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reportResults.m
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%% ---- Report Results ---- %%
% This function is to report the results for the
% study "The Reproducibility of deep learning-based segmentation of the
% prostate on T2-weighted MR images".
%
% Sunoqrot, M.R.S.; Selnæs, K.M.; Sandsmark, E.; Langørgen, S.; Bertilsson,
% H.; Bathen, T.F.; Elschot, M. The Reproducibility of Deep Learning-Based
% Segmentation of the Prostate Gland and Zones on T2-Weighted MR Images.
% Diagnostics 2021, 11, 1690. https://doi.org/10.3390/diagnostics11091690
% https://www.mdpi.com/2075-4418/11/9/1690
%
% By Mohammed R. S. Sunoqrot, MR cancer group, NTNU, Trondheim, Norway
% 09.Nov.2020
%
% Input:
% 1. masterPath: The path to the master analysis file. (string)
% 2. stats: The statistical analysis results. (structure)
%
% output:
% Graphs, figures and tables saved to "Report" folder
%
function reportResults(masterPath,stats)
% %---- Main investigation
%% Make report folder
rpP = fullfile(masterPath,'Report','Main');
if ~exist(rpP,'dir')
mkdir(rpP)
end
%% Scanning protocol parameters of the investigation set
protocolSummary = repProtocol(stats.protocol,rpP);
%% Scanning protocol parameters of the Training set
repProtocolTtr(stats.protocolTr,rpP);
%% Segmentation example
repSeg(masterPath,rpP)
%% ICC
repICC(stats.ICC,stats.sICC,rpP);
%% CV
repCV(stats.CV,stats.sCV,rpP);
%% Perfroamcne score
repPS(stats.pScores.pScores,stats.sScores,rpP);
%% Number of slices
repNS(stats.nSlices,stats.sSlices,rpP);
%% Volume
repVL(stats.Volume,stats.sVolume,...
stats.cVolume,stats.sCvolume,stats.CV,protocolSummary,rpP);
%% Bland-altman analysis of Volume
blandaltmanVolume(stats.Volume,rpP);
%% Investigate Parameters
invp(stats.protocol,stats.CV);
%---- After Impleminting QC system
%% Make report folder
rpP = fullfile(masterPath,'Report','QC');
if ~exist(rpP,'dir')
mkdir(rpP)
end
%% ICC
repICC(stats.ICCAE,stats.sICCAE,rpP);
%% CV
repCV(stats.CVAE,stats.sCVAE,rpP);
%% Perfroamcne score
repPS(stats.pScoresAE,stats.sScoresAE,rpP);
%% Number of slices
repNS(stats.nSlicesAE,stats.sSlicesAE,rpP);
%---- Before including post-processing step
%% Make report folder
rpP = fullfile(masterPath,'Report','PP');
if ~exist(rpP,'dir')
mkdir(rpP)
end
%% ICC
repICC(stats.ICCBPP,stats.sICCBPP,rpP);
%% CV
repCV(stats.CVBPP,stats.sCVBPP,rpP);
%% Perfroamcne score
repPS(stats.pScoresBPP.pScores,stats.sScoresBPP,rpP);
%% Number of slices
repNS(stats.nSlicesBPP,stats.sSlicesBPP,rpP);
%% Volume
repVL(stats.VolumeBPP,stats.sVolumeBPP,...
stats.cVolumeBPP,stats.sCvolumeBPP,stats.CVBPP,protocolSummary,rpP);
end
%% repProtocol
% This function is to report the scanning protocol parameters
% for Scan 1 and Scan 2 cases
%
% Input:
% 1. protocol: The scanning protocol parameters. (structure)
% 2. rpP: The report folder path. (string)
%
% Output:
% 1. protocolSummary: The summary of the scanning protocol parameters. (structure)
%
function protocolSummary = repProtocol(protocol,rpP)
% --Summary
% Make table to fill
rp.summary = table;
% Loop over scans
scans = fieldnames(protocol);
for kk = 1:numel(scans)
% Loop over the parameters
pars = protocol.(scans{kk}).Properties.VariableNames;
for pp = 1:numel(pars)
% Don't summaraize the exceptions
exception = {'Name','ScanDate','PatientAge'};
if ~ismember(pars{pp},exception)
% Treat cell arrays based on uniqueness
if iscell(protocol.(scans{kk}).(pars{pp}))
rp.summary.(pars{pp})(kk,:) =...
{unique(cell2mat(protocol.(scans{kk}).(pars{pp})),'rows')};
% Treat double arrays based on max and min
else
minTmp = min(protocol.(scans{kk}).(pars{pp}));
maxTmp = max(protocol.(scans{kk}).(pars{pp}));
% Take range if the values are different
if minTmp ~= maxTmp
rp.summary.(pars{pp})(kk,:) =...
{[num2str(minTmp) ' - ' num2str(maxTmp)]};
else
rp.summary.(pars{pp})(kk,:) = {num2str(minTmp)};
end
end
end
end
% Assign the table rows
rp.summary.Properties.RowNames{kk} = scans{kk};
end
%-- Patients
% Compine the scans patients
PatientAge = [protocol.Scan1.PatientAge;protocol.Scan2.PatientAge];
rp.PatientAge = ['Median = ' num2str(median(PatientAge))...
' Range = ' [num2str(min(PatientAge)) ' - ' num2str(max(PatientAge))]];
%-- Time interval
rp.TimeInterval = datenum(protocol.Scan2.ScanDate)-...
datenum(protocol.Scan1.ScanDate);
rp.TimeIntervalS = ['Median = ' num2str(median(rp.TimeInterval))...
' Range = ' [num2str(min(rp.TimeInterval)) ' - ' num2str(max(rp.TimeInterval))]];
% Save
protocolSummary = rp;
save(fullfile(rpP,'protocolSummary.mat'),'protocolSummary')
end
%% repProtocolTr
% This function is to report the scanning protocol parameters
% for the traininng set
%
% Input:
% 1. protocol: The scanning protocol parameters. (table)
% 2. rpP: The report folder path. (string)
%
% Output:
% protocolSummaryTr (table) saved in rpP
%
function repProtocolTtr(protocol,rpP)
% --Summary
% Make table to fill
rp.summary = table;
% Loop over the parameters
pars = protocol.Properties.VariableNames;
for pp = 1:numel(pars)
% Don't summaraize the exceptions
exception = {'Name','ScanDate','PatientAge'};
if ~ismember(pars{pp},exception)
% Treat cell arrays based on uniqueness
if iscell(protocol.(pars{pp}))
rp.summary.(pars{pp}) =...
{unique(cell2mat(protocol.(pars{pp})),'rows')};
% Treat double arrays based on max and min
else
minTmp = min(protocol.(pars{pp}));
maxTmp = max(protocol.(pars{pp}));
% Take range if the values are different
if minTmp ~= maxTmp
rp.summary.(pars{pp}) =...
{[num2str(minTmp) ' - ' num2str(maxTmp)]};
else
rp.summary.(pars{pp}) = {num2str(minTmp)};
end
end
end
end
%-- Patients
% Compine the scans patients
PatientAge = protocol.PatientAge;
rp.PatientAge = ['Median = ' num2str(median(PatientAge))...
' Range = ' [num2str(min(PatientAge)) ' - ' num2str(max(PatientAge))]];
% Save
protocolSummaryTr = rp;
save(fullfile(rpP,'protocolSummaryTr.mat'),'protocolSummaryTr')
end
%% repSeg
% This function is to show examples on the manual and automated
% segmentations
%
% Input:
% 1. masterPath: The path to the master analysis file. (string)
% 2. rpP: The report folder path. (string)
%
% output:
% Figured saved to "Report" folder
%
function repSeg(masterPath,rpP)
% Paths
imP = fullfile(masterPath,'Data','Normalized');
msPath = fullfile(masterPath,'Data','Segmentation','Manual','Final');
esPath = fullfile(masterPath,'Data','Segmentation','AutomatedPP');
% Nets
esN = dir(fullfile(esPath,'*Net*'));
esN = {esN.name}.';
mN = {'Manual'};
nets = [mN;esN];
% Go for it
% Scan1
figure('Name','Scan1 example');
for ii = 1:numel(nets)
% Paths
im = fullfile(imP,'Scan1','Case001_Normalized.mhd');
if contains(nets{ii},'Manual')
p = fullfile(msPath,'Scan1','PZ','Case001_Segmentation.mhd');
t = fullfile(msPath,'Scan1','TZ','Case001_Segmentation.mhd');
else
p = fullfile(esPath,nets{ii},'PZ','Scan1','Case001_Segmentation.mhd');
t = fullfile(esPath,nets{ii},'TZ','Scan1','Case001_Segmentation.mhd');
end
% Read .mhd
[si, ~, ~] = elxMetaIOFileToStrDatax(im, 0);
si = elxStrDataxToIm3d(si);
[sP, ~, ~] = elxMetaIOFileToStrDatax(p, 0);
sP = elxStrDataxToIm3d(sP);
[sT, ~, ~] = elxMetaIOFileToStrDatax(t, 0);
sT = elxStrDataxToIm3d(sT);
% selected slices
num = [8,11,15];
% for subplot
if ii == 1
ff = 1:3;
elseif ii == 2
ff = 4:6;
elseif ii == 3
ff = 7:9;
elseif ii == 4
ff = 10:12;
end
% subplot
for jj = 1:numel(num)
% the image
im = si.Data(:,:,num(jj));
im = im(round(size(im,1)*.3):round(size(im,1)*.7),round(size(im,2)*.3):round(size(im,2)*.7));
im = uint8(im);
% the masks
pz = sP.Data(:,:,num(jj));
pz = pz(round(size(pz,1)*.3):round(size(pz,1)*.7),round(size(pz,2)*.3):round(size(pz,2)*.7));
tz = sT.Data(:,:,num(jj));
tz = tz(round(size(tz,1)*.3):round(size(tz,1)*.7),round(size(tz,2)*.3):round(size(tz,2)*.7));
if sum(sum(pz))>0 && sum(sum(tz))>1
pz = pz;
tz = tz;
elseif sum(sum(pz))>0 && sum(sum(tz))<1
pz = pz;
tz(1) = 1;
elseif sum(sum(tz))>0 && sum(sum(pz))<1
tz = tz;
pz(1) = 1;
end
L = (2*pz)+tz;
% subplot
subplot(numel(nets),numel(num),ff(jj))
B = labeloverlay(im,L,'Colormap','lines','Transparency',0.7);
imshow(B,[]);
clear im pz tz L B
end
end
% Save
savefig(fullfile(rpP,'Scan1example.fig'))
clear im p t si sP sT num L pz tz B
% Scan2
figure('Name','Scan2 example');
for ii = 1:numel(nets)
% Paths
im = fullfile(imP,'Scan2','Case001_Normalized.mhd');
if contains(nets{ii},'Manual')
p = fullfile(msPath,'Scan2','PZ','Case001_Segmentation.mhd');
t = fullfile(msPath,'Scan2','TZ','Case001_Segmentation.mhd');
else
p = fullfile(esPath,nets{ii},'PZ','Scan2','Case001_Segmentation.mhd');
t = fullfile(esPath,nets{ii},'TZ','Scan2','Case001_Segmentation.mhd');
end
% Read .mhd
[si, ~, ~] = elxMetaIOFileToStrDatax(im, 0);
si = elxStrDataxToIm3d(si);
[sP, ~, ~] = elxMetaIOFileToStrDatax(p, 0);
sP = elxStrDataxToIm3d(sP);
[sT, ~, ~] = elxMetaIOFileToStrDatax(t, 0);
sT = elxStrDataxToIm3d(sT);
% selected slices
num = [9,12,16];
% for subplot
if ii == 1
ff = 1:3;
elseif ii == 2
ff = 4:6;
elseif ii == 3
ff = 7:9;
elseif ii == 4
ff = 10:12;
end
% subplot
for jj = 1:numel(num)
% the image
im = si.Data(:,:,num(jj));
im = im(round(size(im,1)*.3):round(size(im,1)*.7),round(size(im,2)*.3):round(size(im,2)*.7));
im = uint8(im);
% the masks
pz = sP.Data(:,:,num(jj));
pz = pz(round(size(pz,1)*.3):round(size(pz,1)*.7),round(size(pz,2)*.3):round(size(pz,2)*.7));
tz = sT.Data(:,:,num(jj));
tz = tz(round(size(tz,1)*.3):round(size(tz,1)*.7),round(size(tz,2)*.3):round(size(tz,2)*.7));
if sum(sum(pz))>0 && sum(sum(tz))>1
pz = pz;
tz = tz;
elseif sum(sum(pz))>0 && sum(sum(tz))<1
pz = pz;
tz(1) = 1;
elseif sum(sum(tz))>0 && sum(sum(pz))<1
tz = tz;
pz(1) = 1;
end
L = (2*pz)+tz;
% subplot
subplot(numel(nets),numel(num),ff(jj))
B = labeloverlay(im,L,'Colormap','lines','Transparency',0.7);
imshow(B,[]);
clear im pz tz L B
end
end
% Save
savefig(fullfile(rpP,'Scan2example.fig'))
end
%% repICC
% This function is to report the ICC results
%
% Input:
% 1. ICC: The calculated ICC results. (structure)
% 2. sICC: The significance of ICC changes. (structure)
% 3. rpP: The report folder path. (string)
%
function repICC(ICC,sICC,rpP)
% --Overview figures by region
% Get Netwroks names
nets = fieldnames(ICC);
% Loop over Netwroks
for ii = 1:numel(nets)
% Get regions and features names
regs = ICC.(nets{ii}).ICC.Properties.VariableNames;
fNames = ICC.(nets{ii}).ICC.Properties.RowNames;
% Combine data in one array
for jj = 1:numel(regs)
data.ICC.(regs{jj})(:,ii) = ICC.(nets{ii}).ICC.(regs{jj})';
data.ciLow.(regs{jj})(:,ii) = cell2mat(cellfun(@(x) x(1),...
ICC.(nets{ii}).ICCCI.(regs{jj}), 'un', 0));
data.ciHigh.(regs{jj})(:,ii) = cell2mat(cellfun(@(x) x(2),...
ICC.(nets{ii}).ICCCI.(regs{jj}), 'un', 0));
end
end
% Figure
figure('Name','ICC')
% Master title
% sgtitle('ICC for regions')
% Loop over regions
for jj = 1:numel(regs)
% Plot
subplot(numel(regs),1,jj)
% Plot groups bar
hBar = bar(1:14,data.ICC.(regs{jj}));
% limits
lm = [-0.15,1];
ylim(lm)
% rotate x ticks
xtickangle(15)
% Add title and labels
title(regs{jj})
ylabel('ICC')
% Turn grid
grid on
hold on
% Calculate the number of bars in each group
nbars = size(data.ICC.(regs{jj}), 2);
% Get the x coordinate of the bars
t.x = [];
for i = 1:nbars
t.x = [t.x ; hBar(i).XEndPoints];
end
% Plot the errorbars
errorbar(t.x',data.ICC.(regs{jj}),...
abs(data.ciLow.(regs{jj})-data.ICC.(regs{jj})),...
abs(data.ciHigh.(regs{jj})-data.ICC.(regs{jj})),...
'k', 'linestyle', 'none')
% Change x ticks labels
xticklabels(fNames')
% Add significany signs
tx = t.x';
counter = 0;
% between nets
for j = 1:numel(nets)
for i = 1:numel(fNames)
sN = sICC.Nets.(nets{j}).(regs{jj})(i);
if sN
counter = counter+1;
LL = [tx(i,1) tx(i,j)];
% add line between them
ld = lm(2)-lm(1);
plot([LL(1),LL(2)],[lm(1)+ld*(j/40) lm(1)+ld*(j/40)],...
'.-','color',[0.333,0.333,0.333])
end
end
end
hold off
% Add background
for i = 2:2:numel(fNames)
n = (numel(fNames)/2)/numel(fNames);
%m = get(gca,'YTick');
%patch([i-n,i+n,i+n,i-n],[m(1),m(1),m(end),m(end)],[0.9 0.9 0.9],'FaceAlpha',0.5)
patch([i-n,i+n,i+n,i-n],[lm(1) lm(1) lm(2) lm(2)],[0.333 0.333 0.333],...
'FaceAlpha',0.1,'LineStyle','none')
end
% Add legends
Nnets = replace(nets,'_','');
% legend([Nnets','below Poor','below Moderate','below Good'],...
% 'Location','northeastoutside')
f = get(gca,'Children');
if counter>0
legend([hBar,f(numel(f)-numel(Nnets)),f(round(numel(fNames)/2)+1)],...
[Nnets','95%CI','Significant'],'Location','northeastoutside')
else
legend([hBar,f(numel(f)-numel(Nnets))],...
[Nnets','95%CI'],'Location','northeastoutside')
end
end
% Save
savefig(fullfile(rpP,'ICC.fig'))
end
%% repCV
% This function is to report the CV results
%
% Input:
% 1. CV: The calculated CV results. (structure)
% 2. sCV: The significance of CV changes. (structure)
% 3. rpP: The report folder path. (string)
%
function repCV(CV,sCV,rpP)
% --Overview figures by region
% Get Netwroks names
nets = fieldnames(CV);
% Loop over Netwroks
for ii = 1:numel(nets)
% Get regions and features names
regs = fieldnames(CV.(nets{ii}));
fNames = CV.(nets{ii}).(regs{1}).Properties.VariableNames;
% Combine data in one array
for jj = 1:numel(regs)
for ff = 1:numel(fNames)
data.tempF = table2array(CV.(nets{ii}).(regs{jj})(:,ff));
for nn = 1:size(CV.(nets{ii}).(regs{jj}),1)
data.(regs{jj})(ff,ii,nn) = data.tempF(nn);
end
end
end
end
% Figure
figure('Name','CV')
% Master titles
sgtitle('CV for regions')
% Loop over regions
for jj = 1:numel(regs)
% Plot
subplot(numel(regs),1,jj)
% Data
dd = data.(regs{jj});
% Limits
lm = [min(min(min(dd))),max(max(max(dd)))];
% Plot
h = boxplot2(dd,1:numel(fNames),lm,fNames);
ylim(lm)
hold on
% Alter linestyle and color
cmap = get(0, 'defaultaxescolororder');
for ii = 1:numel(nets)
structfun(@(x) set(x(ii,:), 'color', cmap(ii,:), ...
'markeredgecolor', cmap(ii,:)), h);
end
set([h.lwhis h.uwhis], 'linestyle', '-');
set(h.out, 'marker', '.');
% Change x ticks labels
set(gca,'XTick',1:numel(fNames),'XTickLabel',fNames)
xtickangle(15)
% Titles and labels
title(regs{jj})
ylabel('CV')
% Reference lines
yline(0.3,'--r', 'LineWidth',1.2);
yline(0.2,'--m', 'LineWidth',1.2);
yline(0.1,'--b', 'LineWidth',1.2);
% Add significany signs
% between nets
counter = 0;
for i = 1:numel(fNames)
sN = sCV.Nets.(regs{jj}).(fNames{i});
for j = 1:numel(nets)-1
if sN(j)<0.05
counter = counter+1;
LL = [(h.box(((numel(nets)*i)-numel(nets)+1)).XData(3)+...
h.box(((numel(nets)*i)-numel(nets)+1)).XData(1))/2,...
(h.box(((numel(nets)*i)-numel(nets)+1+j)).XData(3)+...
h.box(((numel(nets)*i)-numel(nets)+1)+j).XData(1))/2];
% add line between them
ld = lm(2)-lm(1);
plot([LL(1),LL(2)],[lm(2)-ld*(j/40) lm(2)-ld*(j/40)],...
'.-','color',[0.333,0.333,0.333])
end
end
end
% Add legends
Nnets = replace(nets,'_','');
f = get(gca,'Children');
legend([h.box(1:numel(Nnets),1);f(1);f(counter+1:counter+3)],...
[Nnets','{\it p} <0.05','Below excellent','Below good','Below acceptable'],...
'Location','northeastoutside')
hold off
end
% Save
savefig(fullfile(rpP,'CV.fig'))
end
%% repPS
% This function is to report the performance scores results
%
% Input:
% 1. pScores: The calculated performance scores for the cases. (structure)
% 2. sScores: The significance of performance scores changes. (structure)
% 3. rpP: The report folder path. (string)
%
function repPS(pScores,sScores,rpP)
% --Overview figures by region
% Get Netwroks names
nets = fieldnames(pScores);
% Loop over Netwroks
for ii = 1:numel(nets)
% Get regions and features names
regs = fieldnames(pScores.(nets{ii}));
% Loop over regions
for jj = 1:numel(regs)
% Loop over scans
scans = fieldnames(pScores.(nets{ii}).(regs{jj}));
for kk = 1:numel(scans)
for nn = 1:size(pScores.(nets{ii}).(regs{jj}).(scans{kk}).totalScore,1)
data.(regs{jj}).DSC(kk,ii,nn) =...
pScores.(nets{ii}).(regs{jj}).(scans{kk}).WP.DSC(nn);
data.(regs{jj}).total(kk,ii,nn) =...
pScores.(nets{ii}).(regs{jj}).(scans{kk}).totalScore(nn);
end
end
end
end
% Figure
figure('Name','Performance Scores')
% Master titles
sgtitle('Performance Scores')
% Loop over regions
for jj = 1:numel(regs)
ss = 0:numel(scans);
% ---Plot DSC
subplot(numel(regs),2,jj+ss(jj))
% Plot boxplots
% Group names
Nnets = replace(nets,'_','');
% Data
dd = data.(regs{jj}).DSC;
% Limits
lm = [round(min(min(min(dd))),1),1];
% Plot
h = boxplot2(dd,1:numel(scans),lm,Nnets');
ylim(lm)
hold on
% Alter linestyle and color
cmap = get(0, 'defaultaxescolororder');
for ii = 1:numel(nets)
structfun(@(x) set(x(ii,:), 'color', cmap(ii,:), ...
'markeredgecolor', cmap(ii,:)), h);
end
set([h.lwhis h.uwhis], 'linestyle', '-');
set(h.out, 'marker', '.');
% Change x ticks labels
set(gca,'XTick',1:numel(nets),'XTickLabel',scans)
% Titles and labels
title(regs{jj})
ylabel('DSC')
% Add significany signs
% bwtween scans
sS = sScores.Scans.WP.DSC.(regs{jj});
for i = 1:numel(nets)
if sS(i)<0.05
LL = [(h.box(i).XData(3)+h.box(i).XData(1))/2,...
(h.box(i+numel(nets)).XData(3)+h.box(i+numel(nets)).XData(1))/2];
% add line between them
ld = lm(2)-lm(1);
plot([LL(1),LL(2)],[lm(2)-ld*(i/40) lm(2)-ld*(i/40)],...
'.-','color',[0.333,0.333,0.333])
end
end
% Add legend
f = get(gca,'Children');
legend([h.box(1:numel(Nnets),1);f(1)],[Nnets','{\it p} <0.05'],...
'Location','northeastoutside')
hold off
% ---Plot Total
subplot(numel(regs),numel(scans),jj+ss(jj)+1)
% Plot boxplots
% Data
dd = data.(regs{jj}).total;
% Limits
lm = [floor(min(min(min(dd)))),100];
% Plot
h = boxplot2(dd,1:numel(scans),lm,Nnets');
ylim(lm)
hold on
% Alter linestyle and color
cmap = get(0, 'defaultaxescolororder');
for ii = 1:numel(nets)
structfun(@(x) set(x(ii,:), 'color', cmap(ii,:), ...
'markeredgecolor', cmap(ii,:)), h);
end
set([h.lwhis h.uwhis], 'linestyle', '-');
set(h.out, 'marker', '.');
% Change x ticks labels
set(gca,'XTick',1:numel(nets),'XTickLabel',scans)
% Titles and labels
title(regs{jj})
ylabel('Total score')
% Add significany signs
% between scans
sS = sScores.Scans.totalScore.(regs{jj});
for i = 1:numel(nets)
if sS(i)<0.05
LL = [(h.box(i).XData(3)+h.box(i).XData(1))/2,...
(h.box(i+numel(nets)).XData(3)+h.box(i+numel(nets)).XData(1))/2];
% add line between them
ld = lm(2)-lm(1);
plot([LL(1),LL(2)],[lm(2)-ld*(i/40) lm(2)-ld*(i/40)],...
'.-','color',[0.333,0.333,0.333])
end
end
% Add legend
f = get(gca,'Children');
legend([h.box(1:numel(Nnets),1);f(1)],[Nnets','{\it p} <0.05'],...
'Location','northeastoutside')
hold off
% % Change lines width for all graph
% lines = findobj(gcf,'Type','Line');
% for i = 1:numel(lines)
% lines(i).LineWidth = 2.0;
% end
end
% Save
savefig(fullfile(rpP,'Performance Scores.fig'))
% Figure
figure('Name','Performance DSC Scores')
% Master titles
% sgtitle('Performance Scores')
% Loop over regions
for jj = 1:numel(regs)
% ---Plot DSC
subplot(numel(regs),1,jj)
% Plot boxplots
% Group names
Nnets = replace(nets,'_','');
% Data
dd = data.(regs{jj}).DSC;
% Limits
lm = [round(min(min(min(dd))),1),1.15];
% Plot
h = boxplot2(dd,1:numel(scans),lm,Nnets');
ylim(lm)
hold on
% Alter linestyle and color
cmap = get(0, 'defaultaxescolororder');
for ii = 1:numel(nets)
structfun(@(x) set(x(ii,:), 'color', cmap(ii,:), ...
'markeredgecolor', cmap(ii,:)), h);
end
set([h.lwhis h.uwhis], 'linestyle', '-');
set(h.out, 'marker', '.');
% Change x ticks labels
set(gca,'XTick',1:numel(nets),'XTickLabel',scans)
% Titles and labels
title(regs{jj})
ylabel('DSC')
% Add significany signs
% bwtween scans
sS = sScores.Scans.WP.DSC.(regs{jj});
for i = 1:numel(nets)
if sS(i)<0.05
LL = [(h.box(i).XData(3)+h.box(i).XData(1))/2,...
(h.box(i+numel(nets)).XData(3)+h.box(i+numel(nets)).XData(1))/2];
% add line between them
ld = lm(2)-lm(1);
plot([LL(1),LL(2)],[lm(2)-ld*(i/40) lm(2)-ld*(i/40)],...
'.-','color',[0.333,0.333,0.333])
end
end
% between nets
counter = 0;
for i = 1:numel(scans)
sN = sScores.(['NetsScan' num2str(i)]).WP.DSC.(regs{jj});
for j = 1:numel(nets)-1
if sN(j)<0.05
counter = counter+1;
LL = [(h.box(((numel(nets)*i)-numel(nets)+1)).XData(3)+...
h.box(((numel(nets)*i)-numel(nets)+1)).XData(1))/2,...
(h.box(((numel(nets)*i)-numel(nets)+1+j)).XData(3)+...
h.box(((numel(nets)*i)-numel(nets)+1)+j).XData(1))/2];
% add line between them
plot([LL(1),LL(2)],[lm(1)+ld*(j/40) lm(1)+ld*(j/40)],...
'.--','color',[0.333,0.333,0.333])
end
end
for j = numel(nets)
if sN(j)<0.05
counter = counter+1;
LL = [(h.box(((numel(nets)*i)-numel(nets)+2)).XData(3)+...
h.box(((numel(nets)*i)-numel(nets)+2)).XData(1))/2,...
(h.box(((numel(nets)*i)-numel(nets)+j)).XData(3)+...
h.box(((numel(nets)*i)-numel(nets))+j).XData(1))/2];
% add line between them
plot([LL(1),LL(2)],[lm(1)+ld*(j/40) lm(1)+ld*(j/40)],...
'.--','color',[0.333,0.333,0.333])
end
end
end
% Add legend
f = get(gca,'Children');
legend([h.box(1:numel(Nnets),1);f(1);f(counter+1)],...
[Nnets','{\it p} <0.05 between netwroks','{\it p} <0.05 between scans'],...
'Location','northeastoutside')
hold off
% % Change lines width for all graph
% lines = findobj(gcf,'Type','Line');
% for i = 1:numel(lines)
% lines(i).LineWidth = 2.0;
% end
end
% Save
savefig(fullfile(rpP,'Performance DSC Scores.fig'))
end
%% repNS
% This function is to report the number of slices results
%
% Input:
% 1. nSlices: The numbers of the included slices. (structure)
% 2. sSlices: The significance of slices numbers change. (structure)
% 3. rpP: The report folder path. (string)
%
function repNS(nSlices,sSlices,rpP)
% --Overview figures by region
% Get Netwroks names
nets = fieldnames(nSlices);
% Loop over Netwroks
for ii = 1:numel(nets)
% Get regions and features names
regs = fieldnames(nSlices.(nets{ii}));
% Loop over regions
for jj = 1:numel(regs)
% Loop over scans
scans = fieldnames(nSlices.(nets{ii}).(regs{jj}));
for kk = 1:numel(scans)
for nn = 1:size(nSlices.(nets{ii}).(regs{jj}).(scans{kk}),1)
data.(regs{jj})(kk,ii,nn) =...
nSlices.(nets{ii}).(regs{jj}).(scans{kk})(nn);
end
end
end
end
% Figure
figure('Name','Number of slices')
% Master title
sgtitle('Number of mask slices')
% Loop over regions
for jj = 1:numel(regs)
subplot(numel(regs),1,jj)
% Plot boxplots
% Group names
Nnets = replace(nets,'_','');
% Data
dd = data.(regs{jj});
% Limits
lm = [min(min(min(dd)))-2,max(max(max(dd)))+2];
% Plot
h = boxplot2(dd,1:numel(scans),lm,Nnets');
ylim(lm)
hold on
% Alter linestyle and color
cmap = get(0, 'defaultaxescolororder');
for ii = 1:numel(nets)
structfun(@(x) set(x(ii,:), 'color', cmap(ii,:), ...
'markeredgecolor', cmap(ii,:)), h);
end
set([h.lwhis h.uwhis], 'linestyle', '-');
set(h.out, 'marker', '.');
% Change x ticks labels
set(gca,'XTick',1:numel(nets),'XTickLabel',scans)
% Titles and labels
title(regs{jj})
ylabel('Number of mask slices')
% Add significany signs
% bwtween scans
sS = sSlices.Scans.(regs{jj});
for i = 1:numel(nets)
if sS(i)<0.05
LL = [(h.box(i).XData(3)+h.box(i).XData(1))/2,...
(h.box(i+numel(nets)).XData(3)+h.box(i+numel(nets)).XData(1))/2];
% add line between them
ld = lm(2)-lm(1);
plot([LL(1),LL(2)],[lm(2)-ld*(i/40) lm(2)-ld*(i/40)],...
'.-','color',[0.333,0.333,0.333])
end
end
% between nets
counter = 0;
for i = 1:numel(scans)
sN = sSlices.(['NetsScan' num2str(i)]).(regs{jj});
for j = 1:numel(nets)-1
if sN(j)<0.05
counter = counter+1;
LL = [(h.box(((numel(nets)*i)-numel(nets)+1)).XData(3)+...
h.box(((numel(nets)*i)-numel(nets)+1)).XData(1))/2,...
(h.box(((numel(nets)*i)-numel(nets)+1+j)).XData(3)+...
h.box(((numel(nets)*i)-numel(nets)+1)+j).XData(1))/2];
% add line between them
plot([LL(1),LL(2)],[lm(1)+ld*(j/40) lm(1)+ld*(j/40)],...
'.--','color',[0.333,0.333,0.333])
end
end
end
% Add legend
f = get(gca,'Children');
legend([h.box(1:numel(Nnets),1);f(counter+1);f(1)],...
[Nnets','{\it p} <0.05 between scans','{\it p} <0.05 between methods'],...
'Location','northeastoutside')
hold off
% % Change lines width for all graph
% lines = findobj(gcf,'Type','Line');
% for i = 1:numel(lines)
% lines(i).LineWidth = 2.0;
% end
end
% Save
savefig(fullfile(rpP,'Number of slices.fig'))
end
%% repVL
% This function is to report the Volume related results
%
% Input:
% 1. Volume: The volumes. (structure)
% 2. sVolume: The significance of volumes change. (structure)
% 3. cVolume: The change in volumes. (structure)
% 4. sCvolume: The significance of changes in volumes. (structure)
% 5. CV: The calculated CV results. (structure)
% 6. protocol: The summary of the scanning protocol parameters. (structure)
% 7. rpP: The report folder path. (string)
%
function repVL(Volume,sVolume,cVolume,sCvolume,CV,protocol,rpP)
% --Overview figures by region
% Get Netwroks names
nets = fieldnames(Volume);
% Loop over Netwroks
for ii = 1:numel(nets)
% Get regions and features names
regs = fieldnames(Volume.(nets{ii}));
% Loop over regions
for jj = 1:numel(regs)
%-- Volumes
% Loop over scans
scans = fieldnames(Volume.(nets{ii}).(regs{jj}));
for kk = 1:numel(scans)
for nn = 1:size(Volume.(nets{ii}).(regs{jj}).(scans{kk}),1)
data.volume.(regs{jj})(kk,ii,nn) =...
Volume.(nets{ii}).(regs{jj}).(scans{kk})(nn);
end
end
%-- Change in volume
data.cVolume.(regs{jj})(:,ii) =...
cVolume.(nets{ii}).(regs{jj});
end
end
%--Volumes
% Figure
figure('Name','Volumes in scans')
% Master title
sgtitle('Volumes in scans')
% Loop over regions
for jj = 1:numel(regs)
subplot(numel(regs),1,jj)
% Plot boxplots
% Group names
Nnets = replace(nets,'_','');
% Data
dd = data.volume.(regs{jj});
% Limits
lm = [min(min(min(dd)))-(1.5*10^4),max(max(max(dd)))+(1.5*10^4)];
% Plot
h = boxplot2(dd,1:numel(scans),lm,Nnets');
ylim(lm)
hold on
% Alter linestyle and color
cmap = get(0, 'defaultaxescolororder');
for ii = 1:numel(nets)
structfun(@(x) set(x(ii,:), 'color', cmap(ii,:), ...
'markeredgecolor', cmap(ii,:)), h);
end
set([h.lwhis h.uwhis], 'linestyle', '-');
set(h.out, 'marker', '.');
% Change x ticks labels
set(gca,'XTick',1:numel(nets),'XTickLabel',scans)
% Titles and labels
title(regs{jj})
ylabel('Volume')
% Add significany signs
% bwtween scans
sS = sVolume.Scans.(regs{jj});
for i = 1:numel(nets)
if sS(i)<0.05
LL = [(h.box(i).XData(3)+h.box(i).XData(1))/2,...
(h.box(i+numel(nets)).XData(3)+h.box(i+numel(nets)).XData(1))/2];
% add line between them
ld = lm(2)-lm(1);