def get_centerline_from_point(input_image, point_file, gap=4, gaussian_kernel=4, remove_tmp_files=1): # Initialization fname_anat = input_image fname_point = point_file slice_gap = gap remove_tmp_files = remove_tmp_files gaussian_kernel = gaussian_kernel start_time = time() verbose = 1 # get path of the toolbox status, path_sct = commands.getstatusoutput("echo $SCT_DIR") path_sct = sct.slash_at_the_end(path_sct, 1) # Parameters for debug mode if param.debug == 1: sct.printv("\n*** WARNING: DEBUG MODE ON ***\n\t\t\tCurrent working directory: " + os.getcwd(), "warning") status, path_sct_testing_data = commands.getstatusoutput("echo $SCT_TESTING_DATA_DIR") fname_anat = path_sct_testing_data + "/t2/t2.nii.gz" fname_point = path_sct_testing_data + "/t2/t2_centerline_init.nii.gz" slice_gap = 5 # check existence of input files sct.check_file_exist(fname_anat) sct.check_file_exist(fname_point) # extract path/file/extension path_anat, file_anat, ext_anat = sct.extract_fname(fname_anat) path_point, file_point, ext_point = sct.extract_fname(fname_point) # extract path of schedule file # TODO: include schedule file in sct # TODO: check existence of schedule file file_schedule = path_sct + param.schedule_file # Get input image orientation input_image_orientation = get_orientation_3d(fname_anat, filename=True) # Display arguments print "\nCheck input arguments..." print " Anatomical image: " + fname_anat print " Orientation: " + input_image_orientation print " Point in spinal cord: " + fname_point print " Slice gap: " + str(slice_gap) print " Gaussian kernel: " + str(gaussian_kernel) print " Degree of polynomial: " + str(param.deg_poly) # create temporary folder print ("\nCreate temporary folder...") path_tmp = "tmp." + strftime("%y%m%d%H%M%S") sct.create_folder(path_tmp) print "\nCopy input data..." sct.run("cp " + fname_anat + " " + path_tmp + "/tmp.anat" + ext_anat) sct.run("cp " + fname_point + " " + path_tmp + "/tmp.point" + ext_point) # go to temporary folder os.chdir(path_tmp) # convert to nii im_anat = convert("tmp.anat" + ext_anat, "tmp.anat.nii") im_point = convert("tmp.point" + ext_point, "tmp.point.nii") # Reorient input anatomical volume into RL PA IS orientation print "\nReorient input volume to RL PA IS orientation..." set_orientation(im_anat, "RPI") im_anat.setFileName("tmp.anat_orient.nii") # Reorient binary point into RL PA IS orientation print "\nReorient binary point into RL PA IS orientation..." # sct.run(sct.fsloutput + 'fslswapdim tmp.point RL PA IS tmp.point_orient') set_orientation(im_point, "RPI") im_point.setFileName("tmp.point_orient.nii") # Get image dimensions print "\nGet image dimensions..." nx, ny, nz, nt, px, py, pz, pt = Image("tmp.anat_orient.nii").dim print ".. matrix size: " + str(nx) + " x " + str(ny) + " x " + str(nz) print ".. voxel size: " + str(px) + "mm x " + str(py) + "mm x " + str(pz) + "mm" # Split input volume print "\nSplit input volume..." im_anat_split_list = split_data(im_anat, 2) file_anat_split = [] for im in im_anat_split_list: file_anat_split.append(im.absolutepath) im.save() im_point_split_list = split_data(im_point, 2) file_point_split = [] for im in im_point_split_list: file_point_split.append(im.absolutepath) im.save() # Extract coordinates of input point data_point = Image("tmp.point_orient.nii").data x_init, y_init, z_init = unravel_index(data_point.argmax(), data_point.shape) sct.printv("Coordinates of input point: (" + str(x_init) + ", " + str(y_init) + ", " + str(z_init) + ")", verbose) # Create 2D gaussian mask sct.printv("\nCreate gaussian mask from point...", verbose) xx, yy = mgrid[:nx, :ny] mask2d = zeros((nx, ny)) radius = round(float(gaussian_kernel + 1) / 2) # add 1 because the radius includes the center. sigma = float(radius) mask2d = exp(-(((xx - x_init) ** 2) / (2 * (sigma ** 2)) + ((yy - y_init) ** 2) / (2 * (sigma ** 2)))) # Save mask to 2d file file_mask_split = ["tmp.mask_orient_Z" + str(z).zfill(4) for z in range(0, nz, 1)] nii_mask2d = Image("tmp.anat_orient_Z0000.nii") nii_mask2d.data = mask2d nii_mask2d.setFileName(file_mask_split[z_init] + ".nii") nii_mask2d.save() # initialize variables file_mat = ["tmp.mat_Z" + str(z).zfill(4) for z in range(0, nz, 1)] file_mat_inv = ["tmp.mat_inv_Z" + str(z).zfill(4) for z in range(0, nz, 1)] file_mat_inv_cumul = ["tmp.mat_inv_cumul_Z" + str(z).zfill(4) for z in range(0, nz, 1)] # create identity matrix for initial transformation matrix fid = open(file_mat_inv_cumul[z_init], "w") fid.write("%i %i %i %i\n" % (1, 0, 0, 0)) fid.write("%i %i %i %i\n" % (0, 1, 0, 0)) fid.write("%i %i %i %i\n" % (0, 0, 1, 0)) fid.write("%i %i %i %i\n" % (0, 0, 0, 1)) fid.close() # initialize centerline: give value corresponding to initial point x_centerline = [x_init] y_centerline = [y_init] z_centerline = [z_init] warning_count = 0 # go up (1), then down (2) in reference to the binary point for iUpDown in range(1, 3): if iUpDown == 1: # z increases slice_gap_signed = slice_gap elif iUpDown == 2: # z decreases slice_gap_signed = -slice_gap # reverse centerline (because values will be appended at the end) x_centerline.reverse() y_centerline.reverse() z_centerline.reverse() # initialization before looping z_dest = z_init # point given by user z_src = z_dest + slice_gap_signed # continue looping if 0 <= z < nz while 0 <= z_src < nz: # print current z: print "z=" + str(z_src) + ":" # estimate transformation sct.run( fsloutput + "flirt -in " + file_anat_split[z_src] + " -ref " + file_anat_split[z_dest] + " -schedule " + file_schedule + " -verbose 0 -omat " + file_mat[z_src] + " -cost normcorr -forcescaling -inweight " + file_mask_split[z_dest] + " -refweight " + file_mask_split[z_dest] ) # display transfo status, output = sct.run("cat " + file_mat[z_src]) print output # check if transformation is bigger than 1.5x slice_gap tx = float(output.split()[3]) ty = float(output.split()[7]) norm_txy = linalg.norm([tx, ty], ord=2) if norm_txy > 1.5 * slice_gap: print "WARNING: Transformation is too large --> using previous one." warning_count = warning_count + 1 # if previous transformation exists, replace current one with previous one if os.path.isfile(file_mat[z_dest]): sct.run("cp " + file_mat[z_dest] + " " + file_mat[z_src]) # estimate inverse transformation matrix sct.run("convert_xfm -omat " + file_mat_inv[z_src] + " -inverse " + file_mat[z_src]) # compute cumulative transformation sct.run( "convert_xfm -omat " + file_mat_inv_cumul[z_src] + " -concat " + file_mat_inv[z_src] + " " + file_mat_inv_cumul[z_dest] ) # apply inverse cumulative transformation to initial gaussian mask (to put it in src space) sct.run( fsloutput + "flirt -in " + file_mask_split[z_init] + " -ref " + file_mask_split[z_init] + " -applyxfm -init " + file_mat_inv_cumul[z_src] + " -out " + file_mask_split[z_src] ) # open inverse cumulative transformation file and generate centerline fid = open(file_mat_inv_cumul[z_src]) mat = fid.read().split() x_centerline.append(x_init + float(mat[3])) y_centerline.append(y_init + float(mat[7])) z_centerline.append(z_src) # z_index = z_index+1 # define new z_dest (target slice) and new z_src (moving slice) z_dest = z_dest + slice_gap_signed z_src = z_src + slice_gap_signed # Reconstruct centerline # ==================================================================================================== # reverse back centerline (because it's been reversed once, so now all values are in the right order) x_centerline.reverse() y_centerline.reverse() z_centerline.reverse() # fit centerline in the Z-X plane using polynomial function print "\nFit centerline in the Z-X plane using polynomial function..." coeffsx = polyfit(z_centerline, x_centerline, deg=param.deg_poly) polyx = poly1d(coeffsx) x_centerline_fit = polyval(polyx, z_centerline) # calculate RMSE rmse = linalg.norm(x_centerline_fit - x_centerline) / sqrt(len(x_centerline)) # calculate max absolute error max_abs = max(abs(x_centerline_fit - x_centerline)) print ".. RMSE (in mm): " + str(rmse * px) print ".. Maximum absolute error (in mm): " + str(max_abs * px) # fit centerline in the Z-Y plane using polynomial function print "\nFit centerline in the Z-Y plane using polynomial function..." coeffsy = polyfit(z_centerline, y_centerline, deg=param.deg_poly) polyy = poly1d(coeffsy) y_centerline_fit = polyval(polyy, z_centerline) # calculate RMSE rmse = linalg.norm(y_centerline_fit - y_centerline) / sqrt(len(y_centerline)) # calculate max absolute error max_abs = max(abs(y_centerline_fit - y_centerline)) print ".. RMSE (in mm): " + str(rmse * py) print ".. Maximum absolute error (in mm): " + str(max_abs * py) # display if param.debug == 1: import matplotlib.pyplot as plt plt.figure() plt.plot(z_centerline, x_centerline, ".", z_centerline, x_centerline_fit, "r") plt.legend(["Data", "Polynomial Fit"]) plt.title("Z-X plane polynomial interpolation") plt.show() plt.figure() plt.plot(z_centerline, y_centerline, ".", z_centerline, y_centerline_fit, "r") plt.legend(["Data", "Polynomial Fit"]) plt.title("Z-Y plane polynomial interpolation") plt.show() # generate full range z-values for centerline z_centerline_full = [iz for iz in range(0, nz, 1)] # calculate X and Y values for the full centerline x_centerline_fit_full = polyval(polyx, z_centerline_full) y_centerline_fit_full = polyval(polyy, z_centerline_full) # Generate fitted transformation matrices and write centerline coordinates in text file print "\nGenerate fitted transformation matrices and write centerline coordinates in text file..." file_mat_inv_cumul_fit = ["tmp.mat_inv_cumul_fit_z" + str(z).zfill(4) for z in range(0, nz, 1)] file_mat_cumul_fit = ["tmp.mat_cumul_fit_z" + str(z).zfill(4) for z in range(0, nz, 1)] fid_centerline = open("tmp.centerline_coordinates.txt", "w") for iz in range(0, nz, 1): # compute inverse cumulative fitted transformation matrix fid = open(file_mat_inv_cumul_fit[iz], "w") fid.write("%i %i %i %f\n" % (1, 0, 0, x_centerline_fit_full[iz] - x_init)) fid.write("%i %i %i %f\n" % (0, 1, 0, y_centerline_fit_full[iz] - y_init)) fid.write("%i %i %i %i\n" % (0, 0, 1, 0)) fid.write("%i %i %i %i\n" % (0, 0, 0, 1)) fid.close() # compute forward cumulative fitted transformation matrix sct.run("convert_xfm -omat " + file_mat_cumul_fit[iz] + " -inverse " + file_mat_inv_cumul_fit[iz]) # write centerline coordinates in x, y, z format fid_centerline.write( "%f %f %f\n" % (x_centerline_fit_full[iz], y_centerline_fit_full[iz], z_centerline_full[iz]) ) fid_centerline.close() # Prepare output data # ==================================================================================================== # write centerline as text file for iz in range(0, nz, 1): # compute inverse cumulative fitted transformation matrix fid = open(file_mat_inv_cumul_fit[iz], "w") fid.write("%i %i %i %f\n" % (1, 0, 0, x_centerline_fit_full[iz] - x_init)) fid.write("%i %i %i %f\n" % (0, 1, 0, y_centerline_fit_full[iz] - y_init)) fid.write("%i %i %i %i\n" % (0, 0, 1, 0)) fid.write("%i %i %i %i\n" % (0, 0, 0, 1)) fid.close() # write polynomial coefficients savetxt("tmp.centerline_polycoeffs_x.txt", coeffsx) savetxt("tmp.centerline_polycoeffs_y.txt", coeffsy) # apply transformations to data print "\nApply fitted transformation matrices..." file_anat_split_fit = ["tmp.anat_orient_fit_z" + str(z).zfill(4) for z in range(0, nz, 1)] file_mask_split_fit = ["tmp.mask_orient_fit_z" + str(z).zfill(4) for z in range(0, nz, 1)] file_point_split_fit = ["tmp.point_orient_fit_z" + str(z).zfill(4) for z in range(0, nz, 1)] for iz in range(0, nz, 1): # forward cumulative transformation to data sct.run( fsloutput + "flirt -in " + file_anat_split[iz] + " -ref " + file_anat_split[iz] + " -applyxfm -init " + file_mat_cumul_fit[iz] + " -out " + file_anat_split_fit[iz] ) # inverse cumulative transformation to mask sct.run( fsloutput + "flirt -in " + file_mask_split[z_init] + " -ref " + file_mask_split[z_init] + " -applyxfm -init " + file_mat_inv_cumul_fit[iz] + " -out " + file_mask_split_fit[iz] ) # inverse cumulative transformation to point sct.run( fsloutput + "flirt -in " + file_point_split[z_init] + " -ref " + file_point_split[z_init] + " -applyxfm -init " + file_mat_inv_cumul_fit[iz] + " -out " + file_point_split_fit[iz] + " -interp nearestneighbour" ) # Merge into 4D volume print "\nMerge into 4D volume..." # im_anat_list = [Image(fname) for fname in glob.glob('tmp.anat_orient_fit_z*.nii')] fname_anat_list = glob.glob("tmp.anat_orient_fit_z*.nii") im_anat_concat = concat_data(fname_anat_list, 2) im_anat_concat.setFileName("tmp.anat_orient_fit.nii") im_anat_concat.save() # im_mask_list = [Image(fname) for fname in glob.glob('tmp.mask_orient_fit_z*.nii')] fname_mask_list = glob.glob("tmp.mask_orient_fit_z*.nii") im_mask_concat = concat_data(fname_mask_list, 2) im_mask_concat.setFileName("tmp.mask_orient_fit.nii") im_mask_concat.save() # im_point_list = [Image(fname) for fname in glob.glob('tmp.point_orient_fit_z*.nii')] fname_point_list = glob.glob("tmp.point_orient_fit_z*.nii") im_point_concat = concat_data(fname_point_list, 2) im_point_concat.setFileName("tmp.point_orient_fit.nii") im_point_concat.save() # Copy header geometry from input data print "\nCopy header geometry from input data..." im_anat = Image("tmp.anat_orient.nii") im_anat_orient_fit = Image("tmp.anat_orient_fit.nii") im_mask_orient_fit = Image("tmp.mask_orient_fit.nii") im_point_orient_fit = Image("tmp.point_orient_fit.nii") im_anat_orient_fit = copy_header(im_anat, im_anat_orient_fit) im_mask_orient_fit = copy_header(im_anat, im_mask_orient_fit) im_point_orient_fit = copy_header(im_anat, im_point_orient_fit) for im in [im_anat_orient_fit, im_mask_orient_fit, im_point_orient_fit]: im.save() # Reorient outputs into the initial orientation of the input image print "\nReorient the centerline into the initial orientation of the input image..." set_orientation("tmp.point_orient_fit.nii", input_image_orientation, "tmp.point_orient_fit.nii") set_orientation("tmp.mask_orient_fit.nii", input_image_orientation, "tmp.mask_orient_fit.nii") # Generate output file (in current folder) print "\nGenerate output file (in current folder)..." os.chdir("..") # come back to parent folder fname_output_centerline = sct.generate_output_file( path_tmp + "/tmp.point_orient_fit.nii", file_anat + "_centerline" + ext_anat ) # Delete temporary files if remove_tmp_files == 1: print "\nRemove temporary files..." sct.run("rm -rf " + path_tmp, error_exit="warning") # print number of warnings print "\nNumber of warnings: " + str( warning_count ) + " (if >10, you should probably reduce the gap and/or increase the kernel size" # display elapsed time elapsed_time = time() - start_time print "\nFinished! \n\tGenerated file: " + fname_output_centerline + "\n\tElapsed time: " + str( int(round(elapsed_time)) ) + "s\n"
def main(): # Initialization fname_anat = '' fname_point = '' slice_gap = param.gap remove_tmp_files = param.remove_tmp_files gaussian_kernel = param.gaussian_kernel start_time = time.time() verbose = 1 # get path of the toolbox status, path_sct = commands.getstatusoutput('echo $SCT_DIR') path_sct = sct.slash_at_the_end(path_sct, 1) # Parameters for debug mode if param.debug == 1: sct.printv('\n*** WARNING: DEBUG MODE ON ***\n\t\t\tCurrent working directory: '+os.getcwd(), 'warning') status, path_sct_testing_data = commands.getstatusoutput('echo $SCT_TESTING_DATA_DIR') fname_anat = path_sct_testing_data+'/t2/t2.nii.gz' fname_point = path_sct_testing_data+'/t2/t2_centerline_init.nii.gz' slice_gap = 5 else: # Check input param try: opts, args = getopt.getopt(sys.argv[1:],'hi:p:g:r:k:') except getopt.GetoptError as err: print str(err) usage() if not opts: usage() for opt, arg in opts: if opt == '-h': usage() elif opt in ('-i'): fname_anat = arg elif opt in ('-p'): fname_point = arg elif opt in ('-g'): slice_gap = int(arg) elif opt in ('-r'): remove_tmp_files = int(arg) elif opt in ('-k'): gaussian_kernel = int(arg) # display usage if a mandatory argument is not provided if fname_anat == '' or fname_point == '': usage() # check existence of input files sct.check_file_exist(fname_anat) sct.check_file_exist(fname_point) # extract path/file/extension path_anat, file_anat, ext_anat = sct.extract_fname(fname_anat) path_point, file_point, ext_point = sct.extract_fname(fname_point) # extract path of schedule file # TODO: include schedule file in sct # TODO: check existence of schedule file file_schedule = path_sct + param.schedule_file # Get input image orientation input_image_orientation = get_orientation(fname_anat) # Display arguments print '\nCheck input arguments...' print ' Anatomical image: '+fname_anat print ' Orientation: '+input_image_orientation print ' Point in spinal cord: '+fname_point print ' Slice gap: '+str(slice_gap) print ' Gaussian kernel: '+str(gaussian_kernel) print ' Degree of polynomial: '+str(param.deg_poly) # create temporary folder print('\nCreate temporary folder...') path_tmp = 'tmp.'+time.strftime("%y%m%d%H%M%S") sct.create_folder(path_tmp) print '\nCopy input data...' sct.run('cp '+fname_anat+ ' '+path_tmp+'/tmp.anat'+ext_anat) sct.run('cp '+fname_point+ ' '+path_tmp+'/tmp.point'+ext_point) # go to temporary folder os.chdir(path_tmp) # convert to nii convert('tmp.anat'+ext_anat, 'tmp.anat.nii') convert('tmp.point'+ext_point, 'tmp.point.nii') # Reorient input anatomical volume into RL PA IS orientation print '\nReorient input volume to RL PA IS orientation...' #sct.run(sct.fsloutput + 'fslswapdim tmp.anat RL PA IS tmp.anat_orient') set_orientation('tmp.anat.nii', 'RPI', 'tmp.anat_orient.nii') # Reorient binary point into RL PA IS orientation print '\nReorient binary point into RL PA IS orientation...' # sct.run(sct.fsloutput + 'fslswapdim tmp.point RL PA IS tmp.point_orient') set_orientation('tmp.point.nii', 'RPI', 'tmp.point_orient.nii') # Get image dimensions print '\nGet image dimensions...' nx, ny, nz, nt, px, py, pz, pt = Image('tmp.anat_orient.nii').dim print '.. matrix size: '+str(nx)+' x '+str(ny)+' x '+str(nz) print '.. voxel size: '+str(px)+'mm x '+str(py)+'mm x '+str(pz)+'mm' # Split input volume print '\nSplit input volume...' split_data('tmp.anat_orient.nii', 2, '_z') file_anat_split = ['tmp.anat_orient_z'+str(z).zfill(4) for z in range(0, nz, 1)] split_data('tmp.point_orient.nii', 2, '_z') file_point_split = ['tmp.point_orient_z'+str(z).zfill(4) for z in range(0, nz, 1)] # Extract coordinates of input point # sct.printv('\nExtract the slice corresponding to z='+str(z_init)+'...', verbose) # data_point = Image('tmp.point_orient.nii').data x_init, y_init, z_init = unravel_index(data_point.argmax(), data_point.shape) sct.printv('Coordinates of input point: ('+str(x_init)+', '+str(y_init)+', '+str(z_init)+')', verbose) # Create 2D gaussian mask sct.printv('\nCreate gaussian mask from point...', verbose) xx, yy = mgrid[:nx, :ny] mask2d = zeros((nx, ny)) radius = round(float(gaussian_kernel+1)/2) # add 1 because the radius includes the center. sigma = float(radius) mask2d = exp(-(((xx-x_init)**2)/(2*(sigma**2)) + ((yy-y_init)**2)/(2*(sigma**2)))) # Save mask to 2d file file_mask_split = ['tmp.mask_orient_z'+str(z).zfill(4) for z in range(0,nz,1)] nii_mask2d = Image('tmp.anat_orient_z0000.nii') nii_mask2d.data = mask2d nii_mask2d.setFileName(file_mask_split[z_init]+'.nii') nii_mask2d.save() # # # Get the coordinates of the input point # print '\nGet the coordinates of the input point...' # data_point = Image('tmp.point_orient.nii').data # x_init, y_init, z_init = unravel_index(data_point.argmax(), data_point.shape) # print '('+str(x_init)+', '+str(y_init)+', '+str(z_init)+')' # x_init, y_init, z_init = (data > 0).nonzero() # x_init = x_init[0] # y_init = y_init[0] # z_init = z_init[0] # print '('+str(x_init)+', '+str(y_init)+', '+str(z_init)+')' # # numpy.unravel_index(a.argmax(), a.shape) # # file = nibabel.load('tmp.point_orient.nii') # data = file.get_data() # x_init, y_init, z_init = (data > 0).nonzero() # x_init = x_init[0] # y_init = y_init[0] # z_init = z_init[0] # print '('+str(x_init)+', '+str(y_init)+', '+str(z_init)+')' # # # Extract the slice corresponding to z=z_init # print '\nExtract the slice corresponding to z='+str(z_init)+'...' # file_point_split = ['tmp.point_orient_z'+str(z).zfill(4) for z in range(0,nz,1)] # nii = Image('tmp.point_orient.nii') # data_crop = nii.data[:, :, z_init:z_init+1] # nii.data = data_crop # nii.setFileName(file_point_split[z_init]+'.nii') # nii.save() # # # Create gaussian mask from point # print '\nCreate gaussian mask from point...' # file_mask_split = ['tmp.mask_orient_z'+str(z).zfill(4) for z in range(0,nz,1)] # sct.run(sct.fsloutput+'fslmaths '+file_point_split[z_init]+' -s '+str(gaussian_kernel)+' '+file_mask_split[z_init]) # # # Obtain max value from mask # print '\nFind maximum value from mask...' # file = nibabel.load(file_mask_split[z_init]+'.nii') # data = file.get_data() # max_value_mask = numpy.max(data) # print '..'+str(max_value_mask) # # # Normalize mask beween 0 and 1 # print '\nNormalize mask beween 0 and 1...' # sct.run(sct.fsloutput+'fslmaths '+file_mask_split[z_init]+' -div '+str(max_value_mask)+' '+file_mask_split[z_init]) ## Take the square of the mask #print '\nCalculate the square of the mask...' #sct.run(sct.fsloutput+'fslmaths '+file_mask_split[z_init]+' -mul '+file_mask_split[z_init]+' '+file_mask_split[z_init]) # initialize variables file_mat = ['tmp.mat_z'+str(z).zfill(4) for z in range(0,nz,1)] file_mat_inv = ['tmp.mat_inv_z'+str(z).zfill(4) for z in range(0,nz,1)] file_mat_inv_cumul = ['tmp.mat_inv_cumul_z'+str(z).zfill(4) for z in range(0,nz,1)] # create identity matrix for initial transformation matrix fid = open(file_mat_inv_cumul[z_init], 'w') fid.write('%i %i %i %i\n' %(1, 0, 0, 0) ) fid.write('%i %i %i %i\n' %(0, 1, 0, 0) ) fid.write('%i %i %i %i\n' %(0, 0, 1, 0) ) fid.write('%i %i %i %i\n' %(0, 0, 0, 1) ) fid.close() # initialize centerline: give value corresponding to initial point x_centerline = [x_init] y_centerline = [y_init] z_centerline = [z_init] warning_count = 0 # go up (1), then down (2) in reference to the binary point for iUpDown in range(1, 3): if iUpDown == 1: # z increases slice_gap_signed = slice_gap elif iUpDown == 2: # z decreases slice_gap_signed = -slice_gap # reverse centerline (because values will be appended at the end) x_centerline.reverse() y_centerline.reverse() z_centerline.reverse() # initialization before looping z_dest = z_init # point given by user z_src = z_dest + slice_gap_signed # continue looping if 0 < z < nz while 0 <= z_src and z_src <= nz-1: # print current z: print 'z='+str(z_src)+':' # estimate transformation sct.run(fsloutput+'flirt -in '+file_anat_split[z_src]+' -ref '+file_anat_split[z_dest]+' -schedule '+file_schedule+ ' -verbose 0 -omat '+file_mat[z_src]+' -cost normcorr -forcescaling -inweight '+file_mask_split[z_dest]+' -refweight '+file_mask_split[z_dest]) # display transfo status, output = sct.run('cat '+file_mat[z_src]) print output # check if transformation is bigger than 1.5x slice_gap tx = float(output.split()[3]) ty = float(output.split()[7]) norm_txy = linalg.norm([tx, ty],ord=2) if norm_txy > 1.5*slice_gap: print 'WARNING: Transformation is too large --> using previous one.' warning_count = warning_count + 1 # if previous transformation exists, replace current one with previous one if os.path.isfile(file_mat[z_dest]): sct.run('cp '+file_mat[z_dest]+' '+file_mat[z_src]) # estimate inverse transformation matrix sct.run('convert_xfm -omat '+file_mat_inv[z_src]+' -inverse '+file_mat[z_src]) # compute cumulative transformation sct.run('convert_xfm -omat '+file_mat_inv_cumul[z_src]+' -concat '+file_mat_inv[z_src]+' '+file_mat_inv_cumul[z_dest]) # apply inverse cumulative transformation to initial gaussian mask (to put it in src space) sct.run(fsloutput+'flirt -in '+file_mask_split[z_init]+' -ref '+file_mask_split[z_init]+' -applyxfm -init '+file_mat_inv_cumul[z_src]+' -out '+file_mask_split[z_src]) # open inverse cumulative transformation file and generate centerline fid = open(file_mat_inv_cumul[z_src]) mat = fid.read().split() x_centerline.append(x_init + float(mat[3])) y_centerline.append(y_init + float(mat[7])) z_centerline.append(z_src) #z_index = z_index+1 # define new z_dest (target slice) and new z_src (moving slice) z_dest = z_dest + slice_gap_signed z_src = z_src + slice_gap_signed # Reconstruct centerline # ==================================================================================================== # reverse back centerline (because it's been reversed once, so now all values are in the right order) x_centerline.reverse() y_centerline.reverse() z_centerline.reverse() # fit centerline in the Z-X plane using polynomial function print '\nFit centerline in the Z-X plane using polynomial function...' coeffsx = polyfit(z_centerline, x_centerline, deg=param.deg_poly) polyx = poly1d(coeffsx) x_centerline_fit = polyval(polyx, z_centerline) # calculate RMSE rmse = linalg.norm(x_centerline_fit-x_centerline)/sqrt( len(x_centerline) ) # calculate max absolute error max_abs = max( abs(x_centerline_fit-x_centerline) ) print '.. RMSE (in mm): '+str(rmse*px) print '.. Maximum absolute error (in mm): '+str(max_abs*px) # fit centerline in the Z-Y plane using polynomial function print '\nFit centerline in the Z-Y plane using polynomial function...' coeffsy = polyfit(z_centerline, y_centerline, deg=param.deg_poly) polyy = poly1d(coeffsy) y_centerline_fit = polyval(polyy, z_centerline) # calculate RMSE rmse = linalg.norm(y_centerline_fit-y_centerline)/sqrt( len(y_centerline) ) # calculate max absolute error max_abs = max( abs(y_centerline_fit-y_centerline) ) print '.. RMSE (in mm): '+str(rmse*py) print '.. Maximum absolute error (in mm): '+str(max_abs*py) # display if param.debug == 1: import matplotlib.pyplot as plt plt.figure() plt.plot(z_centerline,x_centerline,'.',z_centerline,x_centerline_fit,'r') plt.legend(['Data','Polynomial Fit']) plt.title('Z-X plane polynomial interpolation') plt.show() plt.figure() plt.plot(z_centerline,y_centerline,'.',z_centerline,y_centerline_fit,'r') plt.legend(['Data','Polynomial Fit']) plt.title('Z-Y plane polynomial interpolation') plt.show() # generate full range z-values for centerline z_centerline_full = [iz for iz in range(0, nz, 1)] # calculate X and Y values for the full centerline x_centerline_fit_full = polyval(polyx, z_centerline_full) y_centerline_fit_full = polyval(polyy, z_centerline_full) # Generate fitted transformation matrices and write centerline coordinates in text file print '\nGenerate fitted transformation matrices and write centerline coordinates in text file...' file_mat_inv_cumul_fit = ['tmp.mat_inv_cumul_fit_z'+str(z).zfill(4) for z in range(0,nz,1)] file_mat_cumul_fit = ['tmp.mat_cumul_fit_z'+str(z).zfill(4) for z in range(0,nz,1)] fid_centerline = open('tmp.centerline_coordinates.txt', 'w') for iz in range(0, nz, 1): # compute inverse cumulative fitted transformation matrix fid = open(file_mat_inv_cumul_fit[iz], 'w') fid.write('%i %i %i %f\n' %(1, 0, 0, x_centerline_fit_full[iz]-x_init) ) fid.write('%i %i %i %f\n' %(0, 1, 0, y_centerline_fit_full[iz]-y_init) ) fid.write('%i %i %i %i\n' %(0, 0, 1, 0) ) fid.write('%i %i %i %i\n' %(0, 0, 0, 1) ) fid.close() # compute forward cumulative fitted transformation matrix sct.run('convert_xfm -omat '+file_mat_cumul_fit[iz]+' -inverse '+file_mat_inv_cumul_fit[iz]) # write centerline coordinates in x, y, z format fid_centerline.write('%f %f %f\n' %(x_centerline_fit_full[iz], y_centerline_fit_full[iz], z_centerline_full[iz]) ) fid_centerline.close() # Prepare output data # ==================================================================================================== # write centerline as text file for iz in range(0, nz, 1): # compute inverse cumulative fitted transformation matrix fid = open(file_mat_inv_cumul_fit[iz], 'w') fid.write('%i %i %i %f\n' %(1, 0, 0, x_centerline_fit_full[iz]-x_init) ) fid.write('%i %i %i %f\n' %(0, 1, 0, y_centerline_fit_full[iz]-y_init) ) fid.write('%i %i %i %i\n' %(0, 0, 1, 0) ) fid.write('%i %i %i %i\n' %(0, 0, 0, 1) ) fid.close() # write polynomial coefficients savetxt('tmp.centerline_polycoeffs_x.txt',coeffsx) savetxt('tmp.centerline_polycoeffs_y.txt',coeffsy) # apply transformations to data print '\nApply fitted transformation matrices...' file_anat_split_fit = ['tmp.anat_orient_fit_z'+str(z).zfill(4) for z in range(0,nz,1)] file_mask_split_fit = ['tmp.mask_orient_fit_z'+str(z).zfill(4) for z in range(0,nz,1)] file_point_split_fit = ['tmp.point_orient_fit_z'+str(z).zfill(4) for z in range(0,nz,1)] for iz in range(0, nz, 1): # forward cumulative transformation to data sct.run(fsloutput+'flirt -in '+file_anat_split[iz]+' -ref '+file_anat_split[iz]+' -applyxfm -init '+file_mat_cumul_fit[iz]+' -out '+file_anat_split_fit[iz]) # inverse cumulative transformation to mask sct.run(fsloutput+'flirt -in '+file_mask_split[z_init]+' -ref '+file_mask_split[z_init]+' -applyxfm -init '+file_mat_inv_cumul_fit[iz]+' -out '+file_mask_split_fit[iz]) # inverse cumulative transformation to point sct.run(fsloutput+'flirt -in '+file_point_split[z_init]+' -ref '+file_point_split[z_init]+' -applyxfm -init '+file_mat_inv_cumul_fit[iz]+' -out '+file_point_split_fit[iz]+' -interp nearestneighbour') # Merge into 4D volume print '\nMerge into 4D volume...' # sct.run(fsloutput+'fslmerge -z tmp.anat_orient_fit tmp.anat_orient_fit_z*') # sct.run(fsloutput+'fslmerge -z tmp.mask_orient_fit tmp.mask_orient_fit_z*') # sct.run(fsloutput+'fslmerge -z tmp.point_orient_fit tmp.point_orient_fit_z*') concat_data(glob.glob('tmp.anat_orient_fit_z*.nii'), 'tmp.anat_orient_fit.nii', dim=2) concat_data(glob.glob('tmp.mask_orient_fit_z*.nii'), 'tmp.mask_orient_fit.nii', dim=2) concat_data(glob.glob('tmp.point_orient_fit_z*.nii'), 'tmp.point_orient_fit.nii', dim=2) # Copy header geometry from input data print '\nCopy header geometry from input data...' copy_header('tmp.anat_orient.nii', 'tmp.anat_orient_fit.nii') copy_header('tmp.anat_orient.nii', 'tmp.mask_orient_fit.nii') copy_header('tmp.anat_orient.nii', 'tmp.point_orient_fit.nii') # Reorient outputs into the initial orientation of the input image print '\nReorient the centerline into the initial orientation of the input image...' set_orientation('tmp.point_orient_fit.nii', input_image_orientation, 'tmp.point_orient_fit.nii') set_orientation('tmp.mask_orient_fit.nii', input_image_orientation, 'tmp.mask_orient_fit.nii') # Generate output file (in current folder) print '\nGenerate output file (in current folder)...' os.chdir('..') # come back to parent folder #sct.generate_output_file('tmp.centerline_polycoeffs_x.txt','./','centerline_polycoeffs_x','.txt') #sct.generate_output_file('tmp.centerline_polycoeffs_y.txt','./','centerline_polycoeffs_y','.txt') #sct.generate_output_file('tmp.centerline_coordinates.txt','./','centerline_coordinates','.txt') #sct.generate_output_file('tmp.anat_orient.nii','./',file_anat+'_rpi',ext_anat) #sct.generate_output_file('tmp.anat_orient_fit.nii', file_anat+'_rpi_align'+ext_anat) #sct.generate_output_file('tmp.mask_orient_fit.nii', file_anat+'_mask'+ext_anat) fname_output_centerline = sct.generate_output_file(path_tmp+'/tmp.point_orient_fit.nii', file_anat+'_centerline'+ext_anat) # Delete temporary files if remove_tmp_files == 1: print '\nRemove temporary files...' sct.run('rm -rf '+path_tmp) # print number of warnings print '\nNumber of warnings: '+str(warning_count)+' (if >10, you should probably reduce the gap and/or increase the kernel size' # display elapsed time elapsed_time = time.time() - start_time print '\nFinished! \n\tGenerated file: '+fname_output_centerline+'\n\tElapsed time: '+str(int(round(elapsed_time)))+'s\n'