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hMRI-toolbox-public/tbx_scfg_hmri_proc_US.m
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function proc_us = tbx_scfg_hmri_proc_US | |
% Configuration file for the segmentation part of the processing modules of | |
% the "histological MRI" (hMRI) toolbox. | |
% -> Apply "unifies segementation" (US) on series of images. | |
%_______________________________________________________________________ | |
% Copyright (C) 2017 Cyclotron Research Centre | |
% Written by Christophe Phillips | |
% but largely inspired by the batch from the past VBQ toolbox. | |
% NOTE: | |
% It could be advantageous to define the TPM in a definition file and use | |
% it when ever we need it. Right now, this is hard-coded in the cfg file! | |
% --------------------------------------------------------------------- | |
% vols_pm Parametric maps | |
% --------------------------------------------------------------------- | |
vols_pm = cfg_files; | |
vols_pm.tag = 'vols_pm'; | |
vols_pm.name = 'Maps'; | |
vols_pm.help = {['Select whole brain maps of one type (e.g. MT, R2*, ',... | |
'R1, etc.) from all subjects for processing.']}; | |
vols_pm.filter = 'image'; | |
vols_pm.ufilter = '.*'; | |
vols_pm.num = [1 Inf]; | |
% --------------------------------------------------------------------- | |
% indir Input directory as output directory | |
% --------------------------------------------------------------------- | |
indir = cfg_menu; | |
indir.tag = 'indir'; | |
indir.name = 'Input directory'; | |
indir.help = {['Output files will be written to the same folder as ',... | |
'each corresponding input file.']}; | |
indir.labels = {'Yes'}; | |
indir.values = {1}; | |
indir.val = {1}; | |
% --------------------------------------------------------------------- | |
% outdir Output directory for all data | |
% --------------------------------------------------------------------- | |
outdir = cfg_files; | |
outdir.tag = 'outdir'; | |
outdir.name = 'Output directory, all together'; | |
outdir.help = {['Select a directory where all output files from all '... | |
'subjects put together will be written to.']}; | |
outdir.filter = 'dir'; | |
outdir.ufilter = '.*'; | |
outdir.num = [1 1]; | |
% --------------------------------------------------------------------- | |
% outdir_ps Output directory for per-subject organisation | |
% --------------------------------------------------------------------- | |
outdir_ps = cfg_files; | |
outdir_ps.tag = 'outdir_ps'; | |
outdir_ps.name = 'Output directory, with per-subject sub-directory'; | |
outdir_ps.help = {['Select a directory where output files will be '... | |
'written to, in each subject''s sub-directory.']}; | |
outdir_ps.filter = 'dir'; | |
outdir_ps.ufilter = '.*'; | |
outdir_ps.num = [1 1]; | |
% --------------------------------------------------------------------- | |
% output Output choice | |
% --------------------------------------------------------------------- | |
output = cfg_choice; | |
output.tag = 'output'; | |
output.name = 'Output choice'; | |
output.help = {['Output directory can be the same as the input ',... | |
'directory for each input file or user selected (one for everything ',... | |
'or preserve a per-subject organisation).']}; | |
output.values = {indir outdir outdir_ps }; | |
output.val = {indir}; | |
% --------------------------------------------------------------------- | |
% Get stuff from SPM-US config file & adapting it | |
% --------------------------------------------------------------------- | |
% get the prproc8 config object | |
preproc8 = spm_cfg_preproc8; | |
% % set the bias cutoff and regularisation to 'no bias' 'no reg' correction | |
% preproc8 = cfg_set_val(preproc8, 'data', 'channel', 'biasfwhm', Inf); | |
% preproc8 = cfg_set_val(preproc8, 'data', 'channel', 'biasreg', 0); | |
% NOTE that this only change the values for the 1st channel! | |
% -> use dirty trick here under to both change 1st and default values... | |
% --------------------------------------------------------------------- | |
% rstruct Structurals | |
% --------------------------------------------------------------------- | |
% extract the data channel from preproc8 for the structural reference def. | |
rstruct = eval(['preproc8',cfg_expr(preproc8, 'data')]); | |
rstruct.tag = 'rstruct'; | |
rstruct.name = 'Structurals for segmentation'; | |
% set the bias cutoff to 'no bias' correction | |
rstruct = cfg_set_val(rstruct, 'channel', 'biasfwhm', Inf); | |
% set the bias regularisation to 'no regularisation' correction | |
rstruct = cfg_set_val(rstruct, 'channel', 'biasreg', 0); | |
% Dirty trick to set the default values of biasfhwm to Inf for new channels | |
kk_exp = cfg_expr(rstruct, 'channel', 'biasfwhm'); | |
ll = strfind(kk_exp,'.val{1}'); % pick bits with '.val{1}' | |
% Replace 1nd occurence by '.values{1}' -> defaults | |
kk_exp = [kk_exp(1:ll(1)-1),'.values{1}',kk_exp((ll(1)+7):end)]; | |
eval(['rstruct',kk_exp '.val = {Inf};']); | |
% Same goes for 'biasreg' sot to 0 | |
kk_exp = cfg_expr(rstruct, 'channel', 'biasreg'); | |
ll = strfind(kk_exp,'.val{1}'); % pick bits with '.val{1}' | |
% Replace 1nd occurence by '.values{1}' -> defaults | |
kk_exp = [kk_exp(1:ll(1)-1),'.values{1}',kk_exp((ll(1)+7):end)]; | |
eval(['rstruct',kk_exp '.val = {0};']); | |
% --------------------------------------------------------------------- | |
% many_pams Parameter maps | |
% --------------------------------------------------------------------- | |
% used for 'many subjects', i.e. list the data per map type across subjects | |
many_pams = cfg_repeat; | |
many_pams.tag = 'maps'; | |
many_pams.name = 'Parametric maps'; | |
many_pams.values = {vols_pm}; | |
many_pams.val = {}; % Empty to begin with | |
many_pams.num = [0 Inf]; | |
many_pams.help = {['Select whole brain parameter maps (e.g. MT, ',... | |
'R2*, FA, etc.) from all subjects for processing, one type per entry.']}; | |
% --------------------------------------------------------------------- | |
% vox Voxel sizes | |
% --------------------------------------------------------------------- | |
vox = cfg_entry; | |
vox.tag = 'vox'; | |
vox.name = 'Voxel sizes'; | |
vox.num = [1 3]; | |
vox.strtype = 'e'; | |
vox.val = {[1 1 1]}; | |
vox.help = {[... | |
'Specify the voxel sizes of the deformation field and tissue classes ',... | |
'to be produced. Non-finite values will default to the voxel sizes of ',... | |
'the template image that was originally used to estimate the deformation.']}; | |
%-------------------------------------------------------------------------- | |
% bb Bounding box | |
%-------------------------------------------------------------------------- | |
bb = cfg_entry; | |
bb.tag = 'bb'; | |
bb.name = 'Bounding box'; | |
bb.help = {'The bounding box (in mm) of the volume which is to be written (relative to the anterior commissure).'}; | |
bb.strtype = 'r'; | |
bb.num = [2 3]; | |
bb.def = @(val)spm_get_defaults('normalise.write.bb', val{:}); | |
% --------------------------------------------------------------------- | |
% many_sdatas Many subjects data | |
% --------------------------------------------------------------------- | |
many_sdatas = cfg_branch; | |
many_sdatas.tag = 'many_sdatas'; | |
many_sdatas.name = 'Data & options'; | |
many_sdatas.val = {output unlimit(rstruct) many_pams vox bb}; | |
many_sdatas.help = {'Specify images for many subjects at once.' , ... | |
['Processing will work on 1 subject at the time, using his ',... | |
'structural image(s) to estimate the segmentation and warping parameters. ', ... | |
'Then warps are applied *only* on his parametric maps, if provided.']}; | |
% --------------------------------------------------------------------- | |
% preproc8 Segment MT/T1w data | |
% --------------------------------------------------------------------- | |
proc_us = preproc8; | |
proc_us.name = 'Proc. hMRI -> Segmentation'; | |
proc_us.tag = 'proc_us'; | |
% Combine data defintion (local) with the tissue specs & other parameters | |
% from preproc8 (these 2 are the last elements in preproc8.val) | |
proc_us.val = [{many_sdatas} preproc8.val(2:end)]; | |
% get the output for the tissue classes | |
w_native = hmri_get_defaults('proc.w_native'); | |
w_warped = hmri_get_defaults('proc.w_warped'); | |
% get the number of Gaussians per tissue class | |
nGauss = hmri_get_defaults('proc.nGauss'); | |
% use the hMRI specific TPMs. | |
fn_tpm = hmri_get_defaults('proc.TPM'); | |
% Fill in each tissue class parameters | |
for ii=1:size(w_native,1) | |
proc_us = cfg_set_val(proc_us, 'tissues', ii, 'native', w_native(ii,:)); | |
proc_us = cfg_set_val(proc_us, 'tissues', ii, 'warped', w_warped(ii,:)); | |
proc_us = cfg_set_val(proc_us, 'tissues', ii, 'tpm', ... | |
{spm_file(fn_tpm,'number',ii)}); | |
proc_us = cfg_set_val(proc_us, 'tissues', ii, 'ngaus', nGauss(ii)); | |
end | |
% set the output to write out the forward deformation field | |
proc_us = cfg_set_val(proc_us, 'warp', 'write', [0 1]); | |
proc_us.prog = @hmri_run_proc_US; | |
proc_us.vout = @vout_preproc; | |
proc_us.check = @check_USdata; | |
end | |
%---------------------------------------------------------------------- | |
%---------------------------------------------------------------------- | |
%---------------------------------------------------------------------- | |
%% ======================================================================= | |
% VOUT function | |
% ======================================================================= | |
function dep = vout_preproc(job) | |
% This depends on job contents, which may not be present when virtual | |
% outputs are calculated. | |
cdep = cfg_dep; | |
% Collect tissue class images (4 of them) | |
for i=1:numel(job.tissue) | |
if job.tissue(i).native(1) | |
cdep(end+1) = cfg_dep; | |
cdep(end).sname = sprintf('c%d Images', i); | |
cdep(end).src_output = substruct('.', 'tiss', '()', {i}, '.', 'c', '()', {':'}); | |
cdep(end).tgt_spec = cfg_findspec({{'filter','nifti'}}); | |
% cdep(end).tgt_spec = cfg_findspec({{'filter','image','strtype','e'}}); % cfg_findspec({{'filter','nifti'}}); | |
end | |
if job.tissue(i).native(2) | |
cdep(end+1) = cfg_dep; | |
cdep(end).sname = sprintf('rc%d Images', i); | |
cdep(end).src_output = substruct('.', 'tiss', '()', {i}, '.', 'rc', '()', {':'}); | |
cdep(end).tgt_spec = cfg_findspec({{'filter','nifti'}}); | |
% cdep(end).tgt_spec = cfg_findspec({{'filter','image','strtype','e'}}); | |
end | |
if job.tissue(i).warped(1) | |
cdep(end+1) = cfg_dep; | |
cdep(end).sname = sprintf('wc%d Images', i); | |
cdep(end).src_output = substruct('.', 'tiss', '()', {i}, '.', 'wc', '()', {':'}); | |
cdep(end).tgt_spec = cfg_findspec({{'filter','nifti'}}); | |
% cdep(end).tgt_spec = cfg_findspec({{'filter','image','strtype','e'}}); | |
end | |
if job.tissue(i).warped(2) | |
cdep(end+1) = cfg_dep; | |
cdep(end).sname = sprintf('mwc%d Images', i); | |
cdep(end).src_output = substruct('.', 'tiss', '()', {i}, '.', 'mwc', '()', {':'}); | |
cdep(end).tgt_spec = cfg_findspec({{'filter','nifti'}}); | |
% cdep(end).tgt_spec = cfg_findspec({{'filter','image','strtype','e'}}); | |
end | |
end | |
% Collect warped parametric maps | |
for i=1:numel(job.many_sdatas.vols_pm) | |
cdep(end+1) = cfg_dep; | |
cdep(end).sname = sprintf('Warped p. maps #%d', i); | |
cdep(end).src_output = substruct('.', 'maps', '()', {i}, '.', 'wvols_pm', '()', {':'}); | |
cdep(end).tgt_spec = cfg_findspec({{'filter','nifti'}}); | |
% cdep(end).tgt_spec = cfg_findspec({{'filter','image','strtype','e'}}); | |
end | |
% Collect the deformation fields | |
cdep(end+1) = cfg_dep; | |
cdep(end).sname = 'Def. fields'; | |
cdep(end).src_output = substruct('.', 'def', '.', 'fn', '()', {':'}); | |
cdep(end).tgt_spec = cfg_findspec({{'filter','nifti'}}); | |
% cdep(end).tgt_spec = cfg_findspec({{'filter','image','strtype','e'}}); | |
dep = cdep(2:end); | |
end | |
%% ======================================================================= | |
% CHECKING the data | |
% ======================================================================== | |
function t = check_USdata(job) | |
% Checking that the data are consistent. | |
t = {}; | |
nSubj = numel(job.many_sdatas.channel(1).vols); | |
nChan = numel(job.many_sdatas.channel); % number of channels | |
nPara = numel(job.many_sdatas.vols_pm); % number of maps type | |
% 1/ Check the number of structurals in each channel | |
if nChan>1 | |
for ii=2:nChan | |
if numel(job.many_sdatas.channel(ii).vols)~=0 && ... | |
numel(job.many_sdatas.channel(ii).vols)~=nSubj | |
t{1} = 'Structural channels have different number of images/subjects!'; | |
warndlg(t,'Structural channel numbers'); | |
return | |
end | |
end | |
end | |
% 2/ Check this number matches the number of parametric maps | |
if nPara>1 | |
for ii=1:nPara | |
if numel(job.many_sdatas.vols_pm{ii})~=0 && ... | |
numel(job.many_sdatas.vols_pm{ii})~=nSubj | |
t{1} = 'Number of maps not matching number of structural images/subjects!'; | |
warndlg(t,'Maps numbers'); | |
return | |
end | |
end | |
end | |
end | |
%% ======================================================================= | |
% SUBFUNCTIONS to handle matlabbatch structure and fields | |
% ======================================================================== | |
function expr = cfg_expr(c, varargin) %#ok<INUSL> | |
expr = 'c'; | |
for i=1:size(varargin,2) | |
% if strcmp(class(varargin{i}), 'double') | |
if isa(varargin{i}, 'double') | |
expr = [expr '.val{' num2str(varargin{i}) '}']; %#ok<*AGROW> | |
else | |
v = eval([expr ';']); | |
for j=1:size(v.val,2) | |
if strcmp(v.val{j}.tag, varargin{i}) | |
break | |
end | |
end | |
expr = [expr '.val{' num2str(j) '}']; | |
end | |
end | |
expr = expr(2:end); | |
end | |
%_______________________________________________________________________ | |
function c = cfg_set_val(c, varargin) | |
expr = ['c' cfg_expr(c, varargin{1:end-1})]; | |
eval([expr '.val={varargin{end}};']); | |
end | |
%_______________________________________________________________________ | |
function c = unlimit(c) | |
try | |
if isa(c, 'cfg_files') | |
c.num = [0 Inf]; | |
end | |
catch e %#ok<*NASGU> | |
end | |
try | |
for i=1:numel(c.val) | |
c.val{i} = unlimit(c.val{i}); | |
end | |
catch e | |
end | |
end | |
%_______________________________________________________________________ | |