Source code for nighres.cortex.cruise_cortex_extraction

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, \

[docs]def cruise_cortex_extraction(init_image, wm_image, gm_image, csf_image, vd_image=None, data_weight=0.4, regularization_weight=0.1, max_iterations=500, normalize_probabilities=False, correct_wm_pv=True, wm_dropoff_dist=1.0, topology='wcs', topology_lut_dir=None, save_data=False, overwrite=False, output_dir=None, file_name=None): """ CRUISE cortex extraction Segments the cortex from a whole brain segmented data set with the CRUISE method (includes customized partial voluming corrections and the Anatomically-Consistent Enhancement (ACE) of sulcal fundi). Note that the main input images are generated by the nighres module :func:`nighres.brain.extract_brain_region`. Parameters ---------- init_image: niimg Initial white matter (WM) segmentation mask (binary mask>0 inside WM) wm_image: niimg Filled WM probability map (values in [0,1], including subcortical GM and ventricles) gm_image: niimg Cortical gray matter (GM) probability map (values in [0,1], highest inside the cortex) csf_image: niimg Sulcal cerebro-spinal fluid (CSf) and background probability map (values in [0,1], highest in CSf and masked regions) vd_image: niimg, optional Additional probability map of vessels and dura mater to be excluded data_weight: float Weighting of probability-based balloon forces in CRUISE (default 0.4, sum of {data_weight,regularization_weight} should be below or equal to 1) regularization_weight: float Weighting of curvature regularization forces in CRUISE (default 0.1, sum of {data_weight,regularization_weight} should be below or equal to 1) max_iterations: int Maximum number of iterations in CRUISE (default is 500) normalize_probabilities: bool Whether to normalize the wm, gm, and csf probabilities (default is False) correct_wm_pv: bool Whether to correct for WM partial voluming in gyral crowns (default is True) wm_dropoff_dist: float Distance parameter to lower WM probabilities away from current segmentation (default is 1.0 voxel) topology: {'wcs', 'no'} Topology setting, choose 'wcs' (well-composed surfaces) for strongest topology constraint, 'no' for no topology constraint (default is 'wcs') topology_lut_dir: str Path to directory in which topology files are stored (default is stored in TOPOLOGY_LUT_DIR) 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) * cortex (niimg): Hard segmentation of the cortex with labels background=0, gm=1, and wm=2 (_cruise_cortex) * gwb (niimg): Gray-White matter Boundary (GWB) level set function (_cruise_gwb) * cgb (niimg): CSF-Gray matter Boundary (CGB) level set function (_cruise_cgb) * avg (niimg): Central level set function, obtained as geometric average of GWB and CGB (*not* the middle depth of the cortex, use volumetric_layering if you want accurate depth measures) (_cruise-avg) * thickness (niimg): Simple cortical thickness estimate: distance to the GWB and CGB surfaces, in mm (_cruise-thick) * pwm (niimg): Optimized WM probability, including partial volume and distant values correction (_cruise-pwm) * pgm (niimg): Optimized GM probability, including CSF sulcal ridges correction (_cruise_pgm) * pcsf (niimg): Optimized CSF probability, including sulcal ridges and vessel/dura correction (_cruise-pwm) Notes ---------- Original algorithm by Xiao Han. Java module by Pierre-Louis Bazin. Algorithm details can be found in [1]_ References ---------- .. [1] X. Han, D.L. Pham, D. Tosun, M.E. Rettmann, C. Xu, and J. L. Prince, CRUISE: Cortical Reconstruction Using Implicit Surface Evolution, NeuroImage, vol. 23, pp. 997--1012, 2004 """ print('\nCRUISE Cortical Extraction') # check topology_lut_dir and set default if not given topology_lut_dir = _check_topology_lut_dir(topology_lut_dir) # make sure that saving related parameters are correct if save_data: output_dir = _output_dir_4saving(output_dir, gm_image) cortex_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=gm_image, suffix='cruise-cortex', )) gwb_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=gm_image, suffix='cruise-gwb', )) cgb_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=gm_image, suffix='cruise-cgb', )) avg_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=gm_image, suffix='cruise-avg', )) thick_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=gm_image, suffix='cruise-thick', )) pwm_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=gm_image, suffix='cruise-pwm', )) pgm_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=gm_image, suffix='cruise-pgm', )) pcsf_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=gm_image, suffix='cruise-pcsf', )) if overwrite is False \ and os.path.isfile(cortex_file) \ and os.path.isfile(gwb_file) \ and os.path.isfile(cgb_file) \ and os.path.isfile(avg_file) \ and os.path.isfile(thick_file) \ and os.path.isfile(pwm_file) \ and os.path.isfile(pgm_file) \ and os.path.isfile(pcsf_file) : print("skip computation (use existing results)") output = {'cortex': cortex_file, 'gwb': gwb_file, 'cgb': cgb_file, 'avg': avg_file, 'thickness': thick_file, 'pwm': pwm_file, 'pgm': pgm_file, 'pcsf': pcsf_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 instance cruise = nighresjava.CortexOptimCRUISE() # set parameters cruise.setDataWeight(data_weight) cruise.setRegularizationWeight(regularization_weight) cruise.setMaxIterations(max_iterations) cruise.setNormalizeProbabilities(normalize_probabilities) cruise.setCorrectForWMGMpartialVoluming(correct_wm_pv) cruise.setWMdropoffDistance(wm_dropoff_dist) cruise.setTopology(topology) cruise.setTopologyLUTdirectory(topology_lut_dir) # load images init = load_volume(init_image) init_data = init.get_data() affine = init.affine header = init.header resolution = [x.item() for x in header.get_zooms()] dimensions = init_data.shape cruise.setDimensions(dimensions[0], dimensions[1], dimensions[2]) cruise.setResolutions(resolution[0], resolution[1], resolution[2]) cruise.importInitialWMSegmentationImage(nighresjava.JArray('int')( (init_data.flatten('F')).astype(int).tolist())) wm_data = load_volume(wm_image).get_data() cruise.setFilledWMProbabilityImage(nighresjava.JArray('float')( (wm_data.flatten('F')).astype(float))) gm_data = load_volume(gm_image).get_data() cruise.setGMProbabilityImage(nighresjava.JArray('float')( (gm_data.flatten('F')).astype(float))) csf_data = load_volume(csf_image).get_data() cruise.setCSFandBGProbabilityImage(nighresjava.JArray('float')( (csf_data.flatten('F')).astype(float))) if vd_image is not None: vd_data = load_volume(vd_image).get_data() cruise.setVeinsAndDuraProbabilityImage(nighresjava.JArray('float')( (vd_data.flatten('F')).astype(float))) # execute try: cruise.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 cortex_data = np.reshape(np.array(cruise.getCortexMask(), dtype=np.int32), dimensions, 'F') gwb_data = np.reshape(np.array(cruise.getWMGMLevelset(), dtype=np.float32), dimensions, 'F') cgb_data = np.reshape(np.array(cruise.getGMCSFLevelset(), dtype=np.float32), dimensions, 'F') avg_data = np.reshape(np.array(cruise.getCentralLevelset(), dtype=np.float32), dimensions, 'F') thick_data = np.reshape(np.array(cruise.getCorticalThickness(), dtype=np.float32), dimensions, 'F') pwm_data = np.reshape(np.array(cruise.getCerebralWMprobability(), dtype=np.float32), dimensions, 'F') pgm_data = np.reshape(np.array(cruise.getCorticalGMprobability(), dtype=np.float32), dimensions, 'F') pcsf_data = np.reshape(np.array(cruise.getSulcalCSFprobability(), dtype=np.float32), dimensions, 'F') # adapt header min, max for each image so that correct max is displayed # and create nifiti objects header['cal_min'] = np.nanmax(cortex_data) header['cal_max'] = np.nanmax(cortex_data) cortex = nb.Nifti1Image(cortex_data, affine, header) header['cal_min'] = np.nanmax(gwb_data) header['cal_max'] = np.nanmax(gwb_data) gwb = nb.Nifti1Image(gwb_data, affine, header) header['cal_min'] = np.nanmax(cgb_data) header['cal_max'] = np.nanmax(cgb_data) cgb = nb.Nifti1Image(cgb_data, affine, header) header['cal_min'] = np.nanmax(avg_data) header['cal_max'] = np.nanmax(avg_data) avg = nb.Nifti1Image(avg_data, affine, header) header['cal_min'] = np.nanmax(thick_data) header['cal_max'] = np.nanmax(thick_data) thickness = nb.Nifti1Image(thick_data, affine, header) header['cal_min'] = np.nanmax(pwm_data) header['cal_max'] = np.nanmax(pwm_data) pwm = nb.Nifti1Image(pwm_data, affine, header) header['cal_min'] = np.nanmax(pgm_data) header['cal_max'] = np.nanmax(pgm_data) pgm = nb.Nifti1Image(pgm_data, affine, header) header['cal_min'] = np.nanmax(pcsf_data) header['cal_max'] = np.nanmax(pcsf_data) pcsf = nb.Nifti1Image(pcsf_data, affine, header) if save_data: save_volume(cortex_file, cortex) save_volume(gwb_file, gwb) save_volume(cgb_file, cgb) save_volume(avg_file, avg) save_volume(thick_file, thickness) save_volume(pwm_file, pwm) save_volume(pgm_file, pgm) save_volume(pcsf_file, pcsf) return {'cortex': cortex_file, 'gwb': gwb_file, 'cgb': cgb_file, 'avg': avg_file, 'thickness': thick_file, 'pwm': pwm_file, 'pgm': pgm_file, 'pcsf': pcsf_file} else: return {'cortex': cortex, 'gwb': gwb, 'cgb': cgb, 'avg': avg, 'thickness': thickness, 'pwm': pwm, 'pgm': pgm, 'pcsf': pcsf}