[ENH] use inputs from several datasets for lesion abnormality detection

CITATION.cff

@@ -2,7 +2,7 @@ cff-version: 1.2.0
 
 title: "CPP SPM"
 
-version: 2.1.0
+version: 2.1.0dev
 
 abstract: CPP_SPM is a set pipelines and tools for Octave/MATLAB to process and analyze BIDS data sets using SPM.
 

demos/lesion_detection/lesion_get_option.m

@@ -1,32 +1,33 @@
-function opt = lesion_get_option()
-  %
-  % Returns a structure that contains the options chosen by the user to run the source processing
-  % batch workflow
-  %
-  % USAGE::
-  %
-  %  opt = lesion_get_option()
-  %
-  % :returns: - :opt: (struct)
+function [opt, opt2] = lesion_get_option()
   %
   % (C) Copyright 2021 CPP_SPM developers
 
   % The directory where the data are located
-  opt.dir.raw = 'C:\Users\michm\Data\myphdproject\MRI\CVI-DataLad\data';
-  opt.dir.raw = '/home/remi/gin/Michele/CVI-raw';
 
+  opt.dir.raw = '/home/remi/gin/Michele/CVI-raw';
   opt.dir.derivatives = fullfile(opt.dir.raw, '..', 'derivatives');
+  opt.subjects = {'HH02', 'CTL25'};
+  opt.query.run = '02';
 
   opt.query.modality = 'anat';
 
   opt.pipeline.type = 'preproc';
 
-  % opt.toolbox.ALI.unified_segmentation.step1thr_size = 0.6;
   opt.toolbox.ALI.unified_segmentation.step1fwhm = 6;
   opt.toolbox.ALI.unified_segmentation.step1niti = 3;
+  % Voxel sizes (in mm)
+  opt.toolbox.ALI.unified_segmentation.step1vox = 1;
 
   %% DO NOT TOUCH
   opt = checkOptions(opt);
   saveOptions(opt);
 
+  %%
+  opt2.subjects = {'ctrl01'};
+  opt2.dir.derivatives = '/home/remi/gin/Christine/olfaction_blind/derivatives/lesion/derivatives/';
+  opt2.dir.preproc = fullfile(opt2.dir.derivatives, 'cpp_spm-preproc');
+  opt2.query.run = '';
+  opt2.query.acq = '';
+  opt2.toolbox.ALI.unified_segmentation.step1fwhm = 6;
+  opt2 = checkOptions(opt2);
 end

demos/lesion_detection/run_lesion.m

@@ -15,18 +15,18 @@
 cpp_spm();
 
 %% Set options
-opt = lesion_get_option();
+[opt, opt2] = lesion_get_option();
 
 %% Run batches
-% reportBIDS(opt);
-
-bidsCopyInputFolder(opt);
+% bidsCopyInputFolder(opt);
+% bidsCopyInputFolder(opt2);
 
 %% Step 1: segmentation
-bidsLesionSegmentation(opt);
+% bidsLesionSegmentation(opt);
+% bidsLesionSegmentation(opt2);
 
 %% Step 2: lesion abnormalities
-bidsLesionAbnormalitiesDetection(opt);
+bidsLesionAbnormalitiesDetection(opt, opt2);
 
 % % Step 3: overlap map
 % bidsLesionOverlapMap(opt)

src/workflows/lesion/bidsLesionAbnormalitiesDetection.m

@@ -1,4 +1,4 @@
-function bidsLesionAbnormalitiesDetection(opt)
+function bidsLesionAbnormalitiesDetection(opt, extraOptions)
   %
   % Use the ALI toolbox to detect lesion abnormalities in anatomical image
   % after segmentation of the image.
@@ -9,41 +9,87 @@ function bidsLesionAbnormalitiesDetection(opt)
   %
   %  bidsLesionAbnormalitiesDetection(opt)
   %
-  % :type opt:  structure
   % :param opt: Options chosen for the analysis.
   %             See also: checkOptions
   %             ``checkOptions()`` and ``loadAndCheckOptions()``.
   % :type opt: structure
   %
+  % :param extraOptions: Options chosen for analysis of another dataset
+  %                      in case they need to be merged.
+  %                      See also: checkOptions
+  %                      ``checkOptions()`` and ``loadAndCheckOptions()``.
+  % :type extraOptions: structure
+  %
   % Lesion abnormalities detection will be performed using the information provided
   % from the lesion segmentation output in BIDS format.
   %
   %
   % (C) Copyright 2021 CPP_SPM developers
 
+  % TODO: do not use extraOptions but instead either opt(1) and opt(2) or
+
+  if nargin < 2
+    extraOptions = [];
+  end
+
+  % create a structure to collect image names
+  labels = {'GM', 'WM'};
+  for i = 1:numel(labels)
+    images(i, 1) = struct('controls', [], 'patients', []);
+  end
+
   opt.dir.input = opt.dir.preproc;
 
   if checkToolbox('ALI', 'verbose', opt.verbosity > 0)
     opt = setFields(opt, ALI_my_defaults());
   end
 
-  [BIDS, opt] = setUpWorkflow(opt, 'abnormalities detection');
+  images = collectImagesFromDataset(opt, images, labels);
 
-  labels = {'GM', 'WM'};
+  if ~isempty(extraOptions)
+    if checkToolbox('ALI', 'verbose', extraOptions.verbosity > 0)
+      extraOptions = setFields(extraOptions, ALI_my_defaults());
+    end
+    extraOptions.dir.input = extraOptions.dir.preproc;
+    images = collectImagesFromDataset(extraOptions, images, labels);
+  end
 
-  % create a structure to collect image names
-  for i = 1:numel(labels)
-    images(i, 1) = struct('controls', [], 'patients', []);
+  %%
+  controlsImages = cat(1, images.controls);
+  patientsImages = cat(1, images.patients);
+
+  if isempty(controlsImages) || ...
+    isempty(patientsImages) || ...
+    any(cellfun('isempty', controlsImages)) || ...
+      any(cellfun('isempty', patientsImages))
+    msg = sprintf('Must have segmentation output from patients AND control');
+    id = 'missingImages';
+    errorHandling(mfilename(), id, msg, false);
   end
 
+  matlabbatch = {};
+  matlabbatch = setBatchLesionAbnormalitiesDetection(matlabbatch, opt, images);
+
+  saveAndRunWorkflow(matlabbatch, 'LesionAbnormalitiesDetection', opt);
+
+end
+
+function images = collectImagesFromDataset(opt, images, labels)
+
+  [BIDS, opt] = setUpWorkflow(opt, 'abnormalities detection');
+
   for iSub = 1:numel(opt.subjects)
 
     subLabel = opt.subjects{iSub};
 
     printProcessingSubject(iSub, subLabel, opt);
 
     idx = strcmp(BIDS.participants.content.participant_id, ['sub-' subLabel]);
-    participantsGroup = BIDS.participants.content.group(idx);
+    if isfield(BIDS.participants.content, 'group')
+      participantsGroup = BIDS.participants.content.group(idx);
+    elseif isfield(BIDS.participants.content, 'Group')
+      participantsGroup = BIDS.participants.content.Group(idx);
+    end
 
     anatImage = getAnatFilename(BIDS, opt, subLabel);
     anatImage = bids.File(anatImage);
@@ -79,9 +125,4 @@ function bidsLesionAbnormalitiesDetection(opt)
 
   end
 
-  matlabbatch = {};
-  matlabbatch = setBatchLesionAbnormalitiesDetection(matlabbatch, opt, images);
-
-  saveAndRunWorkflow(matlabbatch, 'LesionAbnormalitiesDetection', opt);
-
 end

version.txt

@@ -1 +1 @@
-v2.1.0
+v2.1.0dev