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ps_LST_lpa.m
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ps_LST_lpa.m
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function varargout = ps_LST_lpa(varargin)
%ps_LST_lpa Lesion segmentation by a lesion predicialgorithm (LGA)
% Part of the LST toolbox, www.statistical-modelling.de/lst.html
%
% ps_LST_lpa Lesion segmentation by the LPA requires a FLAIR image only.
% However, the user is free to choose an additional image that serves as
% a reference image during a coregistration step before the main lesion
% segmentation. This may be helpful if the dimension of the FLAIR image
% is low or if the goal of the lesion segmentation is to fill lesions in
% T1 images. Beside that no additional parameters need to be set. No
% other parameters need to be set by the user.
%
% This routine produces lesion probability maps (ples...), [coregistered]
% bias corrected versions of the FLAIR inputs, a .mat-file with
% information about the segmentation that is needed for a re-run of the
% algorithm, and a HTML report along with a folder if this option has
% been chosen desired. See the documentation for details.
%
% ps_LST_lpa asks the user for the input images (FLAIR and optional
% reference images). A HTML report is produced by default.
%
% ps_LST_lpa(Vf2, Vref, html) performs lesion
% segmentation for the FLAIR volumes given in Vf2 and reference volumes
% given in Vref. The letter can be left empty. If specified, both must be
% characters like a call from from spm_select. The last argument is a
% dummy for the HTML reports. If the last argument ismissing it is
% replaced by its default value, see above.
%
% ps_LST_lpa(job) Same as above but with job being a harvested job data
% structure (see matlabbatch help).
%
%spm_jobman('initcfg')
% Welcome text
fprintf('\n')
fprintf(repmat('-', 1, 72));
fprintf('\n')
%fprintf('\nThis is LST')
strout = 'This is LST';
fprintf(strout)
tt = 'www.statistical-modeling.de/lst.html\n';
strout = [repmat(' ', 1, 72 - numel(tt) - numel(strout) + 2), tt];
fprintf(strout)
strout = 'Algorithm:';
fprintf(strout)
tt = 'Lesion prediction algorithm (LPA)\n';
strout = [repmat(' ', 1, 72 - numel(tt) - numel(strout) + 2), tt];
fprintf(strout)
% Create log file
pthor = cd;
nameLog = ['LST_log_', ps_create_timestamp, '.txt'];
fileID = fopen(nameLog, 'wt');
strout = 'If anything goes wrong:';
fprintf(strout)
tt = [nameLog, '\n'];
strout = [repmat(' ', 1, 72 - numel(tt) - numel(strout) + 2), tt];
fprintf(strout)
% Check input
if ~isempty(varargin) && isfield(varargin{1}, 'data_F2')
viajob = 1;
else
viajob = 0;
end
if ~viajob
if nargin == 0
% call to ps_LST_lpa
fprintf(fileID, 'Select files by function ... ');
Vf2 = spm_select(Inf, 'image', 'Select FLAIR images.');
Vref = spm_select(Inf, 'image', 'Select reference images (optional).');
html_report = 1;
fprintf(fileID, 'ok.\n');
end
if nargin > 0
html_report = 1;
if ischar(varargin{1})
Vf2 = varargin{1};
Vref = [];
else
fprintf(fileID, 'Vf2 is not a character!');
fprintf('Input for Vf2 must be a character, like from spm_select.\n');
fclose(fileID);
return;
end
if nargin > 1
if ischar(varargin{2})
Vref = varargin{2};
else
fprintf(fileID, 'Vref is not a character!');
fprintf('Input for Vref must be a character, like from spm_select.\n');
fclose(fileID);
return;
end
end
if nargin == 3
html_report = varargin{3};
end
if nargin > 3
fprintf(fileID, 'Too many input arguments!');
fprintf('To many input arguments. Did you mean ps_LS_lga?\n');
fclose(fileID);
return;
end
end
else
job = varargin{1};
Vf2 = job.data_F2;
Vref = job.data_coreg;
if isempty(Vref{1})
Vref = [];
end
html_report = job.html_report;
end
fprintf(fileID, 'Load volumes ... ');
coreg = ~isempty(Vref);
Vf2 = spm_vol(Vf2);
if coreg
Vref = spm_vol(Vref);
end
fprintf(fileID, 'ok.\n');
if coreg && ~isequal(numel(Vf2), numel(Vref))
fprintf(fileID, 'numel(FLAIR) != numel(ref)');
error('Number of FLAIR images must match number of reference images.\n');
end
if numel(Vf2) == 0
fprintf(fileID, 'No images selected.');
fprintf('No images selected.\n');
return;
end
% Summarize input
fprintf(fileID, 'Input summary:\n');
fprintf(fileID, ['Jobs: ', num2str(numel(Vf2))]);
strout = 'Number of jobs to process:';
fprintf(strout)
tt = [num2str(numel(Vf2)), '\n'];
strout = [repmat(' ', 1, 72 - numel(tt) - numel(strout) + 2), tt];
fprintf(strout)
fprintf(fileID, ['\nCoregister to reference images: ', num2str(coreg)]);
strout = 'Coregister to reference images:';
fprintf(strout)
if coreg
tt = 'yes\n';
else
tt = 'no\n';
end
strout = [repmat(' ', 1, 72 - numel(tt) - numel(strout) + 2), tt];
fprintf(strout)
% Loop over all subjects
for i = 1:numel(Vf2)
fprintf(fileID, '\n--------------\n');
fprintf(fileID, ['Job ', num2str(i), ' of ', num2str(numel(Vf2)), '\n']);
fprintf(fileID, '--------------\n');
% Extract file information
if viajob
Vf2_tmp = Vf2{i};
else
Vf2_tmp = Vf2(i);
end
Vf2_tmp_or = Vf2_tmp;
if coreg
if viajob
Vref_tmp = Vref{i};
else
Vref_tmp = Vref(i);
end
end
[pthf2, namf2, extf2] = fileparts(Vf2_tmp.fname);
cd(pthf2)
% Which subject?
strout = '\nWorking on job';
fprintf(strout)
tt = [num2str(i), ' out of ', num2str(numel(Vf2)), ' (', num2str(i/numel(Vf2)*100), '%%)\n'];
strout = [repmat(' ', 1, 72 - numel(tt) - numel(strout) + 5), tt];
fprintf(strout)
% Correct directory?
cd_tmp = cd;
strout = 'Current directory ';
if numel(cd_tmp) > (72 - numel(strout) - 5)
fprintf([strout, '...', cd_tmp((end - (72 - numel(strout) - 5)):end), '\n'])
else
fprintf([strout, repmat(' ', 1, 72 - numel(strout) - numel(cd_tmp)), cd_tmp, '\n'])
end
fprintf(fileID, ['Current directory is ', cd_tmp, '\n']);
% Check if user has writing permissions
[~, struc] = fileattrib;
if ~struc.UserWrite
fprintf(fileID, 'User has no writing permissions!');
error('You do not have writing permissions for the current folder.\n');
end
% Create temporary folder
% ID
id = [num2str(round(rand(1, 1) * 1e4)), '_', num2str(round(rand(1, 1) * 1e4)), '_', num2str(round(rand(1, 1) * 1e4))];
tmpFolder = ['LST_tmp_', id];
mkdir(tmpFolder)
% get job defaults
[job1, job2] = ps_LST_lpa_preproc_default;
% Coregister images
% -----------------
% Copy FLAIR into temporary folder
copyfile(Vf2_tmp.fname, fullfile(tmpFolder, '.'))
if strcmp(extf2, '.img')
copyfile([namf2, '.hdr'], fullfile(tmpFolder, '.'))
end
if coreg% && ~exist(['mr', namf2, '.nii'], 'file')% && ~exist(['r', namf2, extf2], 'file')
fprintf(fileID, 'Coregistration ...');
strout = 'Coregister FLAIR to reference ';
fprintf(strout)
tic
job2.ref = {Vref_tmp.fname};
job2.source = {fullfile(tmpFolder, [namf2, extf2])};
ps_LST_spm_run_coreg(job2);
tt = toc; tt = [num2str(round(tt)), 's'];
strout = [repmat(' ', 1, 72 - numel(tt) - numel(strout)), tt, '\n'];
fprintf(strout)
fprintf(fileID, ' ok.\n');
%f2del = 1;
Vf2_tmp = spm_vol(fullfile(tmpFolder, ['r', namf2, extf2]));
[~, namf2, ~] = fileparts(Vf2_tmp.fname);
Vf2_tmp_or = Vf2_tmp;
else
%f2del = 0;
fprintf(fileID, 'Skipped coregistration.\n');
if coreg
namf2 = ['r', namf2];
end
end
% Calculate bias correction and deformation field
% -----------------------------------------------
%if ~(exist(['m', namf2, '.nii'], 'file') && exist(['iy_', namf2, '.nii'], 'file'))
fprintf(fileID, ['Preprocessing of FLAIR (', namf2, extf2, ') ...']);
strout = ['Preprocessing of ', namf2, extf2, ' '];
fprintf(strout)
tic
job1.channel.vols = {fullfile(tmpFolder, [namf2, extf2])};
ps_LST_spm_preproc_run(job1);
tt = toc; tt = [num2str(round(tt)), 's'];
strout = [repmat(' ', 1, 72 - numel(tt) - numel(strout)), tt, '\n'];
fprintf(strout)
fprintf(fileID, ' ok.\n');
copyfile(fullfile(tmpFolder, ['m', namf2, '.nii']), '.')
%copyfile(fullfile(tmpFolder, [namf2, '.nii']), ['m', namf2, '.nii'])
%else
% fprintf(fileID, 'Skipped preprocessing for FLAIR.\n');
% fprintf('Bias corrected FLAIR and inverse deformation field exist\n');
%end
%if f2del
% spm_unlink(Vf2_tmp.fname);
%end
Vf2_tmp = spm_vol(['m', namf2, '.nii']);
[~, namf2, ~] = fileparts(Vf2_tmp.fname);
% Load some stuff
load(fullfile(spm('dir'), 'toolbox', 'LST', 'LST_lpa_stuff.mat'))
% Warp TPMs and spatial effect to native space
% --------------------------------------------
strout = 'Rough segmentation of FLAIR ';
fprintf(strout)
tic
% Warp brain position of MNI space in native space
fprintf(fileID, 'Warp brain position ... ');
cd(tmpFolder)
bp = ps_LST_lpa_mni2ns(bp_mni, 'bp', Vf2_tmp, [], [], []);
indx_brain = find(bp > 0);
fprintf(fileID, 'ok.\n');
cd ..
% Exclude some background voxels
fprintf(fileID, 'Exclude background voxels ... ');
bg = ps_LST_bc_mni2ns(bg_mni, 'tissue', Vf2_tmp, bp_mni, indx_brain, bp);
indx_brain = find(bp > 0 & bg < 0.3);
fprintf(fileID, 'ok.\n');
clear bg;
% Get TPMs in native space
fprintf(fileID, 'TPMs in native space ... ');
csf = ps_LST_bc_mni2ns(csf_mni, 'tissue', Vf2_tmp, bp_mni, indx_brain, bp);
gm = ps_LST_bc_mni2ns(gm_mni, 'tissue', Vf2_tmp, bp_mni, indx_brain, bp);
wm = ps_LST_bc_mni2ns(wm_mni, 'tissue', Vf2_tmp, bp_mni, indx_brain, bp);
fprintf(fileID, 'ok.\n');
% Rough segmentation of FLAIR into CSF, GM and WM
% -----------------------------------------------
fprintf(fileID, 'Rough segmentation of FLAIR ... ');
% For the calculation of the lesion belief maps we need a rough
% segmentation of the FLAIR image. As the segmentation by SPM is
% quite poor we use our one one. Here, we set up a simple mixture
% model with one Gaussian per tissue class and keep the prior
% probabilities (the TPMs) for these classes fixed.
% For the segmentation we smooth the FLAIR image with a Gaussian
% kernel with FWHM at 1.5 mm
f2 = spm_read_vols(Vf2_tmp);
sf2 = 0 .* f2;
spm_smooth(f2, sf2, [1, 1, 1] .* 1.5);
f2_vec = sf2(indx_brain);
clear sf2;
% Prior probabilities are the TPMs
prior = [csf(indx_brain), gm(indx_brain), wm(indx_brain)];
clear gm;
prior = bsxfun(@times, prior, 1 ./ sum(prior, 2));
post = 0 .* prior;
[m, I] = max(prior, [], 2);
st = 0;
counter = 0;
% Main loop of rough segmentation
while ~st
counter = counter + 1;
% Mean and SD of Gaussians
m = [mean(f2_vec(I == 1)), ...
mean(f2_vec(I == 2)), ...
mean(f2_vec(I == 3))];
s = [std(f2_vec(I == 1)), ...
std(f2_vec(I == 2)), ...
std(f2_vec(I == 3))];
% Posterior probabilities
for j = 1:3
%post(:,j) = normpdf(f2_vec, m(j), s(j)) .* prior(:,j);
post(:,j) = ps_dnorm(f2_vec, m(j), s(j)) .* prior(:,j);
end
post = bsxfun(@times, post, 1 ./ sum(post, 2));
% New class labels
[~, I_new] = max(post, [], 2);
% Correct missclassified voxels, i.e. voxels that are segmented
% as CSF but have intensities larger than the mean of GM
I_new(f2_vec > m(2) & I_new == 1) = 2;
% Check if the label of any voxels have been changed.
if isequal(I, I_new)
st = 1;
else
I = I_new;
end
end % END while ~st
% One last iteration. Here we use the last posterior probability as
% the prior probabilities. This helps in identifying CSF voxels
% better.
for j = 1:3
%post(:,j) = normpdf(f2_vec, m(j), s(j)) .* mean(I_new == j);
post(:,j) = ps_dnorm(f2_vec, m(j), s(j)) .* mean(I_new == j);
end
post = bsxfun(@times, post, 1 ./ sum(post, 2));
[~, I_new] = max(post, [], 2);
I_new(f2_vec > m(2) & I_new == 1) = 2;
seg = 0.*f2;
seg(indx_brain) = I_new;
% Correct voxels in the TPM for CSF
csf(seg > 1) = 0;
fprintf(fileID, ['ok, with ' num2str(counter), ' iterations.\n']);
tt = toc; tt = [num2str(round(tt)), 's'];
strout = [repmat(' ', 1, 72 - numel(tt) - numel(strout)), tt, '\n'];
fprintf(strout)
% Predict probabilities
% ---------------------
strout = 'Predict lesion probabilities ';
fprintf(strout)
tic
fprintf(fileID, 'Lesion belief maps ... ');
% For the lesion belief map we use the original FLAIR image
f2_vec = f2(indx_brain);
tmp = f2_vec(I == 2);
f2_vec = f2_vec ./ mean(tmp(~isnan(tmp)));
% Lesion belief map for WM
Bf2_wm = 0 .* f2;
tmp = f2_vec(I == 3);
Bf2_wm(indx_brain) = (f2_vec - mean(tmp(~isnan(tmp))));
Bf2_wm(Bf2_wm < 0) = 0;
% Lesion belief map for GM
Bf2_gm = 0 .* f2;
tmp = f2_vec(I == 2);
Bf2_gm(indx_brain) = (f2_vec - mean(tmp(~isnan(tmp))));
Bf2_gm(Bf2_gm < 0) = 0;
% The final lesion belief map is weightes by the prior probability
% of WM.
gm = ps_LST_bc_mni2ns(gm2_mni, 'tissue', Vf2_tmp, bp_mni, indx_brain, bp);
wmprob = 1 - (csf + gm);
wmprob(wm < .05) = 0;
clear csf; clear gm; clear wm;
% Final lesion belief map
Bf2 = (Bf2_gm + Bf2_wm) .* wmprob;
Bf2_2 = (Bf2_gm + Bf2_wm);
clear wmprob; clear Bf2_gm; clear Bf2_wm;
fprintf(fileID, 'ok.\n');
%Vf2.fname = 'Bf2.nii';
%spm_write_vol(Vf2, Bf2);
%{
gm_mask = spm_read_vols(spm_vol('/Users/paul/201503_NewSegmentation/Data/gm-mask4.img'));
wm_mask = spm_read_vols(spm_vol('/Users/paul/201503_NewSegmentation/Data/wm-mask.img'));
for z = 10:111
gm_mask(z,:,:) = gm_mask(121-z,:,:);
end
gm2_img = zeros(121, 145, 121); gm2_img(bp_mni) = gm_mni;
gm2_img(gm_mask > 0) = gm2_img(gm_mask > 0) ./ 2;
gm2_mni = gm2_img(bp_mni);
sp = zeros(121,145,121);
sp(bp_mni) = sp_mni_Bf2;
for z = 1:60
tmp = (sp(z,:,:) + sp(121-z,:,:))./2;
sp(z,:,:) = tmp;
sp(121-z,:,:) = tmp;
end
bp_img = 0 .* sp; bp_img(bp_mni) = 1;
sp(abs(sp) < 0.1 & bp_img > 0) = 100;
sp(sp == 0) = NaN;
st = 0;
while ~st
indx_tmp = find(sp > 99);
if isempty(indx_tmp)
st = 1;
else
nh = getNeighborhood2(sp, indx_tmp, 1);
f = find(sum(nh < 99) > 0);
mnh = max(nh .* (nh < 99 & abs(nh) > .05));
sp(indx_tmp(f)) = mnh(f);
end
end
sp(isnan(sp)) = 0;
sp_mni2 = sp(bp_mni);
%}
% Calculate lesion probability
fprintf(fileID, 'Lesion probability maps ... ');
% Warp spatial effect to native space
sp_Bf2 = ps_LST_bc_mni2ns(sp_mni2_Bf2, 'tissue', Vf2_tmp, bp_mni, indx_brain, bp);
% Linear predictor
eta = intercept + Bf2_eff .* Bf2(indx_brain) + sp_Bf2(indx_brain);
prob = 0 .* f2;
% Probabilities
prob(indx_brain) = 1 ./ (1+ exp(-eta));
% In order to obtain a smoother version we smooth all voxels > 0.1
% with a Gaussian kernel with FWHM at 1 mm
prob(prob < 0.1) = NaN;
sprob = 0 .* f2;
spm_smooth(prob, sprob, [1, 1, 1]);
prob(prob > 0) = sprob(prob > 0);
prob(isnan(prob)) = 0;
nl = ps_LST_bc_mni2ns(noles, 'tissue', Vf2_tmp, bp_mni, indx_brain, bp);
prob(nl > 0) = 0;
fprintf(fileID, 'ok.\n');
tt = toc; tt = [num2str(round(tt)), 's'];
strout = [repmat(' ', 1, 72 - numel(tt) - numel(strout)), tt, '\n'];
fprintf(strout)
% Clean up
% --------
strout = 'Clean up ';
fprintf(strout)
tic
% Delete all voxels that have no direct neighbor
indx_les = find(prob > 0);
nh = getNeighborhood2(1 .* (prob > 0), indx_les, 1);
prob(indx_les(sum(nh) == 0)) = 0;
% Delete voxels that are too close to outer CSF voxels
fprintf(fileID, 'Select outer voxels ... ');
% Outer voxels
outerCSF = 0 .* f2;
nh = getNeighborhood2(seg, indx_brain, 3);
outerCSF(indx_brain(sum(nh == 0) > 0)) = 1;
% Add all CSF voxels except for the ventricles
vm = ps_LST_bc_mni2ns(vm_mni, 'tissue', Vf2_tmp, bp_mni, indx_brain, bp);
outerCSF(seg == 1 & vm == 0) = 1;
fprintf(fileID, 'ok.\n');
% In order to calculate the correct distance (in mm) we need to
% correct for different voxel dimensions. We do this by artifically
% blowing the brain up until all voxels have a natural number as
% voxel dimension.
fprintf(fileID, 'Voxel size ...');
% Obtain voxel size
vs = zeros(1, 3);
point1 = cor2mni([1 1 1], Vf2_tmp.mat);
% z-direction
point2 = cor2mni([1 1 2], Vf2_tmp.mat);
vs(3) = sqrt(sum((point2 - point1).^2));
% y-direction
point2 = cor2mni([1 2 1], Vf2_tmp.mat);
vs(2) = sqrt(sum((point2 - point1).^2));
% x-direction
point2 = cor2mni([2 1 1], Vf2_tmp.mat);
vs(1) = sqrt(sum((point2 - point1).^2));
fprintf(fileID, 'ok.\n');
if ~exist('bwdist', 'builtin')
fprintf(fileID, 'Calculate distance ... ');
indx_les = find(prob > 0);
coords_csf = indx2coord(find(outerCSF > 0), size(f2, 1), size(f2, 2));
coords_les = indx2coord(indx_les, size(f2, 1), size(f2, 2));
%tic
d = zeros(numel(indx_les), 1);
for k = 1:size(coords_les, 1)
d_tmp = ((coords_csf(:,1) - coords_les(k,1)) .* vs(1)).^2 + ...
((coords_csf(:,2) - coords_les(k,2)) .* vs(2)).^2 + ...
((coords_csf(:,3) - coords_les(k,3)) .* vs(3)).^2;
d(k) = min(d_tmp);
end
outerCSF_dist = 0 .* f2;
outerCSF_dist(indx_les) = d;
%toc
fprintf(fileID, 'ok.\n');
else
fprintf(fileID, 'Rounded voxel size ...');
% Round voxel size to nearest .5-value
for j = 1:3
ch = .5:.5:5;
[~, mi] = min(abs(vs(j) - ch));
vs(j) = ch(mi);
end
fprintf(fileID, [' ok, ', num2str(vs), '\n']);
fprintf(fileID, 'Factor ...');
% Find factor that gives a natural number for each voxel size
fac_rs = 1;
vs_rs = vs;
while any(mod(vs_rs, 1) > 0)
fac_rs = fac_rs + 1;
vs_rs = vs .* fac_rs;
end
fprintf(fileID, [' ok, ', num2str(fac_rs), '\n']);
fprintf(fileID, 'Translate coordinates ... ');
% Create an empty image with the new voxel size
outerCSF_rs = zeros(size(f2, 1) .* vs_rs(1), ...
size(f2, 2) .* vs_rs(2), ...
size(f2, 3) .* vs_rs(3));
% Translate coordinate of the original image into coordinates of
% the new image
coords = indx2coord(indx_brain, size(f2, 1), size(f2, 2));
coords_rs = coords;
for j = 1:3
if vs_rs(j) > 0
coords_rs(:,j) = round(ps_scale(coords(:,j), ...
min(coords(:,j)), ...
max(coords(:,j)) .* vs_rs(j)));
end
end
% Translate coordinates back to indx
indx_rs = coord2indx(coords_rs(:,1), coords_rs(:,2), coords_rs(:,3), ...
size(f2, 1) .* vs_rs(1), size(f2, 2) .* vs_rs(2));
fprintf(fileID, 'ok.\n');
fprintf(fileID, 'Calculate distance ... ');
% Fill new image
outerCSF_rs(indx_rs) = outerCSF(indx_brain);
% Calculate distance of all voxels to non-zero voxels
outerCSF_rs_dist = bwdist(outerCSF_rs);
clear outerCSF_rs;
% Put calculated distances back to original image
outerCSF_dist = 0 .* f2;
outerCSF_dist(indx_brain) = outerCSF_rs_dist(indx_rs) ./ fac_rs;
clear outerCSF_rs_dist;
fprintf(fileID, 'ok.\n');
end
% Finally: delete all voxels whose distance to outer CSF is smaller
% or equal than 4 mm
fprintf(fileID, 'Delete voxels ... ');
prob2 = prob;
prob2(outerCSF_dist <= 4) = 0;
fprintf(fileID, 'ok.\n');
% Correct for big lesions that are too close at outer CSF
fprintf(fileID, 'Correct for big lesions that are too close at outer CSF ... ');
indx_les = find(prob > 0);
bw = ps_bwlabeln(1. * (prob > 0));
bw_tmp = bw(indx_les);
p_tmp = prob(indx_les);
p2_tmp = prob2(indx_les);
gs1 = arrayfun(@(x) mean(p_tmp(bw_tmp == x)), (1:max(bw_tmp))');
gs2 = arrayfun(@(x) mean(p2_tmp(bw_tmp == x)), (1:max(bw_tmp))');
%gs1 = grpstats(p_tmp, bw_tmp, 'mean');
%gs2 = grpstats(p2_tmp, bw_tmp, 'mean');
indx_correct = find(gs1 > .5 & gs2 < .5 & gs2 > 0);
if ~isempty(indx_correct)
for j = indx_correct'
prob2(bw == j) = prob(bw == j);
end
end
prob = prob2; clear prob2;
fprintf(fileID, 'ok.\n');
% Delete small lesions with low probability
fprintf(fileID, 'Delete small lesions with low probability ... ');
bw = ps_bwlabeln(1 .* (prob > 0));
volfactor = abs(det(Vf2_tmp.mat(1:3,1:3))) / 1000;
for j = 1:max(bw(bw > 0))
indx_tmp = find(bw == j);
if (sum(prob(indx_tmp) > .1)*volfactor < 3*0.003)
prob(indx_tmp) = 0;
end
end
fprintf(fileID, 'ok.\n');
% Delete holes
fprintf(fileID, 'Delete holes ... ');
ch = 0;
iter = 0;
while ~ch
iter = iter + 1;
indx_tmp = find(prob == 0 & bp > 0);
nh = getNeighborhood2(prob, indx_tmp, 1);
indx_tmp = indx_tmp(sum(nh > 0) > 4);
if isempty(indx_tmp) || iter > 10
ch = 1;
else
prob(indx_tmp) = mean(nh(:,sum(nh > 0) > 4));
end
end
fprintf(fileID, ['ok, with ', num2str(iter), ' iterations.\n']);
% A bit of growing
fprintf(fileID, 'Grow a bit ... ');
st = 0;
iter = 0;
while ~st
iter = iter + 1;
indx_tmp = find(Bf2_2 > 0.5 & prob == 0);
nh = getNeighborhood2(prob, indx_tmp, 3);
tmp_tmp = find(mean(nh > 0) > 0.25);
if numel(tmp_tmp) > 10
tmp = nh(:,tmp_tmp); tmp(tmp == 0) = NaN;
prob(indx_tmp(tmp_tmp)) = ps_quantile(tmp, .85);
else
st = 1;
end
end
fprintf(fileID, ['ok, with ', num2str(iter), ' iterations.\n']);
tt = toc; tt = [num2str(round(tt)), 's'];
strout = [repmat(' ', 1, 72 - numel(tt) - numel(strout)), tt, '\n'];
fprintf(strout)
% Write results
% -----------------------------------------------------------------
% Save results in LST.mat
fprintf(fileID, 'Save LST.mat ... ');
lpa.indx_brain = indx_brain;
lpa.bp_indx = bp(indx_brain);
lpa.seg_vec = I_new;
lpa.I = I;
lpa.f2_vec = f2(:);
if coreg
lpa.Vref = Vref_tmp;
end
%Vf2_tmp.dt = [16 0];
lpa.Vf2 = Vf2_tmp;
% Should we flip the images?
V = spm_vol(fullfile(spm('dir'), 'toolbox', 'LST', 'atlas_wm.nii'));
or_ch = zeros(V.dim);
for j = 1:121
or_ch(60,70,j) = 1;
or_ch(61,71,j) = 1;
or_ch(59,69,j) = 1;
or_ch(j,70,60) = 2;
or_ch(j,71,61) = 2;
or_ch(j,69,59) = 2;
end
for j = 1:72
or_ch(60,j,60) = 3;
or_ch(61,j,61) = 3;
or_ch(59,j,59) = 3;
or_ch(60,146-j,60) = 4;
or_ch(61,146-j,61) = 4;
or_ch(59,146-j,59) = 4;
end
cd(tmpFolder)
V.fname = 'or_ch.nii';
spm_write_vol(V, or_ch);
clear job
job.comp{1}.def = {['iy_', namf2(2:end), '.nii']};
job.out{1}.pull.fnames = {'or_ch.nii'};
job.out{1}.pull.savedir.savepwd = 1;
job.out{1}.pull.interp = 0;
job.out{1}.pull.mask = 1;
job.out{1}.pull.fwhm = [0 0 0];
spm_deformations(job);
or_ch = spm_read_vols(spm_vol('wor_ch.nii'));
spm_unlink('wor_ch.nii');
spm_unlink('or_ch.nii');
or_ch(isnan(or_ch)) = 0;
cd ..
c_tmp = indx2coord(find(or_ch == 1), size(or_ch, 1), size(or_ch, 2));
%z = find(ps_range(c_tmp) == max(ps_range(c_tmp)));
z = find(ps_range(c_tmp).*vs == max(ps_range(c_tmp).*vs));
c_tmp = indx2coord(find(or_ch == 2), size(or_ch, 1), size(or_ch, 2));
%x = find(ps_range(c_tmp) == max(ps_range(c_tmp)));
x = find(ps_range(c_tmp).*vs == max(ps_range(c_tmp).*vs));
c_tmp = indx2coord(find(or_ch == 3 | or_ch == 4), size(or_ch, 1), size(or_ch, 2));
%y = find(ps_range(c_tmp) == max(ps_range(c_tmp)));
y = find(ps_range(c_tmp).*vs == max(ps_range(c_tmp).*vs));
or = [x y z];
if any(or == 0)
unique(or(or > 0))
end
% Flip?
if(numel(or) > 2)
or_ch = permute(or_ch, or);
c_tmp3 = indx2coord(find(or_ch == 3), size(or_ch, 1), size(or_ch, 2));
c_tmp4 = indx2coord(find(or_ch == 4), size(or_ch, 1), size(or_ch, 2));
fl = max(c_tmp3(:,2)) > max(c_tmp4(:,2));
lpa.or = or;
lpa.fl = fl;
else
or = [1 2 3];
lpa.or = or;
fl = 0;
lpa.fl = fl;
end
save(['LST_lpa_', namf2, '.mat'], 'lpa')
fprintf(fileID, 'ok.\n');
fprintf(fileID, 'Write results ... ');
Vles = Vf2_tmp_or;
Vles.dt = Vf2_tmp.dt;
Vles.fname = ['ples_lpa_', namf2, '.nii'];
Vles.descrip = 'Probability lesion map obtained by LPA within LST toolbox';
spm_write_vol(Vles, prob);
rmdir(tmpFolder, 's')
fprintf(fileID, 'ok.\n');
if html_report
% HTML report
% -----------------------------------------------------------------
stroutHTML = 'Create HTML report';
fprintf(stroutHTML);
tic
% create HTML report
nameFolder = ['LST_lpa_' namf2];
warning('off');
mkdir(nameFolder)
warning('on');
% Create PNGs
fprintf(fileID, 'Create PNGs ...');
pngFailed = '';
try
[~, r] = ps_LST_create_gif(fullfile(cd, [namf2, '.nii']), ...
Vles.fname, nameFolder, 0, [or, fl]);
fprintf(fileID, ' ok.\n');
catch ME
fprintf(fileID, ' failed!.\n');
pngFailed = ME.message;
r = 0:1;
end
% Create images for glass brains
ps_LST_create_glass_brain(1 .* (prob > .5), ...
1 .* (seg > 0), ...
fullfile(nameFolder, 'gb'), [or, fl]);
fprintf(fileID, 'Create main HTML file ...');
% Main HTML file#
nameHTML = ['report_LST_lpa_', namf2, '.html'];
copyfile(fullfile(spm('dir'), 'toolbox', 'LST', 'LST_main_html.html'), nameHTML)
HTMLid = fopen(nameHTML, 'at');
strout = [' <script src=\"', ps_fullfile(spm('dir'), 'toolbox', 'LST', 'js', 'raphael.js'), '\"></script>\n', ...
' <script src=\"', ps_fullfile(spm('dir'), 'toolbox', 'LST', 'js', 'jquery.min.js'), '\"></script>\n', ...
' <link href=\"', ps_fullfile(spm('dir'), 'toolbox', 'LST', 'js', 'jquery-ui.css'), '\" rel=\"stylesheet\"></script>\n', ...
' <script src=\"', ps_fullfile(spm('dir'), 'toolbox', 'LST', 'js', 'jquery-ui.js'), '\"></script>\n', ...
' </head>\n <body>\n'];
fprintf(HTMLid, strout);
fprintf(fileID, ' ok.\n');
% create subject specific html file
fprintf(fileID, 'Create subject specific HTML file ...');
volfactor = abs(det(Vles.mat(1:3,1:3))) / 1000;
if any(prob(:) > .5)
bw = ps_bwlabeln(1. * (prob > .5));
les_size = zeros(max(bw(:)), 1);
for j = 1:max(bw(:))
les_size(j) = sum(bw(:) == j) * volfactor;
end
les_size = les_size(les_size > 0.015);
else
les_size = [];
end
tlv = sum(les_size);
numles = numel(les_size);
jsid = [nameFolder, '_', ps_create_timestamp];
jsid(regexp(jsid, '\.')) = [];
strout = ['\n<div class=\"container\">\n', ...
' <h1>Lesion segmentation by LST</h1>\n', ...
' <div class=\"column-01\">\n', ...
' <h2>Input summary</h2>\n', ...
' <table style=\"min-width: 500px;\">\n', ...
' <tr>\n', ...
' <td>Date of analysis</td>\n', ...
' <td class=\"ta_right\">', datestr(clock()), '</td>\n', ...
' </tr>\n', ...
' <tr>\n', ...
' <td>Algorithm used for segmentation</td>\n', ...
' <td class=\"ta_right\">LPA</td>\n', ...
' </tr>\n', ...
' <tr>\n', ...
' <td>FLAIR image</td>\n', ...
' <td class=\"ta_right\">', ps_fileparts(ps_shorten_string(Vf2_tmp.fname, 28), 2:3), '</td>\n', ...
' </tr>\n', ...
' <tr>\n', ...
' <td>Reference image</td>\n'];%, ...
if coreg
strout = [strout, ...
' <td class=\"ta_right\">', ps_fileparts(ps_shorten_string(Vref_tmp.fname, 28), 2:3), '</td>\n'];
else
strout = [strout, ...
' <td class=\"ta_right\">none</td>\n'];
end
strout = [strout, ...
' </tr>\n', ...
' </table>\n', ...
' </div>\n', ...
' <div class=\"column-02\">\n', ...
' <h2>Results</h2>\n', ...
' <table style=\"width: 500px\">\n', ...
' <tr>\n', ...
' <td>Lesion map</td>\n', ...
' <td class=\"ta_right\">', Vles.fname, '</td>\n', ...
' </tr>\n', ...
' <tr>\n', ...
' <td>Lesion volume</td>\n', ...
' <td class=\"ta_right\">', num2str(tlv), ' ml</td>\n', ...
' </tr>\n', ...
' <tr>\n', ...
' <td>Number of lesions</td>\n', ...
' <td class=\"ta_right\">', num2str(numles), '</td>\n', ...
' </tr>\n', ...
' </table>\n', ...
' </div>\n', ...
' <div style=\"clear:both\"></div>\n', ...
' <div class=\"column-01\">\n', ...
' <h2>Lesion location</h2>\n', ...%' <img src=\"', fullfile(cd, nameFolder, ['c22_', id, '.png']), '\" width=\"120px\" style=\"vertical-align: top;\">\n', ...
' <img src=\"', ps_fullfile(cd, nameFolder, 'gb_2.png'), '\" width=\"120px\" style=\"vertical-align: top;\">\n', ...
' <img src=\"', ps_fullfile(cd, nameFolder, 'gb_1.png'), '\" width=\"169.41px\" style=\"vertical-align: top;\"><br>\n', ...
' <img src=\"', ps_fullfile(cd, nameFolder, 'gb_3.png'), '\" width=\"120px\" style=\"vertical-align: top;\">\n', ...
' </div>\n', ...
' <div class=\"column-02\" style=\"vertical-align: top;\">\n', ...
' <h2>Overlay</h2>\n'];
if strcmp(pngFailed, '')
strout = [strout, ...
' <script src=\"', ps_fullfile(cd, nameFolder, 'lpa.js'), '\" type=\"text/javascript\"></script>\n', ...
' <img width=\"450px\" id=\"overlay', jsid, '\" src=\"', ps_fullfile(cd, nameFolder, ['overlay_', num2str(round(mean(r))), '.png']), '\" />\n', ...
' <div id=\"slider_', jsid, '\" style=\"width: 450px; text-align: center;\"></div>\n', ...
' <div style=\"width: 450px; text-align: center;\">\n', ...
' <button id=\"button-left', jsid, '\">\n', ...
' <\n', ...
' </button>\n', ...
' <span id="slice', jsid, '">Slice ', num2str(round(mean(r))), '</span>\n', ...
' <button id=\"button-right', jsid, '\">\n', ...
' >\n', ...
' </button>\n', ...
' </div>\n'];
else
strout = [strout, ...
'Sorry, there was a problem when creating the PNG images. MATLAB said: ', pngFailed, '\n'];
end
strout = [strout, ...
' </div>\n', ...
' <div style=\"clear:both\"></div>\n', ...
' </div>\n', ...
'<br><hr>\n']; %% !!
%' $( \"#overlay', jsid, '\" ).attr(\"src\", \"', ps_fullfile(cd, nameFolder, 'overlay_\" + ui.value + \".png\"'), ');\n', ...
%' $( \"#overlay', jsid, '\" ).attr(\"src\", \"', ps_fullfile(cd, nameFolder, ['overlay_\" + slice', jsid, ' + \".png\"']), ');\n', ...
%' $( \"#overlay', jsid, '\").attr(\"src\", \"', ps_fullfile(cd, nameFolder, ['overlay_\" + slice', jsid, ' + \".png\"']), ');\n', ...
JSid = fopen(fullfile(nameFolder, 'lpa.js'), 'wt');
js_strout = ['$(function () {\n', ...
' var min_slice', jsid, ' = ', num2str(r(1)), ',\n', ...
' max_slice', jsid, ' = ', num2str(r(2)), ',\n', ...
' slice', jsid, ' = ', num2str(round(mean(r))), ';\n', ...
' $( \"#slider_', jsid, '\" ).slider({\n', ...
' min: min_slice', jsid, ',\n', ...
' max: max_slice', jsid, ',\n', ...
' value: slice', jsid, ',\n', ...
' slide: function( event, ui ) {\n', ...
' slice', jsid, ' = ui.value;\n', ...
' $( \"#overlay', jsid, '\" ).attr(\"src\", \"', ps_fullfile(cd, nameFolder), '/overlay_\" + ui.value + \".png\"', ');\n', ...
' $( \"#slice', jsid, '\" ).text(\"Slice \" + ui.value);\n', ...
' }\n', ...
' });\n', ...
' $( \"#button-left', jsid, '\" ).button({\n', ...
' icons: {\n', ...
' primary: \"ui-icon-carat-1-w\"\n', ...
' },\n', ...
' text: false,\n', ...
' }).click(function(event, ui){\n', ...
' if(slice', jsid, ' > min_slice', jsid, '){\n', ...
' slice', jsid, ' = slice', jsid, ' - 1;\n', ...
' $( \"#overlay', jsid, '\" ).attr(\"src\", \"', ps_fullfile(cd, nameFolder), ['/overlay_\" + slice', jsid, ' + \".png\"'], ');\n', ...
' $( \"#slice', jsid, '\" ).text(\"Slice \" + slice', jsid, ');\n', ...
' $(\"#slider_', jsid, '\").slider(\"option\", "value", slice', jsid, ');\n', ...
' }\n', ...
' });\n', ...
' $(\"#button-right', jsid, '\").button({\n', ...
' icons: {\n', ...
' primary: \"ui-icon-carat-1-e\"\n', ...
' },\n', ...
' text: false,\n', ...
' }).click(function(event, ui){\n', ...
' if(slice', jsid, ' < max_slice', jsid, '){\n', ...
' slice', jsid, ' = slice', jsid, ' + 1;\n', ...
' $( \"#overlay', jsid, '\").attr(\"src\", \"', ps_fullfile(cd, nameFolder), ['/overlay_\" + slice', jsid, ' + \".png\"'], ');\n', ...
' $( \"#slice', jsid, '\").text(\"Slice \" + slice', jsid, ');\n', ...
' $(\"#slider_', jsid, '\").slider(\"option\", \"value\", slice', jsid, ');\n', ...
' }\n', ...
' });', ...
'});'];
fprintf(JSid, js_strout);
fclose(JSid);
%HTMLid = fopen(fullfile(nameFolder, [id, '.html']), 'wt');
fprintf(HTMLid, strout);
HTMLid2 = fopen(fullfile(nameFolder, ['LST_lpa_', namf2, '.html']), 'wt');
fprintf(HTMLid2, strout);
fclose(HTMLid2);
strout = ' </head>\n <body>\n';
fprintf(HTMLid, strout);
fclose(HTMLid);
fprintf(fileID, ' ok.\n');
tt = toc; tt = [num2str(round(tt)), 's'];
stroutHTML = [repmat(' ', 1, 72 - numel(tt) - numel(stroutHTML)), tt, '\n'];
fprintf(stroutHTML)
end
end
% delete log file if segmentation terminated successfully
fclose(fileID);
cd(pthor)
spm_unlink(nameLog);