diff --git a/brainchop-mainthread.js b/brainchop-mainthread.js
index 14906a0..1d5529d 100644
--- a/brainchop-mainthread.js
+++ b/brainchop-mainthread.js
@@ -28,6 +28,43 @@ async function load_model(modelUrl) {
   return await tf.loadLayersModel(modelUrl)
 }
 
+// return first and last non-zero voxel in row (dim = 0), column (1) or slice (2) dimension
+async function firstLastNonZero(tensor3D, dim = 0) {
+  let mxs = []
+  if (dim === 0) {
+    mxs = await tensor3D.max(2).max(1).arraySync()
+  } else if (dim === 1) {
+    mxs = await tensor3D.max(2).max(0).arraySync()
+  } else {
+    mxs = await tensor3D.max(1).max(0).arraySync()
+  }
+  let mn = mxs.length
+  let mx = 0
+  for (let i = 0; i < mxs.length; i++) {
+    if (mxs[i] > 0) {
+      mn = i
+      break
+    }
+  }
+  for (let i = mxs.length - 1; i >= 0; i--) {
+    if (mxs[i] > 0) {
+      mx = i
+      break
+    }
+  }
+  return [mn, mx]
+}
+
+async function firstLastNonZero3D(tensor3D) {
+  const [row_min, row_max] = await firstLastNonZero(tensor3D, 0)
+  const [col_min, col_max] = await firstLastNonZero(tensor3D, 1)
+  const [depth_min, depth_max] = await firstLastNonZero(tensor3D, 2)
+  console.log('row min and max  :', row_min, row_max)
+  console.log('col min and max  :', col_min, col_max)
+  console.log('depth min and max  :', depth_min, depth_max)
+  return [row_min, row_max, col_min, col_max, depth_min, depth_max]
+}
+
 async function getAllSlicesDataAsTF3D(num_of_slices, niftiHeader, niftiImage) {
   // Get nifti dimensions
   const cols = niftiHeader.dims[1] // Slice width
@@ -709,54 +746,14 @@ async function inferenceFullVolumeSeqCovLayerPhase2(
     mask_3d = await pipeline1_out.greater([0]).asType('bool')
     // -- pipeline1_out.dispose();
   }
-
   console.log(' mask_3d shape :  ', mask_3d.shape)
-
-  const coords = await tf.whereAsync(mask_3d)
-  // -- Get each voxel coords (x, y, z)
-
+  const [row_min, row_max, col_min, col_max, depth_min, depth_max] = await firstLastNonZero3D(mask_3d)
   mask_3d.dispose()
-
-  const coordsArr = coords.arraySync()
-
-  let row_min = slice_height
-  let row_max = 0
-  let col_min = slice_width
-  let col_max = 0
-  let depth_min = num_of_slices
-  let depth_max = 0
-
-  for (let i = 0; i < coordsArr.length; i++) {
-    if (row_min > coordsArr[i][0]) {
-      row_min = coordsArr[i][0]
-    } else if (row_max < coordsArr[i][0]) {
-      row_max = coordsArr[i][0]
-    }
-
-    if (col_min > coordsArr[i][1]) {
-      col_min = coordsArr[i][1]
-    } else if (col_max < coordsArr[i][1]) {
-      col_max = coordsArr[i][1]
-    }
-
-    if (depth_min > coordsArr[i][2]) {
-      depth_min = coordsArr[i][2]
-    } else if (depth_max < coordsArr[i][2]) {
-      depth_max = coordsArr[i][2]
-    }
-  }
-
-  console.log('row min and max  :', row_min, row_max)
-  console.log('col min and max  :', col_min, col_max)
-  console.log('depth min and max  :', depth_min, depth_max)
-
   // -- Reference voxel that cropped volume started slice with it
   const refVoxel = [row_min, col_min, depth_min]
   // -- Starting form refVoxel, size of bounding volume
   const boundVolSizeArr = [row_max - row_min + 1, col_max - col_min + 1, depth_max - depth_min + 1]
 
-  coords.dispose()
-
   // -- Extract 3d object (e.g. brain)
   const cropped_slices_3d = await slices_3d.slice(
     [row_min, col_min, depth_min],
@@ -865,10 +862,6 @@ async function inferenceFullVolumeSeqCovLayerPhase2(
 
     const curTensor = []
     curTensor[0] = await cropped_slices_3d_w_pad.reshape(adjusted_input_shape)
-    // console.log("curTensor[0] :", curTensor[0].dataSync());
-
-    // let curProgBar = parseInt(document.getElementById("progressBar").style.width);
-
     const timer = window.setInterval(async function () {
       try {
         if (res.layers[i].activation.getClassName() !== 'linear') {
@@ -1071,7 +1064,6 @@ async function inferenceFullVolumeSeqCovLayerPhase2(
       callbackUI(unreliableReasons, NaN, unreliableReasons)
     }
   }
-  // });
 }
 
 async function inferenceFullVolumePhase2(
@@ -1121,42 +1113,8 @@ async function inferenceFullVolumePhase2(
     // -- pipeline1_out.dispose()
   }
   console.log(' mask_3d shape :  ', mask_3d.shape)
-  const coords = await tf.whereAsync(mask_3d)
-  // -- Get each voxel coords (x, y, z)
+  const [row_min, row_max, col_min, col_max, depth_min, depth_max] = await firstLastNonZero3D(mask_3d)
   mask_3d.dispose()
-  const coordsArr = coords.arraySync()
-
-  let row_min = slice_height
-  let row_max = 0
-  let col_min = slice_width
-  let col_max = 0
-  let depth_min = num_of_slices
-  let depth_max = 0
-
-  for (let i = 0; i < coordsArr.length; i++) {
-    if (row_min > coordsArr[i][0]) {
-      row_min = coordsArr[i][0]
-    } else if (row_max < coordsArr[i][0]) {
-      row_max = coordsArr[i][0]
-    }
-
-    if (col_min > coordsArr[i][1]) {
-      col_min = coordsArr[i][1]
-    } else if (col_max < coordsArr[i][1]) {
-      col_max = coordsArr[i][1]
-    }
-
-    if (depth_min > coordsArr[i][2]) {
-      depth_min = coordsArr[i][2]
-    } else if (depth_max < coordsArr[i][2]) {
-      depth_max = coordsArr[i][2]
-    }
-  }
-
-  console.log('row min and max  :', row_min, row_max)
-  console.log('col min and max  :', col_min, col_max)
-  console.log('depth min and max  :', depth_min, depth_max)
-
   // -- Reference voxel that cropped volume started slice with it
   const refVoxel = [row_min, col_min, depth_min]
   console.log('refVoxel :', refVoxel)
@@ -1166,8 +1124,6 @@ async function inferenceFullVolumePhase2(
 
   console.log('boundVolSizeArr :', boundVolSizeArr)
 
-  coords.dispose()
-
   // -- Extract 3d object (e.g. brain)
   const cropped_slices_3d = slices_3d.slice(
     [row_min, col_min, depth_min],
diff --git a/brainchop-webworker.js b/brainchop-webworker.js
index 8e0ede4..944c1b5 100644
--- a/brainchop-webworker.js
+++ b/brainchop-webworker.js
@@ -52,6 +52,58 @@ async function load_model(modelUrl) {
   return await tf.loadLayersModel(modelUrl)
 }
 
+// return first and last non-zero voxel in row (dim = 0), column (1) or slice (2) dimension
+async function firstLastNonZero(tensor3D, dim = 0) {
+  let mxs = []
+  if (dim === 0) {
+    mxs = await tensor3D.max(2).max(1).arraySync()
+  } else if (dim === 1) {
+    mxs = await tensor3D.max(2).max(0).arraySync()
+  } else {
+    mxs = await tensor3D.max(1).max(0).arraySync()
+  }
+  let mn = mxs.length
+  let mx = 0
+  for (let i = 0; i < mxs.length; i++) {
+    if (mxs[i] > 0) {
+      mn = i
+      break
+    }
+  }
+  for (let i = mxs.length - 1; i >= 0; i--) {
+    if (mxs[i] > 0) {
+      mx = i
+      break
+    }
+  }
+  return [mn, mx]
+}
+
+async function firstLastNonZero3D(tensor3D) {
+  const [row_min, row_max] = await firstLastNonZero(tensor3D, 0)
+  const [col_min, col_max] = await firstLastNonZero(tensor3D, 1)
+  const [depth_min, depth_max] = await firstLastNonZero(tensor3D, 2)
+  console.log('row min and max  :', row_min, row_max)
+  console.log('col min and max  :', col_min, col_max)
+  console.log('depth min and max  :', depth_min, depth_max)
+  return [row_min, row_max, col_min, col_max, depth_min, depth_max]
+}
+
+/*
+//simpler function, but x4 slower
+async function firstLastNonZero3D(tensor3D) {
+  const coords = await tf.whereAsync(tensor3D)
+  const row_min = coords.min(0).arraySync()[0]
+  const row_max = coords.max(0).arraySync()[0]
+  const col_min = coords.min(0).arraySync()[1]
+  const col_max = coords.max(0).arraySync()[1]
+  const depth_min = coords.min(0).arraySync()[2]
+  const depth_max = coords.max(0).arraySync()[2]
+  coords.dispose()
+  return [row_min, row_max, col_min, col_max, depth_min, depth_max]
+}
+*/
+
 async function getAllSlicesDataAsTF3D(num_of_slices, niftiHeader, niftiImage) {
   // Get nifti dimensions
   const cols = niftiHeader.dims[1] // Slice width
@@ -734,28 +786,13 @@ async function inferenceFullVolumeSeqCovLayerPhase2(
   }
 
   console.log(' mask_3d shape :  ', mask_3d.shape)
-  const coords = await tf.whereAsync(mask_3d)
-  // -- Get each voxel coords (x, y, z)
-
+  const [row_min, row_max, col_min, col_max, depth_min, depth_max] = await firstLastNonZero3D(mask_3d)
   mask_3d.dispose()
-  const row_min = coords.min(0).arraySync()[0]
-  const row_max = coords.max(0).arraySync()[0]
-  const col_min = coords.min(0).arraySync()[1]
-  const col_max = coords.max(0).arraySync()[1]
-  const depth_min = coords.min(0).arraySync()[2]
-  const depth_max = coords.max(0).arraySync()[2]
-
-  console.log('row min and max  :', row_min, row_max)
-  console.log('col min and max  :', col_min, col_max)
-  console.log('depth min and max  :', depth_min, depth_max)
-
   // -- Reference voxel that cropped volume started slice with it
   const refVoxel = [row_min, col_min, depth_min]
   // -- Starting form refVoxel, size of bounding volume
   const boundVolSizeArr = [row_max - row_min + 1, col_max - col_min + 1, depth_max - depth_min + 1]
 
-  coords.dispose()
-
   // -- Extract 3d object (e.g. brain)
   const cropped_slices_3d = await slices_3d.slice(
     [row_min, col_min, depth_min],
@@ -1114,42 +1151,8 @@ async function inferenceFullVolumePhase2(
     // -- pipeline1_out.dispose()
   }
   console.log(' mask_3d shape :  ', mask_3d.shape)
-  const coords = await tf.whereAsync(mask_3d)
-  // -- Get each voxel coords (x, y, z)
+  const [row_min, row_max, col_min, col_max, depth_min, depth_max] = await firstLastNonZero3D(mask_3d)
   mask_3d.dispose()
-  const coordsArr = coords.arraySync()
-
-  let row_min = slice_height
-  let row_max = 0
-  let col_min = slice_width
-  let col_max = 0
-  let depth_min = num_of_slices
-  let depth_max = 0
-
-  for (let i = 0; i < coordsArr.length; i++) {
-    if (row_min > coordsArr[i][0]) {
-      row_min = coordsArr[i][0]
-    } else if (row_max < coordsArr[i][0]) {
-      row_max = coordsArr[i][0]
-    }
-
-    if (col_min > coordsArr[i][1]) {
-      col_min = coordsArr[i][1]
-    } else if (col_max < coordsArr[i][1]) {
-      col_max = coordsArr[i][1]
-    }
-
-    if (depth_min > coordsArr[i][2]) {
-      depth_min = coordsArr[i][2]
-    } else if (depth_max < coordsArr[i][2]) {
-      depth_max = coordsArr[i][2]
-    }
-  }
-
-  console.log('row min and max  :', row_min, row_max)
-  console.log('col min and max  :', col_min, col_max)
-  console.log('depth min and max  :', depth_min, depth_max)
-
   // -- Reference voxel that cropped volume started slice with it
   const refVoxel = [row_min, col_min, depth_min]
   console.log('refVoxel :', refVoxel)
@@ -1158,9 +1161,6 @@ async function inferenceFullVolumePhase2(
   const boundVolSizeArr = [row_max - row_min + 1, col_max - col_min + 1, depth_max - depth_min + 1]
 
   console.log('boundVolSizeArr :', boundVolSizeArr)
-
-  coords.dispose()
-
   // -- Extract 3d object (e.g. brain)
   const cropped_slices_3d = slices_3d.slice(
     [row_min, col_min, depth_min],
@@ -2096,4 +2096,4 @@ self.addEventListener(
     runInferenceWW(event.data.opts, event.data.modelEntry, event.data.niftiHeader, event.data.niftiImage)
   },
   false
-)
+)
\ No newline at end of file