--- a
+++ b/brainchop-webworker.js
@@ -0,0 +1,1356 @@
+import * as tf from '@tensorflow/tfjs'
+import { inferenceModelsList } from './brainchop-parameters.js'
+import {
+  addZeroPaddingTo3dTensor,
+  applyMriThreshold,
+  binarizeVolumeDataTensor,
+  convByOutputChannelAndInputSlicing,
+  draw3dObjBoundingVolume,
+  firstLastNonZero3D,
+  generateBrainMask,
+  generateOutputSlicesV2,
+  getAllSlicesDataAsTF3D,
+  getModelNumLayers,
+  getModelNumParameters,
+  isModelChnlLast,
+  load_model,
+  minMaxNormalizeVolumeData,
+  quantileNormalizeVolumeData,
+  removeZeroPaddingFrom3dTensor,
+  resizeWithZeroPadding,
+  SequentialConvLayer
+} from './tensor-utils.js'
+
+function callbackUI(message = '', progressFrac = -1, modalMessage = '', statData = []) {
+  let statStr = []
+  if (Object.keys(statData).length > 0) {
+    function arrayToStr() {
+      const list = {}
+      for (const key in statData) {
+        list[key] = statData[key]
+      }
+      return JSON.stringify(list)
+    }
+    statStr = arrayToStr(statData)
+  }
+  self.postMessage({
+    cmd: 'ui',
+    message,
+    progressFrac,
+    modalMessage,
+    statData: statStr
+  })
+}
+
+function callbackImg(img, opts, modelEntry) {
+  self.postMessage({ cmd: 'img', img, opts, modelEntry })
+}
+
+async function inferenceFullVolumeSeqCovLayerPhase2(
+  opts,
+  modelEntry,
+  model,
+  slices_3d,
+  num_of_slices,
+  slice_height,
+  slice_width,
+  pipeline1_out,
+  statData,
+  niftiImage
+) {
+  // --Phase-2, After remove the skull try to allocate brain volume and make inferece
+
+  console.log(' ---- Start FullVolume Inference with Sequential Conv Layer for phase-II ---- ')
+  const quantileNorm = modelEntry.enableQuantileNorm
+  if (quantileNorm) {
+    // Quantile normalize function needs specific models to be used
+    console.log('preModel Quantile normalization enabled')
+    slices_3d = await quantileNormalizeVolumeData(slices_3d)
+  } else {
+    // Min Max Nomalize MRI data to be from 0 to 1
+    console.log('preModel Min Max normalization enabled')
+    slices_3d = await minMaxNormalizeVolumeData(slices_3d)
+  }
+
+  let mask_3d
+
+  if (pipeline1_out == null) {
+    // preModel is null
+
+    // Check if thresholding the MRI to remove noisy voxels for better cropping is needed.
+    const autoThresholdValue = modelEntry.autoThreshold
+
+    if (autoThresholdValue > 0 && autoThresholdValue <= 1) {
+      // Filtered MRI from noisy voxel below  autoThresholdValue
+      mask_3d = await applyMriThreshold(slices_3d, autoThresholdValue)
+    } else {
+      console.log('No valid crop threshold value')
+      // binarize original image
+      mask_3d = await slices_3d.greater([0]).asType('bool')
+    }
+  } else {
+    mask_3d = await pipeline1_out.greater([0]).asType('bool')
+    // -- pipeline1_out.dispose()
+  }
+
+  console.log(' mask_3d shape :  ', mask_3d.shape)
+  const [row_min, row_max, col_min, col_max, depth_min, depth_max] = await firstLastNonZero3D(mask_3d)
+  mask_3d.dispose()
+  // -- 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]
+
+  // -- Extract 3d object (e.g. brain)
+  const cropped_slices_3d = await slices_3d.slice(
+    [row_min, col_min, depth_min],
+    [row_max - row_min + 1, col_max - col_min + 1, depth_max - depth_min + 1]
+  )
+  slices_3d.dispose()
+
+  // -- Padding size add to cropped brain
+  const pad = modelEntry.cropPadding
+
+  // Create margin around the bounding volume
+  let cropped_slices_3d_w_pad = await addZeroPaddingTo3dTensor(cropped_slices_3d, [pad, pad], [pad, pad], [pad, pad])
+  console.log(' cropped slices_3d with padding shape:  ', cropped_slices_3d_w_pad.shape)
+
+  cropped_slices_3d.dispose()
+
+  if (opts.drawBoundingVolume) {
+    let testVol = await removeZeroPaddingFrom3dTensor(cropped_slices_3d_w_pad, pad, pad, pad)
+    console.log(' outLabelVolume without padding shape :  ', testVol.shape)
+
+    testVol = await resizeWithZeroPadding(testVol, num_of_slices, slice_height, slice_width, refVoxel, boundVolSizeArr)
+    console.log(' outLabelVolume final shape after resizing :  ', testVol.shape)
+    draw3dObjBoundingVolume(tf.unstack(testVol), opts, modelEntry, callbackImg)
+    testVol.dispose()
+
+    return 0
+  }
+
+  statData.Brainchop_Ver = 'FullVolume'
+  const res = await model
+  try {
+    let startTime = performance.now()
+    const inferenceStartTime = performance.now()
+    // maxLabelPredicted in whole volume of the brain
+    let maxLabelPredicted = 0
+    const transpose = modelEntry.enableTranspose
+
+    if (transpose) {
+      cropped_slices_3d_w_pad = await cropped_slices_3d_w_pad.transpose()
+      console.log('Input transposed for pre-model')
+    } else {
+      console.log('Transpose not enabled for pre-model')
+    }
+
+    let i = 1
+    const layersLength = res.layers.length
+    console.log('res.layers.length ', layersLength)
+
+    const isChannelLast = isModelChnlLast(res)
+    const batchSize = opts.batchSize
+    const numOfChan = opts.numOfChan
+    let adjusted_input_shape
+    // -- Adjust model input shape
+    if (isChannelLast) {
+      res.layers[0].batchInputShape[1] = cropped_slices_3d_w_pad.shape[0]
+      res.layers[0].batchInputShape[2] = cropped_slices_3d_w_pad.shape[1]
+      res.layers[0].batchInputShape[3] = cropped_slices_3d_w_pad.shape[2]
+
+      adjusted_input_shape = [
+        batchSize,
+        res.layers[0].batchInputShape[1],
+        res.layers[0].batchInputShape[2],
+        res.layers[0].batchInputShape[3],
+        numOfChan
+      ]
+    } else {
+      res.layers[0].batchInputShape[2] = cropped_slices_3d_w_pad.shape[0]
+      res.layers[0].batchInputShape[3] = cropped_slices_3d_w_pad.shape[1]
+      res.layers[0].batchInputShape[4] = cropped_slices_3d_w_pad.shape[2]
+
+      adjusted_input_shape = [
+        batchSize,
+        numOfChan,
+        res.layers[0].batchInputShape[2],
+        res.layers[0].batchInputShape[3],
+        res.layers[0].batchInputShape[4]
+      ]
+    }
+
+    console.log(' Model batch input shape : ', res.layers[0].batchInputShape)
+    // -- batchInputShape {Array} input_shape - e.g. [?, D, H, W, Ch] or [?, Ch, D, H, W]
+
+    statData.Input_Shape = JSON.stringify(res.layers[0].batchInputShape)
+    statData.Output_Shape = JSON.stringify(res.output.shape)
+    statData.Channel_Last = await isChannelLast
+    statData.Model_Param = await getModelNumParameters(res)
+    statData.Model_Layers = await getModelNumLayers(res)
+    statData.Model = modelEntry.modelName
+    statData.Seq_Conv = modelEntry.enableSeqConv
+    // statData.Extra_Info = null
+
+    // Determine the number of output channels in the last layer of the model
+    //  e.g. 3, 50, 104
+    const outputLayer = res.layers[res.layers.length - 1]
+    console.log('Output Layer : ', outputLayer)
+
+    const expected_Num_labels = isChannelLast
+      ? outputLayer.outputShape[outputLayer.outputShape.length - 1]
+      : outputLayer.outputShape[1]
+    console.log('Num of output channels x: ', expected_Num_labels)
+
+    const curTensor = []
+    curTensor[0] = await cropped_slices_3d_w_pad.reshape(adjusted_input_shape)
+    while (true) {
+      try {
+        if (res.layers[i].activation.getClassName() !== 'linear') {
+          curTensor[i] = await res.layers[i].apply(curTensor[i - 1])
+        } else {
+          curTensor[i] = await convByOutputChannelAndInputSlicing(
+            curTensor[i - 1],
+            res.layers[i].getWeights()[0],
+            res.layers[i].getWeights()[1],
+            res.layers[i].strides,
+            res.layers[i].padding,
+            res.layers[i].dilationRate,
+            3
+          ) // important for memory use
+        }
+
+        tf.dispose(curTensor[i - 1])
+      } catch (err) {
+        const errTxt = 'Your graphics card (e.g. Intel) may not be compatible with WebGL. ' + err.message
+        callbackUI(errTxt, -1, errTxt)
+
+        tf.engine().endScope()
+        tf.engine().disposeVariables()
+
+        statData.Inference_t = Infinity
+        statData.Postprocess_t = Infinity
+        statData.Status = 'Fail'
+        statData.Error_Type = err.message
+        statData.Extra_Err_Info = 'Failed while model layer ' + i + ' apply'
+
+        callbackUI('', -1, '', statData)
+
+        return 0
+      }
+
+      console.log('layer output Tensor shape : ', curTensor[i].shape)
+      console.log('layer count params ', res.layers[i].countParams())
+
+      res.layers[i].dispose()
+      curTensor[i - 1].dispose()
+
+      callbackUI('Layer ' + i.toString(), (i + 1) / layersLength)
+      if (tf.memory().unreliable) {
+        const unreliableReasons = 'unreliable reasons :' + tf.memory().reasons
+        callbackUI(unreliableReasons, NaN, unreliableReasons)
+      }
+      if (i === layersLength - 2) {
+        // Stop before the last layer or classification layer.
+
+        // // Create an instance of SequentialConvLayer
+        // The second parameter is important for memory,
+        // the larger it is, the more memory it uses
+        // it was 8, but I set it to 3, got a different error
+        // let seqConvLayer = new SequentialConvLayer(res, 10, isChannelLast)
+        const seqConvLayer = await new SequentialConvLayer(res, 10, isChannelLast, callbackUI)
+
+        // Apply the last output tensor to the seq. instance
+        let outputTensor = null
+        const profileInfo = await tf.profile(async () => {
+          // Your tensor operations here
+          outputTensor = await seqConvLayer.apply(curTensor[i])
+        })
+        console.log('profileInfo : ', profileInfo)
+
+        // -- document.getElementById("progressBarChild").style.width = 0 + "%";
+
+        // Dispose the previous layer input tensor
+        tf.dispose(curTensor[i])
+        // delete the used class
+        // ? delete seqConvLayer
+
+        // You can now use 'outputTensor' as needed
+        console.log(' Output tensor', outputTensor)
+        console.log(' Output tensor shape : ', outputTensor.shape)
+        // Array(3) [ 256, 256, 256 ]
+
+        if (outputTensor.shape.length !== 3) {
+          const msg = 'Output tensor shape should be 3 dims but it is ' + outputTensor.shape.length
+          callbackUI(msg, -1, msg)
+        }
+
+        console.log(' find array max ')
+        const curBatchMaxLabel = await outputTensor.max().dataSync()[0]
+        const Inference_t = ((performance.now() - startTime) / 1000).toFixed(4)
+        
+        if (maxLabelPredicted < curBatchMaxLabel) {
+          maxLabelPredicted = curBatchMaxLabel
+        }
+
+        const numSegClasses = maxLabelPredicted + 1
+        console.log('Predicted num of segmentation classes', numSegClasses)
+        statData.Actual_Labels = numSegClasses
+        statData.Expect_Labels = expected_Num_labels
+        statData.NumLabels_Match = numSegClasses === expected_Num_labels
+        if (numSegClasses !== expected_Num_labels) {
+          const msg = 'expected ' + expected_Num_labels + ' labels, but the predicted are ' + numSegClasses
+          callbackUI(msg, -1, msg)
+        }
+
+        // -- Transpose back to original unpadded size
+        let outLabelVolume = outputTensor.reshape([
+          cropped_slices_3d_w_pad.shape[0],
+          cropped_slices_3d_w_pad.shape[1],
+          cropped_slices_3d_w_pad.shape[2]
+        ])
+        tf.dispose(outputTensor)
+
+        // Transpose MRI data to be match pytorch/keras input output
+        if (transpose) {
+          console.log('outLabelVolume transposed')
+          outLabelVolume = outLabelVolume.transpose()
+        }
+
+        outLabelVolume = await removeZeroPaddingFrom3dTensor(outLabelVolume, pad, pad, pad)
+        console.log(' outLabelVolume without padding shape :  ', outLabelVolume.shape)
+        outLabelVolume = await resizeWithZeroPadding(
+          outLabelVolume,
+          num_of_slices,
+          slice_height,
+          slice_width,
+          refVoxel,
+          boundVolSizeArr
+        )
+        console.log(' outLabelVolume final shape after resizing :  ', outLabelVolume.shape)
+
+        // let filterOutWithPreMask =  inferenceModelsList[$$("selectModel").getValue() - 1]["filterOutWithPreMask"]
+        const filterOutWithPreMask = modelEntry.filterOutWithPreMask
+        // To clean the skull area wrongly segmented inphase-2.
+        if (pipeline1_out != null && opts.isBrainCropMaskBased && filterOutWithPreMask) {
+          const bin = await binarizeVolumeDataTensor(pipeline1_out)
+          outLabelVolume = await outLabelVolume.mul(bin)
+        }
+
+        startTime = performance.now()
+        // Generate output volume or slices
+        console.log('Generating correct output')
+        let outimg
+        try {
+          const img = await new Uint32Array(outLabelVolume.dataSync())
+          const Vshape = outLabelVolume.shape
+          const Vtype = outLabelVolume.dtype
+          outimg = await generateOutputSlicesV2(
+            img,
+            Vshape,
+            Vtype,
+            num_of_slices,
+            numSegClasses,
+            slice_height,
+            slice_width,
+            modelEntry,
+            opts,
+            niftiImage
+          )
+          console.log(' Phase-2 num of tensors after generateOutputSlicesV2: ', tf.memory().numTensors)
+
+          tf.dispose(outLabelVolume)
+          tf.engine().endScope()
+          tf.engine().disposeVariables()
+        } catch (error) {
+          // -- Timing data to collect
+          tf.engine().endScope()
+          tf.engine().disposeVariables()
+          console.log('Error while generating output: ', error)
+          const msg = 'Failed while generating output due to limited browser memory available'
+          callbackUI(msg, -1, msg)
+
+          statData.Inference_t = Inference_t
+          statData.Postprocess_t = Infinity
+          statData.Status = 'Fail'
+          statData.Error_Type = error.message
+          statData.Extra_Err_Info = 'Failed while generating output'
+
+          callbackUI('', -1, '', statData)
+
+          return 0
+        }
+        const Postprocess_t = ((performance.now() - startTime) / 1000).toFixed(4)
+
+        console.log(
+          'Processing the whole brain volume in tfjs for multi-class output mask took : ',
+          ((performance.now() - inferenceStartTime) / 1000).toFixed(4) + '  Seconds'
+        )
+
+        // -- Timing data to collect
+        statData.Inference_t = Inference_t
+        statData.Postprocess_t = Postprocess_t
+        statData.Status = 'OK'
+
+        callbackUI('', -1, '', statData)
+        callbackUI('Segmentation finished', 0)
+        callbackImg(outimg, opts, modelEntry)
+        return 0
+      } else {
+        i++
+      }
+    }
+  } catch (err) {
+    callbackUI(err.message, -1, err.message)
+    console.log(
+      'If webgl context is lost, try to restore webgl context by visit the link ' +
+        '<a href="https://support.biodigital.com/hc/en-us/articles/218322977-How-to-turn-on-WebGL-in-my-browser">here</a>'
+    )
+    if (tf.memory().unreliable) {
+      const unreliableReasons = 'unreliable reasons :' + tf.memory().reasons
+      callbackUI(unreliableReasons, NaN, unreliableReasons)
+    }
+  }
+}
+
+async function inferenceFullVolumePhase2(
+  model,
+  slices_3d,
+  num_of_slices,
+  slice_height,
+  slice_width,
+  pipeline1_out,
+  modelEntry,
+  statData,
+  opts,
+  niftiImage
+) {
+  let outimg = []
+  // --Phase-2, After remove the skull try to allocate brain volume and make inferece
+  console.log(' ---- Start FullVolume inference phase-II ---- ')
+  const quantileNorm = modelEntry.enableQuantileNorm
+  if (quantileNorm) {
+    // Quantile normalize function needs specific models to be used
+    console.log('preModel Quantile normalization enabled')
+    slices_3d = await quantileNormalizeVolumeData(slices_3d)
+  } else {
+    // Min Max Nomalize MRI data to be from 0 to 1
+    console.log('preModel Min Max normalization enabled')
+    slices_3d = await minMaxNormalizeVolumeData(slices_3d)
+  }
+  let mask_3d
+  if (pipeline1_out == null) {
+    // preModel is null
+
+    // Check if thresholding the MRI to remove noisy voxels for better cropping is needed.
+    const autoThresholdValue = modelEntry.autoThreshold
+
+    if (autoThresholdValue > 0 && autoThresholdValue <= 1) {
+      // Filtered MRI from noisy voxel below  autoThresholdValue
+      mask_3d = await applyMriThreshold(slices_3d, autoThresholdValue)
+    } else {
+      console.log('No valid crop threshold value')
+      // binarize original image
+      mask_3d = await slices_3d.greater([0]).asType('bool')
+    }
+  } else {
+    mask_3d = await pipeline1_out.greater([0]).asType('bool')
+    // -- pipeline1_out.dispose()
+  }
+  console.log(' mask_3d shape :  ', mask_3d.shape)
+  const [row_min, row_max, col_min, col_max, depth_min, depth_max] = await firstLastNonZero3D(mask_3d)
+  mask_3d.dispose()
+  // -- Reference voxel that cropped volume started slice with it
+  const refVoxel = [row_min, col_min, depth_min]
+  console.log('refVoxel :', refVoxel)
+
+  // -- Starting form refVoxel, size of bounding volume
+  const boundVolSizeArr = [row_max - row_min + 1, col_max - col_min + 1, depth_max - depth_min + 1]
+
+  console.log('boundVolSizeArr :', boundVolSizeArr)
+  // -- Extract 3d object (e.g. brain)
+  const cropped_slices_3d = slices_3d.slice(
+    [row_min, col_min, depth_min],
+    [row_max - row_min + 1, col_max - col_min + 1, depth_max - depth_min + 1]
+  )
+
+  slices_3d.dispose()
+
+  // -- Padding size add to cropped brain
+  const pad = modelEntry.cropPadding
+
+  // Create margin around the bounding volume
+  let cropped_slices_3d_w_pad = await addZeroPaddingTo3dTensor(cropped_slices_3d, [pad, pad], [pad, pad], [pad, pad])
+  console.log(' cropped slices_3d with padding shape:  ', cropped_slices_3d_w_pad.shape)
+
+  cropped_slices_3d.dispose()
+
+  // -- Test dim after padding ..
+  // for (let i = 0; i < cropped_slices_3d_w_pad.rank; i++) {
+  //     if(cropped_slices_3d_w_pad.shape[i] > 256) {
+  //          console.log(" cropped_slices_3d_w_pad > 256 ")
+  //     }
+
+  // }
+
+  if (opts.drawBoundingVolume) {
+    let testVol = await removeZeroPaddingFrom3dTensor(cropped_slices_3d_w_pad, pad, pad, pad)
+    console.log(' outLabelVolume without padding shape :  ', testVol.shape)
+
+    testVol = await resizeWithZeroPadding(testVol, num_of_slices, slice_height, slice_width, refVoxel, boundVolSizeArr)
+    console.log(' outLabelVolume final shape after resizing :  ', testVol.shape)
+    draw3dObjBoundingVolume(tf.unstack(testVol), opts, modelEntry, callbackImg)
+    testVol.dispose()
+
+    return 0
+  }
+
+  statData.Brainchop_Ver = 'FullVolume'
+  let startTime = performance.now()
+  let adjusted_input_shape = []
+  const res = await model
+  try {
+    startTime = performance.now()
+    const inferenceStartTime = performance.now()
+    // maxLabelPredicted in whole volume of the brain
+    let maxLabelPredicted = 0
+    const transpose = modelEntry.enableTranspose
+
+    if (transpose) {
+      cropped_slices_3d_w_pad = cropped_slices_3d_w_pad.transpose()
+      console.log('Input transposed for pre-model')
+    } else {
+      console.log('Transpose not enabled for pre-model')
+    }
+
+    let i = 1
+    const layersLength = res.layers.length
+    console.log('res.layers.length ', layersLength)
+
+    const isChannelLast = isModelChnlLast(res)
+    const batchSize = opts.batchSize
+    const numOfChan = opts.numOfChan
+
+    // -- Adjust model input shape
+    if (isChannelLast) {
+      res.layers[0].batchInputShape[1] = cropped_slices_3d_w_pad.shape[0]
+      res.layers[0].batchInputShape[2] = cropped_slices_3d_w_pad.shape[1]
+      res.layers[0].batchInputShape[3] = cropped_slices_3d_w_pad.shape[2]
+
+      adjusted_input_shape = [
+        batchSize,
+        res.layers[0].batchInputShape[1],
+        res.layers[0].batchInputShape[2],
+        res.layers[0].batchInputShape[3],
+        numOfChan
+      ]
+    } else {
+      res.layers[0].batchInputShape[2] = cropped_slices_3d_w_pad.shape[0]
+      res.layers[0].batchInputShape[3] = cropped_slices_3d_w_pad.shape[1]
+      res.layers[0].batchInputShape[4] = cropped_slices_3d_w_pad.shape[2]
+
+      adjusted_input_shape = [
+        batchSize,
+        numOfChan,
+        res.layers[0].batchInputShape[2],
+        res.layers[0].batchInputShape[3],
+        res.layers[0].batchInputShape[4]
+      ]
+    }
+
+    console.log(' Model batch input shape : ', res.layers[0].batchInputShape)
+    // -- batchInputShape {Array} input_shape - e.g. [?, D, H, W, Ch] or [?, Ch, D, H, W]
+
+    statData.Input_Shape = JSON.stringify(res.layers[0].batchInputShape)
+    statData.Output_Shape = JSON.stringify(res.output.shape)
+    statData.Channel_Last = await isChannelLast
+    statData.Model_Param = await getModelNumParameters(res)
+    statData.Model_Layers = await getModelNumLayers(res)
+    statData.Model = modelEntry.modelName
+    // statData.Extra_Info = null
+
+    const curTensor = []
+    curTensor[0] = cropped_slices_3d_w_pad.reshape(adjusted_input_shape)
+    // console.log("curTensor[0] :", curTensor[0].dataSync())
+
+    while (true) {
+      try {
+        // -- curTensor[i] = res.layers[i].apply( curTensor[i-1])
+        curTensor[i] = res.layers[i].apply(curTensor[i - 1])
+      } catch (err) {
+        callbackUI(err.message, -1, err.message)
+        tf.engine().endScope()
+        tf.engine().disposeVariables()
+
+        statData.Inference_t = Infinity
+        statData.Postprocess_t = Infinity
+        statData.Status = 'Fail'
+        statData.Error_Type = err.message
+        statData.Extra_Err_Info = 'Failed while model layer ' + i + ' apply'
+
+        callbackUI('', -1, '', statData)
+
+        return 0
+      }
+      callbackUI('Layer ' + i.toString(), (i + 1) / layersLength)
+      console.log('layer output Tensor shape : ', curTensor[i].shape)
+      console.log('layer count params ', res.layers[i].countParams())
+      res.layers[i].dispose()
+      curTensor[i - 1].dispose()
+      if (tf.memory().unreliable) {
+        const unreliableReasons = 'unreliable reasons :' + tf.memory().reasons
+        callbackUI(unreliableReasons, NaN, unreliableReasons)
+      }
+
+      if (i === layersLength - 1) {
+        // prediction = res.layers[res.layers.length-1].apply(curTensor[i])
+        // curTensor[i].print()
+        // outputDataBeforArgmx = Array.from(curTensor[i].dataSync())
+
+        const axis = isChannelLast ? -1 : 1
+        console.log(' find argmax ')
+        console.log('last Tensor shape : ', curTensor[i].shape)
+        // -- curTensor[i].shape  e.g. [ 1, 256, 256, 256, 3 ]
+        const expected_Num_labels = isChannelLast ? curTensor[i].shape[4] : curTensor[i].shape[1]
+        let prediction_argmax
+
+        // Try for argMax with model output tensor.
+
+        try {
+          const argMaxTime = performance.now()
+          console.log(' Try tf.argMax for fullVolume ..')
+          prediction_argmax = tf.argMax(curTensor[i], axis)
+          console.log('tf.argMax for fullVolume takes : ', ((performance.now() - argMaxTime) / 1000).toFixed(4))
+        } catch (err1) {
+          // if channel last
+          if (axis === -1) {
+            try {
+              const argMaxLargeTime = performance.now()
+              console.log(' tf.argMax failed .. try argMaxLarge ..')
+              callbackUI('', -1, 'tensor2LightBuffer() is not dead code?')
+              callbackUI('', -1, 'argMaxLarge() is not dead code?')
+              console.log(
+                'argMaxLarge for fullVolume takes : ',
+                ((performance.now() - argMaxLargeTime) / 1000).toFixed(4)
+              )
+            } catch (err2) {
+              const errTxt = "argMax buffer couldn't be created due to limited memory resources."
+              callbackUI(errTxt, -1, errTxt)
+
+              tf.engine().endScope()
+              tf.engine().disposeVariables()
+
+              statData.Inference_t = Infinity
+              statData.Postprocess_t = Infinity
+              statData.Status = 'Fail'
+              statData.Error_Type = err2.message
+              statData.Extra_Err_Info = 'prediction_argmax from argMaxLarge failed'
+
+              callbackUI('', -1, '', statData)
+              return 0
+            }
+          } else {
+            // if channel first ..
+            const errTxt = "argMax buffer couldn't be created due to limited memory resources."
+            callbackUI(errTxt, -1, errTxt)
+
+            prediction_argmax.dispose()
+
+            tf.engine().endScope()
+            tf.engine().disposeVariables()
+
+            statData.Inference_t = Infinity
+            statData.Postprocess_t = Infinity
+            statData.Status = 'Fail'
+            statData.Error_Type = err1.message
+            statData.Extra_Err_Info = 'prediction_argmax from argMaxLarge not support yet channel first'
+
+            callbackUI('', -1, '', statData)
+
+            return 0
+          }
+        }
+
+        console.log(' prediction_argmax shape : ', prediction_argmax.shape)
+        // -- prediction_argmax.shape  : [ 1, 256, 256, 256]
+
+        const Inference_t = ((performance.now() - startTime) / 1000).toFixed(4)
+
+        // outputDataBeforArgmx = Array.from(prediction_argmax.dataSync())
+        tf.dispose(curTensor[i])
+        console.log(' find array max ')
+        const curBatchMaxLabel = await prediction_argmax.max().dataSync()[0]
+
+        if (maxLabelPredicted < curBatchMaxLabel) {
+          maxLabelPredicted = curBatchMaxLabel
+        }
+
+        const numSegClasses = maxLabelPredicted + 1
+        console.log('numSegClasses', numSegClasses)
+        statData.Actual_Labels = numSegClasses
+        statData.Expect_Labels = expected_Num_labels
+        statData.NumLabels_Match = numSegClasses === expected_Num_labels
+
+        if (numSegClasses !== expected_Num_labels) {
+          // errTxt = "expected " + expected_Num_labels + " labels, but the predicted are " + numSegClasses + ". For possible solutions please refer to <a href='https://github.com/neuroneural/brainchop/wiki/FAQ#Q3' target='_blank'><b> FAQ </b></a>.", "alert-error"
+          const errTxt = 'expected ' + expected_Num_labels + ' labels, but the predicted are ' + numSegClasses
+          callbackUI(errTxt, -1, errTxt)
+        }
+
+        // -- Transpose back to original unpadded size
+        let outLabelVolume = prediction_argmax.reshape([
+          cropped_slices_3d_w_pad.shape[0],
+          cropped_slices_3d_w_pad.shape[1],
+          cropped_slices_3d_w_pad.shape[2]
+        ])
+        tf.dispose(prediction_argmax)
+
+        // Transpose MRI data to be match pytorch/keras input output
+        if (transpose) {
+          console.log('outLabelVolume transposed')
+          outLabelVolume = outLabelVolume.transpose()
+        }
+        outLabelVolume = await removeZeroPaddingFrom3dTensor(outLabelVolume, pad, pad, pad)
+        console.log(' outLabelVolume without padding shape :  ', outLabelVolume.shape)
+        outLabelVolume = await resizeWithZeroPadding(
+          outLabelVolume,
+          num_of_slices,
+          slice_height,
+          slice_width,
+          refVoxel,
+          boundVolSizeArr
+        )
+        console.log(' outLabelVolume final shape after resizing :  ', outLabelVolume.shape)
+
+        const filterOutWithPreMask = modelEntry.filterOutWithPreMask
+        // To clean the skull area wrongly segmented in phase-2.
+        if (pipeline1_out != null && opts.isBrainCropMaskBased && filterOutWithPreMask) {
+          const bin = binarizeVolumeDataTensor(pipeline1_out)
+          outLabelVolume = outLabelVolume.mul(bin)
+        }
+
+        startTime = performance.now()
+        // Generate output volume or slices
+        console.log('Generating correct output')
+
+        try {
+          const img = new Uint32Array(outLabelVolume.dataSync())
+          const Vshape = outLabelVolume.shape
+          const Vtype = outLabelVolume.dtype
+          tf.dispose(outLabelVolume)
+          tf.engine().endScope()
+          tf.engine().disposeVariables()
+          outimg = await generateOutputSlicesV2(
+            img,
+            Vshape,
+            Vtype,
+            num_of_slices,
+            numSegClasses,
+            slice_height,
+            slice_width,
+            modelEntry,
+            opts,
+            niftiImage
+          )
+          console.log(' Phase-2 num of tensors after generateOutputSlicesV2: ', tf.memory().numTensors)
+        } catch (error) {
+          // -- Timing data to collect
+          tf.engine().endScope()
+          tf.engine().disposeVariables()
+
+          const errTxt = 'Failed while generating output due to limited browser memory available'
+          callbackUI(errTxt, -1, errTxt)
+          statData.Inference_t = Inference_t
+          statData.Postprocess_t = Infinity
+          statData.Status = 'Fail'
+          statData.Error_Type = error.message
+          statData.Extra_Err_Info = 'Failed while generating output'
+
+          callbackUI('', -1, '', statData)
+
+          return 0
+        }
+
+        const Postprocess_t = ((performance.now() - startTime) / 1000).toFixed(4)
+
+        tf.engine().disposeVariables()
+
+        console.log(
+          'Processing the whole brain volume in tfjs for multi-class output mask took : ',
+          ((performance.now() - inferenceStartTime) / 1000).toFixed(4) + '  Seconds'
+        )
+
+        // -- Timing data to collect
+        statData.Inference_t = Inference_t
+        statData.Postprocess_t = Postprocess_t
+        statData.Status = 'OK'
+        callbackUI('Segmentation finished', 0)
+        callbackUI('', -1, '', statData)
+        callbackImg(outimg, opts, modelEntry)
+
+        return 0
+      }
+      i++
+    }
+  } catch (err) {
+    callbackUI(err.message, -1, err.message)
+    console.log(
+      'If webgl context is lost, try to restore webgl context by visit the link ' +
+        '<a href="https://support.biodigital.com/hc/en-us/articles/218322977-How-to-turn-on-WebGL-in-my-browser">here</a>'
+    )
+  }
+}
+
+async function inferenceFullVolumePhase1(
+  model,
+  slices_3d,
+  num_of_slices,
+  slice_height,
+  slice_width,
+  isModelFullVol,
+  modelEntry,
+  statData,
+  opts,
+  niftiHeader,
+  niftiImage
+) {
+  statData.No_SubVolumes = 1
+  // load pre-model for inference first, can be null if no pre-model such as GWM models
+  if (modelEntry.preModelId) {
+    const preModel = await load_model(opts.rootURL + inferenceModelsList[modelEntry.preModelId - 1].path)
+    const transpose = inferenceModelsList[modelEntry.preModelId - 1].enableTranspose
+    const quantileNorm = inferenceModelsList[modelEntry.preModelId - 1].enableQuantileNorm
+    let preModel_slices_3d = null
+
+    // -- If pre-model is not null then slices_3d mask will be generated..
+    // -- The mask is needed to remove the skull and set noise in background to 0, and get the brain bounding volume properly
+    const slices_3d_mask = null
+
+    if (quantileNorm) {
+      // Quantile normalize function needs specific models to be used
+      console.log('preModel Quantile normalization enabled')
+      preModel_slices_3d = await quantileNormalizeVolumeData(slices_3d)
+    } else {
+      // Min Max Nomalize MRI data to be from 0 to 1
+      console.log('preModel Min Max normalization enabled')
+      preModel_slices_3d = await minMaxNormalizeVolumeData(slices_3d)
+    }
+
+    // -- Transpose MRI data to be match pytorch/keras input output
+    // -- Check if pre-model needs transpose..
+    if (transpose) {
+      preModel_slices_3d = preModel_slices_3d.transpose()
+      console.log('Input transposed for pre-model')
+    } else {
+      console.log('Transpose not enabled for pre-model')
+    }
+
+    statData.Brainchop_Ver = 'PreModel_FV' // e.g. "PreModel_FV"
+
+    // preModel.then(function (res) {
+    const res = await preModel
+
+    try {
+      const inferenceStartTime = performance.now()
+      const preModelObject = res
+
+      // read input shape from model.json object
+      const preModelBatchInputShape = preModelObject.layers[0].batchInputShape
+      console.log(' Pre-Model batch input shape : ', preModelBatchInputShape)
+
+      // -- Verify input shape
+      if (preModelBatchInputShape.length !== 5) {
+        const errTxt = 'The pre-model input shape must be 5D '
+        callbackUI(errTxt, -1, errTxt)
+        return 0
+      }
+
+      const isPreModelChannelLast = await isModelChnlLast(preModelObject)
+      const batchSize = opts.batchSize
+      const numOfChan = opts.numOfChan
+      let batch_D, batch_H, batch_W
+      let preModel_input_shape
+      if (isPreModelChannelLast) {
+        console.log('Pre-Model Channel Last')
+        if (isNaN(preModelBatchInputShape[4]) || preModelBatchInputShape[4] !== 1) {
+          const errTxt = 'The number of channels for pre-model input shape must be 1'
+          callbackUI(errTxt, -1, errTxt)
+          return 0
+        }
+
+        batch_D = preModelBatchInputShape[1]
+        batch_H = preModelBatchInputShape[2]
+        batch_W = preModelBatchInputShape[3]
+
+        preModel_input_shape = [batchSize, batch_D, batch_H, batch_W, numOfChan]
+      } else {
+        console.log('Pre-Model Channel First')
+        if (isNaN(preModelBatchInputShape[1]) || preModelBatchInputShape[1] !== 1) {
+          const errTxt = 'The number of channels for pre-model input shape must be 1'
+          callbackUI(errTxt, -1, errTxt)
+          return 0
+        }
+
+        batch_D = preModelBatchInputShape[2]
+        batch_H = preModelBatchInputShape[3]
+        batch_W = preModelBatchInputShape[4]
+
+        preModel_input_shape = [batchSize, numOfChan, batch_D, batch_H, batch_W]
+      }
+
+      statData.Input_Shape = JSON.stringify(preModel_input_shape)
+      statData.Output_Shape = JSON.stringify(preModelObject.output.shape)
+      statData.Channel_Last = await isPreModelChannelLast
+      statData.Model_Param = await getModelNumParameters(preModelObject)
+      statData.Model_Layers = await getModelNumLayers(preModelObject)
+
+      // maxLabelPredicted in whole volume of the brain
+      let maxLabelPredicted = 0
+
+      let i = 1
+      const layersLength = res.layers.length
+
+      const curTensor = []
+      // -- reshape MRI to model input shape
+      curTensor[0] = preModel_slices_3d.reshape(preModel_input_shape)
+
+      // Dispose the volume
+      tf.dispose(preModel_slices_3d)
+      while (true) {
+        try {
+          curTensor[i] = res.layers[i].apply(curTensor[i - 1])
+        } catch (err) {
+          const errTxt = 'Your graphics card (e.g. Intel) may not be compatible with WebGL. ' + err.message
+          callbackUI(errTxt, -1, errTxt)
+
+          tf.engine().endScope()
+          tf.engine().disposeVariables()
+
+          statData.Inference_t = Infinity
+          statData.Postprocess_t = Infinity
+          statData.Status = 'Fail'
+          statData.Error_Type = err.message
+          statData.Extra_Err_Info = 'PreModel Failed while model layer ' + i + ' apply'
+
+          callbackUI('', -1, '', statData)
+
+          return 0
+        }
+
+        res.layers[i].dispose()
+        curTensor[i - 1].dispose()
+
+        callbackUI('Layer ' + i.toString(), (i + 1) / layersLength)
+        if (tf.memory().unreliable) {
+          const unreliableReasons = 'unreliable reasons :' + tf.memory().reasons
+          callbackUI(unreliableReasons, NaN, unreliableReasons)
+        }
+
+        if (i === layersLength - 1) {
+          // -- prediction = res.layers[res.layers.length-1].apply(curTensor[i])
+          // -- curTensor[i].print()
+          // -- outputDataBeforArgmx = Array.from(curTensor[i].dataSync())
+
+          const axis = isPreModelChannelLast ? -1 : 1
+          console.log(' find argmax ')
+          console.log('last Tensor shape : ', curTensor[i].shape)
+          // -- curTensor[i].shape  : [ 1, 256, 256, 256, 3 ]
+          const expected_Num_labels = isPreModelChannelLast ? curTensor[i].shape[4] : curTensor[i].shape[1]
+          let prediction_argmax
+
+          // Try for argMax with model output tensor.
+
+          try {
+            console.log(' Try tf.argMax for fullVolume ..')
+            prediction_argmax = await tf.argMax(curTensor[i], axis)
+          } catch (err1) {
+            // if channel last
+            if (axis === -1) {
+              try {
+                const argMaxLargeTime = performance.now()
+                console.log(' tf.argMax failed .. try argMaxLarge ..')
+                callbackUI('', -1, 'tensor2LightBuffer() is not dead code?')
+                callbackUI('', -1, 'argMaxLarge() is not dead code?')
+                console.log(
+                  'argMaxLarge for fullVolume takes : ',
+                  ((performance.now() - argMaxLargeTime) / 1000).toFixed(4)
+                )
+              } catch (err2) {
+                const errTxt = "argMax buffer couldn't be created due to limited memory resources."
+                callbackUI(errTxt, -1, errTxt)
+
+                prediction_argmax.dispose()
+
+                tf.engine().endScope()
+                tf.engine().disposeVariables()
+
+                statData.Inference_t = Infinity
+                statData.Postprocess_t = Infinity
+                statData.Status = 'Fail'
+                statData.Error_Type = err2.message
+                statData.Extra_Err_Info = 'preModel prediction_argmax from argMaxLarge failed'
+
+                callbackUI('', -1, '', statData)
+
+                return 0
+              }
+            } else {
+              // if channel first ..
+              const errTxt = "argMax buffer couldn't be created due to limited memory resources."
+              callbackUI(errTxt, -1, errTxt)
+
+              prediction_argmax.dispose()
+
+              tf.engine().endScope()
+              tf.engine().disposeVariables()
+
+              statData.Inference_t = Infinity
+              statData.Postprocess_t = Infinity
+              statData.Status = 'Fail'
+              statData.Error_Type = err1.message
+              statData.Extra_Err_Info = 'preModel prediction_argmax from argMaxLarge not support yet channel first'
+
+              callbackUI('', -1, '', statData)
+
+              return 0
+            }
+          }
+
+          console.log(' Pre-model prediction_argmax shape : ', prediction_argmax.shape)
+          // -- prediction_argmax.shape  : [ 1, 256, 256, 256]
+
+          const Inference_t = ((performance.now() - inferenceStartTime) / 1000).toFixed(4)
+
+          tf.dispose(curTensor[i])
+
+          console.log(' Pre-model find array max ')
+          const curBatchMaxLabel = await prediction_argmax.max().dataSync()[0]
+
+          if (maxLabelPredicted < curBatchMaxLabel) {
+            maxLabelPredicted = curBatchMaxLabel
+          }
+
+          const numSegClasses = maxLabelPredicted + 1
+          console.log('Pre-model numSegClasses', numSegClasses)
+
+          statData.Actual_Labels = numSegClasses
+          statData.Expect_Labels = expected_Num_labels
+          statData.NumLabels_Match = numSegClasses === expected_Num_labels
+
+          // -- Transpose back to original unpadded size
+          let outLabelVolume = await prediction_argmax.reshape([num_of_slices, slice_height, slice_width])
+          tf.dispose(prediction_argmax)
+          // Transpose MRI data to be match pytorch/keras input output
+          if (transpose) {
+            console.log('Pre-model outLabelVolume transposed')
+            outLabelVolume = outLabelVolume.transpose()
+          }
+          const startTime = performance.now()
+          // Generate output volume or slices
+          console.log('Generating pre-model output')
+          let slices_3d_mask
+          try {
+            const unstackOutVolumeTensor = await tf.unstack(outLabelVolume)
+            slices_3d_mask = await generateBrainMask(
+              unstackOutVolumeTensor,
+              num_of_slices,
+              slice_height,
+              slice_width,
+              modelEntry,
+              opts,
+              niftiHeader,
+              niftiImage,
+              false
+            )
+            await tf.dispose(outLabelVolume)
+            console.log(' Phase-1 num of tensors after generateBrainMask: ', tf.memory().numTensors)
+          } catch (error) {
+            // -- Timing data to collect
+            tf.engine().endScope()
+            tf.engine().disposeVariables()
+
+            const errTxt = 'Failed while generating pre-model output due to limited browser memory available'
+            callbackUI(errTxt, -1, errTxt)
+
+            statData.Inference_t = Inference_t
+            statData.Postprocess_t = Infinity
+            statData.Status = 'Fail'
+            statData.Error_Type = error.message
+            statData.Extra_Err_Info = 'Pre-model failed while generating output'
+
+            callbackUI('', -1, '', statData)
+
+            return 0
+          }
+          const Postprocess_t = ((performance.now() - startTime) / 1000).toFixed(4)
+          console.log(
+            'Pre-model processing the whole brain volume in tfjs tooks for multi-class output mask : ',
+            ((performance.now() - inferenceStartTime) / 1000).toFixed(4) + '  Seconds'
+          )
+
+          // -- Timing data to collect
+          statData.Inference_t = Inference_t
+          statData.Postprocess_t = Postprocess_t
+          statData.Status = 'OK'
+
+          callbackUI('', -1, '', statData)
+
+          if (slices_3d_mask == null) {
+            const msg = 'slice_3d_mask failed ...'
+            callbackUI(msg, -1, msg)
+            return 0
+          } else {
+            // --Phase-2, After remove the skull try to allocate brain volume and make inferece
+            console.log('--- pre-model done ---')
+            // --mask_3d = slices_3d_mask.greater([0]).asType('bool')
+            // --slices_3d_mask.dispose()
+
+            if (isModelFullVol) {
+              if (modelEntry.enableSeqConv) {
+                // Mask cropping & seq conv
+                // Non-Atlas model (e.g. GWM) needs sequential convolution layer.
+                // Sequential convolution layer to be used after cropping - slow but reliable on most machines
+                console.log('------ Mask Cropping & Seq Convoluton ------')
+                await inferenceFullVolumeSeqCovLayerPhase2(
+                  opts,
+                  modelEntry,
+                  model,
+                  slices_3d,
+                  num_of_slices,
+                  slice_height,
+                  slice_width,
+                  slices_3d_mask,
+                  statData,
+                  niftiImage
+                )
+                return 0
+                // inferenceFullVolumeSeqCovLayerPhase2(model, slices_3d.transpose(), num_of_slices, slice_height, slice_width, slices_3d_mask)
+              } else {
+                // Mask cropping BUT no seq conv
+                console.log('------ Mask Cropping  -  NO Seq Convoluton ------')
+                await inferenceFullVolumePhase2(
+                  model,
+                  slices_3d,
+                  num_of_slices,
+                  slice_height,
+                  slice_width,
+                  slices_3d_mask,
+                  modelEntry,
+                  statData,
+                  opts,
+                  niftiImage
+                )
+                // inferenceFullVolumePhase2(model, slices_3d.transpose(), num_of_slices, slice_height, slice_width, slices_3d_mask)
+              }
+            } else {
+              // -- In version 3.0.0 this function not used
+              callbackUI('', -1, 'inferenceSubVolumes() is not dead code?')
+            }
+          }
+        }
+        i++
+      }
+    } catch (err) {
+      callbackUI(err.message, -1, err.message)
+      console.log(
+        'If webgl context is lost, try to restore webgl context by visit the link ' +
+          '<a href="https://support.biodigital.com/hc/en-us/articles/218322977-How-to-turn-on-WebGL-in-my-browser">here</a>'
+      )
+
+      // document.getElementById("webGl2Status").style.backgroundColor =  isWebGL2ContextLost() ? "Red" : "Green"
+      // document.getElementById("memoryStatus").style.backgroundColor =  tf.memory().unreliable ? "Red" : "Green"
+    }
+    // })
+
+    // -- if(...)  end
+  } else {
+    // No preModel
+
+    // --Phase-2, After remove the skull try to allocate brain volume and make inferece
+    console.log('--- No pre-model is selected ---')
+    console.log('------ Run voxel cropping ------')
+    // -- mask_3d = slices_3d.greater([0]).asType('bool')
+
+    if (isModelFullVol) {
+      if (modelEntry.enableSeqConv) {
+        // Voxel cropping & seq conv
+        // Non-Atlas model (e.g. GWM) needs sequential convolution layer.
+        // Sequential convolution layer to be used after cropping - slow but reliable on most machines
+        console.log('------ Seq Convoluton ------')
+        await inferenceFullVolumeSeqCovLayerPhase2(
+          opts,
+          modelEntry,
+          model,
+          slices_3d,
+          num_of_slices,
+          slice_height,
+          slice_width,
+          null,
+          statData,
+          niftiImage
+        )
+      } else {
+        // Voxel cropping BUT no seq conv
+        // todo: we do not use result const outimg = await
+        inferenceFullVolumePhase2(
+          model,
+          slices_3d,
+          num_of_slices,
+          slice_height,
+          slice_width,
+          null,
+          modelEntry,
+          statData,
+          opts,
+          niftiImage
+        )
+      }
+    } else {
+      // -- In version 3.0.0 this function not used
+      callbackUI('', -1, 'inferenceSubVolumes() is not dead code?')
+    }
+  }
+}
+
+async function enableProductionMode(textureF16Flag = true) {
+  // -- tf.setBackend('cpu')
+  tf.setBackend('webgl')
+  // -- tf.removeBackend('cpu')
+  // -- Calling enableProdMode() method
+  await tf.enableProdMode()
+  // -- Setting debug mode of the  environment
+  tf.env().set('DEBUG', false)
+  tf.env().set('WEBGL_FORCE_F16_TEXTURES', textureF16Flag)
+  // -- set this flag so that textures are deleted when tensors are disposed.
+  tf.env().set('WEBGL_DELETE_TEXTURE_THRESHOLD', -1)
+  // -- tf.env().set('WEBGL_PACK', false)
+  // -- Put ready after sets above
+  await tf.ready()
+  // -- Printing output
+  console.log('tf env() flags :', tf.env().flags)
+  console.log('tf env() features :', tf.env().features)
+  console.log('tf env total features: ', Object.keys(tf.env().features).length)
+  console.log('tf backend: ', tf.getBackend())
+}
+
+async function runInferenceWW(opts, modelEntry, niftiHeader, niftiImage) {
+  const statData = []
+  statData.startTime = Date.now() // for common webworker/mainthread do not use performance.now()
+  callbackUI('Segmentation started', 0)
+  const batchSize = opts.batchSize
+  const numOfChan = opts.numOfChan
+  if (isNaN(batchSize) || batchSize !== 1) {
+    const errTxt = 'The batch Size for input shape must be 1'
+    callbackUI(errTxt, -1, errTxt)
+    return 0
+  }
+  if (isNaN(numOfChan) || numOfChan !== 1) {
+    const errTxt = 'The number of channels for input shape must be 1'
+    callbackUI(errTxt, -1, errTxt)
+    return 0
+  }
+  tf.engine().startScope()
+  console.log('Batch size: ', batchSize)
+  console.log('Num of Channels: ', numOfChan)
+  const model = await load_model(opts.rootURL + modelEntry.path)
+  await enableProductionMode(true)
+  statData.TF_Backend = tf.getBackend()
+  const modelObject = model
+  let batchInputShape = []
+  // free global variable of 16777216 voxel
+  // allOutputSlices3DCC1DimArray = []
+  // outputSceneRendered = false
+  // read input shape from model.json object
+  batchInputShape = modelObject.layers[0].batchInputShape
+  console.log(' Model batch input shape : ', batchInputShape)
+  // -- Verify input shape
+  if (batchInputShape.length !== 5) {
+    const errTxt = 'The model input shape must be 5D'
+    callbackUI(errTxt, -1, errTxt)
+    return 0
+  }
+  let batch_D, batch_H, batch_W
+  const slice_width = niftiHeader.dims[1]
+  const slice_height = niftiHeader.dims[2]
+  const num_of_slices = niftiHeader.dims[3]
+  const isChannelLast = await isModelChnlLast(modelObject)
+  if (isChannelLast) {
+    console.log('Model Channel Last')
+    if (isNaN(batchInputShape[4]) || batchInputShape[4] !== 1) {
+      const errTxt = 'The number of channels for input shape must be 1'
+      callbackUI(errTxt, -1, errTxt)
+      return 0
+    }
+    batch_D = batchInputShape[1]
+    batch_H = batchInputShape[2]
+    batch_W = batchInputShape[3]
+  } else {
+    console.log('Model Channel First')
+    if (isNaN(batchInputShape[1]) || batchInputShape[1] !== 1) {
+      const errTxt = 'The number of channels for input shape must be 1'
+      callbackUI(errTxt, -1, errTxt)
+      return 0
+    }
+    batch_D = batchInputShape[2]
+    batch_H = batchInputShape[3]
+    batch_W = batchInputShape[4]
+  }
+  // const input_shape = [batchSize, numOfChan, batch_D, batch_H, batch_W]
+  // --Check whether the model will make inference at once as FullVolumeModel
+  let isModelFullVol
+  if (batch_D === 256 && batch_H === 256 && batch_W === 256) {
+    isModelFullVol = true
+  } else {
+    isModelFullVol = false
+  }
+  statData.isModelFullVol = isModelFullVol
+  // Model output number of segmentations
+  let slices_3d = await getAllSlicesDataAsTF3D(num_of_slices, niftiHeader, niftiImage)
+  const transpose = modelEntry.enableTranspose
+  const enableCrop = modelEntry.enableCrop
+  if (isModelFullVol) {
+    if (enableCrop) {
+      // FullVolume with Crop option before inference ..
+      // pre-model to mask the volume, can also be null and the cropping will be on the MRI.
+      await inferenceFullVolumePhase1(
+        model,
+        slices_3d,
+        num_of_slices,
+        slice_height,
+        slice_width,
+        isModelFullVol,
+        modelEntry,
+        statData,
+        opts,
+        niftiHeader,
+        niftiImage
+      )
+    } else {
+      // Transpose MRI data to be match pytorch/keras input output
+      console.log('Cropping Disabled')
+
+      if (transpose) {
+        slices_3d = slices_3d.transpose()
+        console.log('Input transposed')
+      } else {
+        console.log('Transpose NOT Enabled')
+      }
+
+      const enableSeqConv = modelEntry.enableSeqConv
+
+      if (enableSeqConv) {
+        callbackUI('', -1, 'inferenceFullVolumeSeqCovLayer() is not dead code?')
+      } else {
+        callbackUI('', -1, 'inferenceFullVolume() is not dead code?')
+      }
+    }
+  }
+}
+
+self.addEventListener(
+  'message',
+  function (event) {
+    runInferenceWW(event.data.opts, event.data.modelEntry, event.data.niftiHeader, event.data.niftiImage)
+  },
+  false
+)