image to inform segmentation, must be in the same space as constrast_image1, requires contrast_type4 contrast_type4: str, optional Contrast type of fourth input image, must be listed as a prior in used atlas (specified in atlas_file). Possible inputs by default are the same as with parameter contrast_type1 (see above). n_steps: int, optional Number of steps for MGDM (default is 5, set to 0 for quick testing of registration of priors, which does not perform true segmentation) max_iterations: int, optional Maximum number of iterations per step for MGDM (default is 800, set to 1 for quick testing of registration of priors, which does not perform true segmentation) topology: {'wcs', 'no'}, optional Topology setting, choose 'wcs' (well-composed surfaces) for strongest topology constraint, 'no' for no topology constraint (default is 'wcs') atlas_file: str, optional Path to plain text atlas file (default is stored in DEFAULT_ATLAS) or atlas name to be searched in ATLAS_DIR topology_lut_dir: str, optional Path to directory in which topology files are stored (default is stored in TOPOLOGY_LUT_DIR) normalize_qmaps: bool Normalize quantitative maps into [0,1] (default is False) adjust_intensity_priors: bool Adjust intensity priors based on dataset (default is False) normalize_qmaps: bool Normalize quantitative maps in [0,1] (default in True, change this if using one of the -quant atlas text files in ATLAS_DIR) compute_posterior: bool Compute posterior probabilities for segmented structures (default is False) posterior_scale: float Posterior distance scale from segmented structures to compute posteriors (default is 5.0 mm) diffuse_probabilities: bool Regularize probability distribution with a non-linear diffusion scheme (default is False) save_data: bool Save output data to file (default is False) overwrite: bool Overwrite existing results (default is False) output_dir: str, optional Path to desired output directory, will be created if it doesn't exist file_name: str, optional Desired base name for output files with file extension (suffixes will be added) Returns ---------- dict Dictionary collecting outputs under the following keys (suffix of output files in brackets) * segmentation (niimg): Hard brain segmentation with topological constraints (if chosen) (_mgdm_seg) * labels (niimg): Maximum tissue probability labels (_mgdm_lbls) * memberships (niimg): Maximum tissue probability values, 4D image where the first dimension shows each voxel's highest probability to belong to a specific tissue, the second dimension shows the second highest probability to belong to another tissue etc. (_mgdm_mems) * distance (niimg): Minimum distance to a segmentation boundary (_mgdm_dist) Notes ---------- Original Java module by Pierre-Louis Bazin. Algorithm details can be found in [1]_ and [2]_ References ---------- .. [1] Bazin et al. (2014). A computational framework for ultra-high resolution cortical segmentation at 7 Tesla. doi: 10.1016/j.neuroimage.2013.03.077 .. [2] Bogovic et al. (2013). A multiple object geometric deformable model for image segmentation. doi:10.1016/j.cviu.2012.10.006.A """ print('\nMGDM Segmentation') # check atlas_file and set default if not given atlas_file = _check_atlas_file(atlas_file) # check topology_lut_dir and set default if not given topology_lut_dir = _check_topology_lut_dir(topology_lut_dir) # find available intensity priors in selected MGDM atlas mgdm_intensity_priors = _get_mgdm_intensity_priors(atlas_file) # sanity check contrast types contrasts = [contrast_image1, contrast_image2, contrast_image3, contrast_image4] ctypes = [contrast_type1, contrast_type2, contrast_type3, contrast_type4] for idx, ctype in enumerate(ctypes): if ctype is None and contrasts[idx] is not None: raise ValueError(("If specifying contrast_image{0}, please also " "specify contrast_type{0}".format(idx+1, idx+1))) elif ctype is not None and ctype not in mgdm_intensity_priors: raise ValueError(("{0} is not a valid contrast type for " "contrast_type{1} please choose from the " "following contrasts provided by the chosen " "atlas: ").format(ctype, idx+1), ", ".join(mgdm_intensity_priors)) # make sure that saving related parameters are correct if save_data: output_dir = _output_dir_4saving(output_dir, contrast_image1) seg_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=contrast_image1, suffix='mgdm-seg', )) lbl_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=contrast_image1, suffix='mgdm-lbls')) mems_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=contrast_image1, suffix='mgdm-mems')) dist_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=contrast_image1, suffix='mgdm-dist')) if overwrite is False \ and os.path.isfile(seg_file) \ and os.path.isfile(lbl_file) \ and os.path.isfile(mems_file) \ and os.path.isfile(dist_file) : print("skip computation (use existing results)") output = { 'segmentation': seg_file, 'labels': lbl_file, 'memberships': mems_file, 'distance': dist_file } return output # start virtual machine, if not already running try: mem = _check_available_memory() nighresjava.initVM(initialheap=mem['init'], maxheap=mem['max']) except ValueError: pass # create mgdm instance mgdm = nighresjava.BrainMgdmMultiSegmentation2() # set mgdm parameters mgdm.setAtlasFile(atlas_file) mgdm.setTopologyLUTdirectory(topology_lut_dir) mgdm.setOutputImages('label_memberships') mgdm.setAdjustIntensityPriors(adjust_intensity_priors) mgdm.setComputePosterior(compute_posterior) mgdm.setPosteriorScale_mm(posterior_scale) mgdm.setDiffuseProbabilities(diffuse_probabilities) mgdm.setSteps(n_steps) mgdm.setMaxIterations(max_iterations) mgdm.setTopology(topology) mgdm.setNormalizeQuantitativeMaps(normalize_qmaps) # set to False for "quantitative" brain prior atlases # (version quant-3.0.5 and above) # load contrast image 1 and use it to set dimensions and resolution img = load_volume(contrast_image1) data = img.get_data() affine = img.affine header = img.header resolution = [x.item() for x in header.get_zooms()] dimensions = data.shape mgdm.setDimensions(dimensions[0], dimensions[1], dimensions[2]) mgdm.setResolutions(resolution[0], resolution[1], resolution[2]) # convert orientation information to mgdm slice and orientation info sliceorder, LR, AP, IS = _get_mgdm_orientation(affine, mgdm) mgdm.setOrientations(sliceorder, LR, AP, IS) # input image 1 mgdm.setContrastImage1(nighresjava.JArray('float')( (data.flatten('F')).astype(float))) mgdm.setContrastType1(contrast_type1) # if further contrast are specified, input them if contrast_image2 is not None: data = load_volume(contrast_image2).get_data() mgdm.setContrastImage2(nighresjava.JArray('float')( (data.flatten('F')).astype(float))) mgdm.setContrastType2(contrast_type2) if contrast_image3 is not None: data = load_volume(contrast_image3).get_data() mgdm.setContrastImage3(nighresjava.JArray('float')( (data.flatten('F')).astype(float))) mgdm.setContrastType3(contrast_type3) if contrast_image4 is not None: data = load_volume(contrast_image4).get_data() mgdm.setContrastImage4(nighresjava.JArray('float')( (data.flatten('F')).astype(float))) mgdm.setContrastType4(contrast_type4) # execute MGDM try: mgdm.execute() except: # if the Java module fails, reraise the error it throws print("\n The underlying Java code did not execute cleanly: ") print(sys.exc_info()[0]) raise return # reshape output to what nibabel likes seg_data = np.reshape(np.array(mgdm.getSegmentedBrainImage(), dtype=np.int32), dimensions, 'F') dist_data = np.reshape(np.array(mgdm.getLevelsetBoundaryImage(), dtype=np.float32), dimensions, 'F') # membership and labels output has a 4th dimension, set to 6 dimensions4d = [dimensions[0], dimensions[1], dimensions[2], 6] lbl_data = np.reshape(np.array(mgdm.getPosteriorMaximumLabels4D(), dtype=np.int32), dimensions4d, 'F') mems_data = np.reshape(np.array(mgdm.getPosteriorMaximumMemberships4D(), dtype=np.float32), dimensions4d, 'F') # adapt header max for each image so that correct max is displayed # and create nifiti objects header['cal_max'] = np.nanmax(seg_data) seg = nb.Nifti1Image(seg_data, affine, header) header['cal_max'] = np.nanmax(dist_data) dist = nb.Nifti1Image(dist_data, affine, header) header['cal_max'] = np.nanmax(lbl_data) lbls = nb.Nifti1Image(lbl_data, affine, header) header['cal_max'] = np.nanmax(mems_data) mems = nb.Nifti1Image(mems_data, affine, header) if save_data: save_volume(seg_file, seg) save_volume(dist_file, dist) save_volume(lbl_file, lbls) save_volume(mems_file, mems) output = { 'segmentation': seg_file, 'labels': lbl_file, 'memberships': mems_file, 'distance': dist_file } else: output = { 'segmentation': seg, 'labels': lbls, 'memberships': mems, 'distance': dist } return output