Deploying to demo from @ 5e0b9e8 🚀

niivue · Jun 7, 2024 · e7f04ca · e7f04ca
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diff --git a/assets/index-DgVXdzB4.js → assets/index-jh3uDHfm.js b/assets/index-DgVXdzB4.js → assets/index-jh3uDHfm.js
diff --git a/index.html b/index.html
@@ -6,7 +6,7 @@
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <link rel="stylesheet" href="./niivue.css" />
   <title>Niivue Neglect Predictions</title>
-  <script type="module" crossorigin src="./assets/index-DgVXdzB4.js"></script>
+  <script type="module" crossorigin src="./assets/index-jh3uDHfm.js"></script>
 </head>
 
 <body>

diff --git a/mask.nii.gz b/mask.nii.gz
diff --git a/models_5x10_diff.mat b/models_5x10_diff.mat
diff --git a/neglect_predict.m b/neglect_predict.m
@@ -0,0 +1,100 @@
+function neglect_predict(fnm, acuteCoC)
+%Predict chronic recovery based on lesion map and acute center of
+%cancellation score.
+%Examples
+% neglect_predict; %use GUI;
+% neglect_predict('M2095_lesion.nii.gz', 0.65)
+    if nargin < 1
+        [p_file, p_path] = uigetfile('*.nii.gz;*.nii', 'Select lesion map');
+        if p_file==0
+            return
+        end
+        fnm = fullfile(p_path, p_file);
+    end
+    if nargin < 2
+        prompt = {'Enter acute CoC score (-1..1):'};
+        dlgtitle = 'Input';
+        fieldsize = [1];
+        definput = {'0.65'};
+        answer = inputdlg(prompt,dlgtitle,fieldsize,definput);
+        if length(answer) == 0
+            return
+        end
+        acuteCoC = str2double(answer{1});
+    end
+    lesion = niftiread(fnm);
+    mpath = fileparts(mfilename("fullpath"));
+    fnmMsk = fullfile(mpath, 'mask.nii.gz');
+    if ~exist(fnmMsk,'file')
+        error('Unable to find %s', fnmMsk)
+    end
+    maskVox012 = uint8(niftiread(fnmMsk));
+    maskROI = uint8(maskVox012 > 0);
+    maskVox = uint8(maskVox012 > 1);
+    ROI_volVox = nnz(maskROI & lesion);
+    ROI_volML = ROI_volVox / 1000; %convert voxels to ML
+    fprintf("%d lesioned voxels in ROI mask: %g ml\n", ROI_volVox, ROI_volML);
+    %PCA
+    fnmPCA = fullfile(mpath, 'pca_values_5x21220.mat');
+    if ~exist(fnmPCA,'file')
+        error('Unable to find %s', fnmPCA)
+    end
+    pca_val = load(fnmPCA).pca_val;
+    if size(pca_val.mu,2) ~= nnz(maskVox)
+        error('maskVox size does not match pca_val')
+    end
+    map_org = double(lesion(:));
+    mask_log = maskVox ~=0;
+    map = map_org(mask_log);
+    PC = (map'-pca_val.mu)*pca_val.coeff;
+    if numel(PC) ~= 5
+        error('scores_new should have 5 values')
+    end
+    %normalize values to range 0..1
+    %       -> PCs: min = -51.9073; max = 110.0535
+    %       -> CoC: min = -0.024243014; max = 0.951938077
+    %       -> ROI_vol: min = 0; max = 21.625
+    for i = 1:5
+        PC(i) = norm0to1(PC(i), -51.9073, 110.0535);
+    end
+   acuteCoC = norm0to1(acuteCoC, -0.024243014, 0.951938077);
+    ROI_volML = norm0to1(ROI_volML, 0, 21.625);
+    % input_vector = [PC1, PC2, PC3, PC4, PC5, CoC, ROI_vol];
+    % Exemplary patient data (with outcome = 0.8155):
+    input_vector = [PC(1), PC(2), PC(3), PC(4), PC(5), acuteCoC, ROI_volML];
+    fnmModel = fullfile(mpath, 'models_5x10_diff.mat');
+    if ~exist(fnmPCA,'file')
+        error('Unable to find %s', fnmPCA)
+    end
+    models = load(fnmModel).models;
+    % Blank prediction-array
+    predictions_mdls = zeros(numel(models),1);
+
+    % For each single model (5-fold nested cross validation x 10 model repetitions)
+    for i = 1:numel(models)
+        % Extract information from the i-th model
+        support_vectors_i = models{i}.SVs;
+        coefficients_i = models{i}.sv_coef;
+        bias_i = -models{i}.rho;
+        % Define RBF kernel function
+        gamma_i = models{i}.Parameters(4);
+        rbf_kernel = @(x1, x2) exp(-gamma_i * sum((x1 - x2).^2));
+        % Calculate the kernel values
+        kernel_values_i = arrayfun(@(j) rbf_kernel(input_vector, support_vectors_i(j, :)), 1:size(support_vectors_i, 1));
+        % Calculate the prediction using the regression function
+        predictions_mdls(i) = sum(coefficients_i' .* kernel_values_i) + bias_i;
+        % Feature weights and bias term
+        % w = coefficients_i' * support_vectors_i; 
+        % b = bias_i;
+    end
+    % Calculate mean prediction
+    prediction_mean = mean(predictions_mdls);
+    disp(['Mean prediction: ' num2str(prediction_mean)]);
+end
+function ret = norm0to1(val, mn, mx)
+    %return normalized 0..1, linearly interpolated min..max
+    range = mx - mn;
+    ret = (val - mn)/range;
+    ret = min(max(ret,0),1);
+end
+
diff --git a/pca_values_5x21220.mat b/pca_values_5x21220.mat
diff --git a/pca_values_coeff.nii.gz b/pca_values_coeff.nii.gz
diff --git a/pca_values_mu.nii.gz b/pca_values_mu.nii.gz