fixes

neuroneural · Mar 27, 2024 · 773756a · 773756a
1 parent e985526
commit 773756a
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Showing 2 changed files with 20 additions and 18 deletions.
diff --git a/js/brainchop/mainMeshNetFunctions.js b/js/brainchop/mainMeshNetFunctions.js
@@ -1258,7 +1258,7 @@ rgbToHex = (rgbObj) => {
 
 
 /**
-* Get MRI after threshold noisy voxels around the brain for better cropping later
+* Get MRI mask after threshold noisy voxels around the brain for better cropping later
 * @since 3.0.0
 * @param {tf.Tensor} tensor - Tensor3d,  e.g. Tensor3d of all MRI volume data 
 * @param {number} percentage - Threshold percentage is just a number between 0 and 1 
@@ -1278,18 +1278,18 @@ applyMriThreshold = async(tensor, percentage) => {
     thresholdTensor.dispose();
 
     // Use tf.tidy for synchronous operations
-    const denoisedMriData = tf.tidy(() => {
+    return tf.tidy(() => {
       const dataForProcessing = tensor.clone();
 
       // Thresholding (assuming background has very low values compared to the head)
       const mask = dataForProcessing.greater(threshold[0]);
-      const denoisedMriData = dataForProcessing.mul(mask);
+      //-- const denoisedMriData = dataForProcessing.mul(mask);
 
       // No need to  manually dispose dataForProcessing and mask, as tf.tidy() will dispose them auto.
-      return denoisedMriData;
+      return mask;
     });
 
-    return denoisedMriData;
+    //-- return denoisedMriData;
 }
 
 
@@ -4876,13 +4876,12 @@ function convByOutputChannelAndInputSlicing(input, filter, biases, stride, pad,
               if( (autoThresholdValue > 0) && (autoThresholdValue <= 1) ) {
 
                   // Filtered MRI from noisy voxel below  autoThresholdValue
-                  slices_3d = await applyMriThreshold(slices_3d, autoThresholdValue);
+                  mask_3d = await applyMriThreshold(slices_3d, autoThresholdValue);
               } else {
                  console.log("No valid crop threshold value");
-              }
-
-              // binarize original image 
-              mask_3d = slices_3d.greater([0]).asType('bool');            
+                 // binarize original image 
+                 mask_3d = slices_3d.greater([0]).asType('bool');                  
+              }           
 
            } else {
 
@@ -6115,13 +6114,12 @@ get3dObjectBoundingVolume = async(slices_3d) => {
               if( (autoThresholdValue > 0) && (autoThresholdValue <= 1) ) {
 
                   // Filtered MRI from noisy voxel below  autoThresholdValue
-                  slices_3d = await applyMriThreshold(slices_3d, autoThresholdValue);
+                  mask_3d = await applyMriThreshold(slices_3d, autoThresholdValue);
               } else {
                  console.log("No valid crop threshold value");
-              }
-
-              // binarize original image 
-              mask_3d = slices_3d.greater([0]).asType('bool');            
+                 // binarize original image 
+                 mask_3d = slices_3d.greater([0]).asType('bool');                  
+              }            
 
            } else {
 
@@ -6169,8 +6167,12 @@ get3dObjectBoundingVolume = async(slices_3d) => {
 
           //-- Reference voxel that cropped volume started slice with it
           let refVoxel = [row_min, col_min, depth_min]; 
+          console.log("refVoxel :", refVoxel)
+
           // -- Starting form refVoxel, size of bounding volume 
-          let boundVolSizeArr = [row_max - row_min + 1, col_max - col_min + 1, depth_max - depth_min + 1];          
+          let boundVolSizeArr = [row_max - row_min + 1, col_max - col_min + 1, depth_max - depth_min + 1];  
+
+          console.log("boundVolSizeArr :", boundVolSizeArr)       
 
           coords.dispose();
 
@@ -6622,7 +6624,7 @@ checkInferenceModelList = () => {
 
                    preModel_slices_3d = preModel_slices_3d.transpose();
                    console.log("Input transposed for pre-model");
-                   
+
                 } else {
                    console.log("Transpose not enabled for pre-model");
                 }   

diff --git a/js/brainchop/mainParameters.js b/js/brainchop/mainParameters.js
@@ -115,7 +115,7 @@
                                        enableTranspose : true, // Keras and tfjs input orientation may need a tranposing step to be matched                                        
                                        enableCrop: true, // For speed-up inference, crop brain from background before feeding to inference model to lower memory use.
                                        cropPadding: 2, // Padding size add to cropped brain 
-                                       autoThreshold: 0.2, // Threshold between 0 and 1, given no preModel and tensor is normalized either min-max or by quantiles. Will remove noisy voxels around brain
+                                       autoThreshold: 0.1, // Threshold between 0 and 1, given no preModel and tensor is normalized either min-max or by quantiles. Will remove noisy voxels around brain
                                        enableQuantileNorm:  false, // Some models needs Quantile Normaliztion.
                                        filterOutWithPreMask: false, // Can be used to multiply final output with premodel output mask to crean noisy areas                                      
                                        enableSeqConv: false, // For low memory system and low configuration, enable sequential convolution instead of last layer