resize ALI mask in function of chosen options

Author: Remi-Gau

src/batches/lesion/setBatchLesionAbnormalitiesDetection.m

@@ -21,6 +21,8 @@
 
   outliers_detection = opt.toolbox.ALI.outliers_detection;
 
+  outliers_detection.step3mask{1} = resizeAliMask(opt);
+
   for i = 1:size(images, 1)
 
     % 1. Define smoothed segmented tissue images of patients

src/defaults/ALI_my_defaults.m

@@ -54,13 +54,6 @@
   % Specify lambda parameter
   defaults.toolbox.ALI.outliers_detection.step3tissue.step3Lambda = -4;
 
-  % specify lesion mask
-  defaults.toolbox.ALI.outliers_detection.step3mask{1} = fullfile(spmDir, ...
-                                                                  'toolbox', ...
-                                                                  'ALI', ...
-                                                                  'Mask_image', ...
-                                                                  'mask_controls_vox2mm.nii');
-
   % threshold for the mask
   defaults.toolbox.ALI.outliers_detection.step3mask_thr = 0;
   % binary lesion: threshold U

src/infra/resizeAliMask.m

@@ -0,0 +1,28 @@
+function aliMask = resizeAliMask(opt)
+  %
+  % USAGE::
+  %
+  %  aliMask = resizeAliMask(opt)
+  %
+  %
+  % (C) Copyright 2022 CPP_SPM developers
+  aliMask = fullfile(spm('dir'), 'toolbox', 'ALI', 'Mask_image', 'mask_controls_vox2mm.nii');
+
+  if opt.toolbox.ALI.unified_segmentation.step1vox ~= 2
+
+    voxdim = repmat(opt.toolbox.ALI.unified_segmentation.step1vox, [3, 1]);
+    bb = nan(2, 3);
+    ismask = true;
+    resize_img(aliMask, voxdim, bb, ismask);
+
+    aliMask = fullfile(spm('dir'), ...
+                       'toolbox', ...
+                       'ALI', ...
+                       'Mask_image', ...
+                       ['mask_controls_vox' num2str(voxdim(1)) 'mm.nii']);
+
+    movefile(fullfile(spm('dir'), 'toolbox', 'ALI', 'Mask_image', 'rmask_controls_vox2mm.nii'), ...
+             aliMask);
+  end
+
+end

src/workflows/lesion/bidsLesionAbnormalitiesDetection.m

@@ -50,7 +50,9 @@ function bidsLesionAbnormalitiesDetection(opt)
     filter = anatImage.entities;
     filter.modality = 'anat';
     filter.suffix = 'probseg';
-    filter.desc = 'smth8';
+
+    fwhm = opt.toolbox.ALI.unified_segmentation.step1fwhm;
+    filter.desc = ['smth' num2str(fwhm)];
 
     for i = 1:numel(labels)
 

tests/tests_batches/lesion/test_resizeAliMask.m

@@ -0,0 +1,36 @@
+function test_suite = test_resizeAliMask %#ok<*STOUT>
+  % (C) Copyright 2021 CPP_SPM developers
+
+  try % assignment of 'localfunctions' is necessary in Matlab >= 2016
+    test_functions = localfunctions(); %#ok<*NASGU>
+  catch % no problem; early Matlab versions can use initTestSuite fine
+  end
+  initTestSuite;
+end
+
+function test_resizeAliMask_basic()
+
+  subLabel = '01';
+  opt = setOptions('MoAE', subLabel, 'useRaw', true);
+
+  if isGithubCi
+    return
+  else
+    opt = setFields(opt, ALI_my_defaults());
+  end
+
+  opt.toolbox.ALI.unified_segmentation.step1vox = 1.2;
+  aliMask = resizeAliMask(opt);
+
+  expected =     fullfile(spm('dir'), ...
+                          'toolbox', ...
+                          'ALI', ...
+                          'Mask_image', ...
+                          'mask_controls_vox1.2mm.nii');
+
+  assertEqual(aliMask, expected);
+  assertEqual(exist(expected, 'file'), 2);
+
+  delete(aliMask);
+
+end

tests/tests_batches/preproc/test_setBatchLesionSegmentation.m renamed to tests/tests_batches/lesion/test_setBatchLesionSegmentation.m

@@ -1,6 +1,6 @@
-% (C) Copyright 2021 CPP_SPM developers
-
 function test_suite = test_setBatchLesionSegmentation %#ok<*STOUT>
+  % (C) Copyright 2021 CPP_SPM developers
+
   try % assignment of 'localfunctions' is necessary in Matlab >= 2016
     test_functions = localfunctions(); %#ok<*NASGU>
   catch % no problem; early Matlab versions can use initTestSuite fine
@@ -14,29 +14,34 @@ function test_setBatchLesionSegmentation_basic()
 
   opt = setOptions('MoAE', subLabel, 'useRaw', true);
 
-  if not(isfield(opt.toolbox, 'ALI'))
+  if isGithubCi
     return
+  else
+    opt = setFields(opt, ALI_my_defaults());
   end
 
   [BIDS, opt] = getData(opt, opt.dir.input);
 
   matlabbatch = {};
   matlabbatch = setBatchLesionSegmentation(matlabbatch, BIDS, opt, subLabel);
 
-  unified_segmentation.step1data{1} = fullfile(getMoaeDir(), 'inputs', 'raw', ...
+  unified_segmentation.step1data{1} = fullfile(getMoaeDir(), ...
+                                               'inputs', ...
+                                               'raw', ...
                                                ['sub-' subLabel], ...
                                                'anat', ...
                                                'sub-01_T1w.nii');
   unified_segmentation.step1prior = {fullfile(spm('dir'), ...
-                                              'toolbox', 'ALI', ...
+                                              'toolbox', ...
+                                              'ALI', ...
                                               'Priors_extraClass', ...
                                               'wc4prior0.nii')};
   unified_segmentation.step1niti = 2;
   unified_segmentation.step1thr_prob = 0.3330;
   unified_segmentation.step1thr_size = 0.8000;
   unified_segmentation.step1coregister = 1;
   unified_segmentation.step1mask = {''};
-  unified_segmentation.step1vox = 1;
+  unified_segmentation.step1vox = 2;
   unified_segmentation.step1fwhm = [8 8 8];
 
   assertEqual(matlabbatch{1}.spm.tools.ali.unified_segmentation, ...