brainchop-parameters.js

export {inferenceModelsList, brainChopOpts }

const brainChopOpts = {
  // General settings for input shape [batchSize, batch_D, batch_H, batch_W, numOfChan]
  batchSize: 1, // How many batches are used during each inference iteration
  numOfChan: 1, // num of channel of the input shape
  isColorEnable: true, // If false, grey scale will enabled
  isAutoColors: true, // If false, manualColorsRange will be in use
  bgLabelValue: 0, // Semenatic Segmentation background label value
  drawBoundingVolume: false, // plot bounding volume used to crop the brain
  isGPU: true, //use WebGL/GPU (faster) or CPU (compatibility)
  isBrainCropMaskBased: true, // Check if brain masking will be used for cropping & optional show or brain tissue will be used
  showPhase1Output: false, // This will load to papaya the output of phase-1 (ie. brain mask or brain tissue)
  isPostProcessEnable: true, // If true 3D Connected Components filter will apply
  isContoursViewEnable: false, // If true 3D contours of the labeled regions will apply
  browserArrayBufferMaxZDim: 30, // This value depends on Memory available
  telemetryFlag: false, // Ethical and transparent collection of browser usage while adhering to security and privacy standards
  chartXaxisStepPercent: 10, // percent from total labels on Xaxis
  uiSampleName: 'BC_UI_Sample', // Sample name used by interface
  atlasSelectedColorTable: 'Fire' // Select from ["Hot-and-Cold", "Fire", "Grayscale", "Gold", "Spectrum"]
}

// Inference Models, the ids must start from 1 in sequence
const inferenceModelsList = [
  {
    id: 1,
    type: 'Segmentation',
    path: '/models/model5_gw_ae/model.json',
    modelName: '\u26A1 Tissue GWM (light)',
    labelsPath: './models/model5_gw_ae/labels.json',
    colorsPath: './models/model5_gw_ae/colorLUT.json',
    colormapPath: './models/model5_gw_ae/colormap3.json',
    preModelId: null, // Model run first e.g.  crop the brain   { null, 1, 2, ..  }
    preModelPostProcess: false, // If true, perform postprocessing to remove noisy regions after preModel inference generate output.
    isBatchOverlapEnable: false, // create extra overlap batches for inference
    numOverlapBatches: 0, // Number of extra overlap batches for inference
    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: 18, // Padding size add to cropped brain
    autoThreshold: 0, // 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
    textureSize: 0, // Requested Texture size for the model, if unknown can be 0.
    warning: null, // Warning message to show when select the model.
    inferenceDelay: 100, // Delay in ms time while looping layers applying.
    description:
      'Gray and white matter segmentation model. Operates on full T1 image in a single pass, but uses only 5 filters per layer. Can work on integrated graphics cards but is barely large enough to provide good accuracy. Still more accurate than the subvolume model.'
  },
  {
    id: 2,
    type: 'Segmentation',
    path: '/models/model20chan3cls/model.json',
    modelName: '\u{1F52A} Tissue GWM (High Acc)',
    labelsPath: './models/model20chan3cls/labels.json',
    colorsPath: './models/model20chan3cls/colorLUT.json',
    colormapPath: './models/model20chan3cls/colormap.json',
    preModelId: null, // Model run first e.g.  crop the brain   { null, 1, 2, ..  }
    preModelPostProcess: false, // If true, perform postprocessing to remove noisy regions after preModel inference generate output.
    isBatchOverlapEnable: false, // create extra overlap batches for inference
    numOverlapBatches: 0, // Number of extra overlap batches for inference
    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: 0, // 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
    enableQuantileNorm: true, // 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
    textureSize: 0, // Requested Texture size for the model, if unknown can be 0.
    warning:
      "This model may need dedicated graphics card.  For more info please check with Browser Resources <i class='fa fa-cogs'></i>.",
    inferenceDelay: 100, // Delay in ms time while looping layers applying.
    description:
      'Gray and white matter segmentation model. Operates on full T1 image in a single pass but needs a dedicated graphics card to operate. Provides the best accuracy with hard cropping for better speed'
  },
  {
    id: 3,
    type: 'Segmentation',
    path: '/models/model20chan3cls/model.json',
    modelName: '\u{1F52A} Tissue GWM (High Acc, Low Mem)',
    labelsPath: './models/model20chan3cls/labels.json',
    colorsPath: './models/model20chan3cls/colorLUT.json',
    colormapPath: './models/model20chan3cls/colormap.json',
    preModelId: null, // Model run first e.g.  crop the brain   { null, 1, 2, ..  }
    preModelPostProcess: false, // If true, perform postprocessing to remove noisy regions after preModel inference generate output.
    isBatchOverlapEnable: false, // create extra overlap batches for inference
    numOverlapBatches: 0, // Number of extra overlap batches for inference
    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: 0, // 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
    enableQuantileNorm: true, // Some models needs Quantile Normaliztion.
    filterOutWithPreMask: false, // Can be used to multiply final output with premodel output mask to crean noisy areas
    enableSeqConv: true, // For low memory system and low configuration, enable sequential convolution instead of last layer
    textureSize: 0, // Requested Texture size for the model, if unknown can be 0.
    warning:
      "This model may need dedicated graphics card.  For more info please check with Browser Resources <i class='fa fa-cogs'></i>.",
    inferenceDelay: 100, // Delay in ms time while looping layers applying.
    description:
      'Gray and white matter segmentation model. Operates on full T1 image in a single pass but needs a dedicated graphics card to operate. Provides high accuracy and fit low memory available but slower'
  },
  {
    id: 4,
    type: 'Atlas',
    path: '/models/model30chan18cls/model.json',
    modelName: '\u{1FA93} Subcortical + GWM (High Mem, Fast)',
    labelsPath: './models/model30chan18cls/labels.json',
    colorsPath: './models/model30chan18cls/colorLUT.json',
    colormapPath: './models/model30chan18cls/colormap.json',
    preModelId: null, // Model run first e.g.  crop the brain  { null, 1, 2, ..  }
    preModelPostProcess: false, // If true, perform postprocessing to remove noisy regions after preModel inference generate output.
    isBatchOverlapEnable: false, // create extra overlap batches for inference
    numOverlapBatches: 200, // Number of extra overlap batches for inference
    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: 0, // 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
    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
    textureSize: 0, // Requested Texture size for the model, if unknown can be 0.
    warning:
      "This model may need dedicated graphics card.  For more info please check with Browser Resources <i class='fa fa-cogs'></i>.", // Warning message to show when select the model.
    inferenceDelay: 100, // Delay in ms time while looping layers applying.
    description:
      'Parcellation of the brain into 17 regions: gray and white matter plus subcortical areas. This is a robust model able to handle range of data quality, including varying saturation, and even clinical scans. It may work on infant brains, but your mileage may vary.'
  },
  {
    id: 5,
    type: 'Atlas',
    path: '/models/model30chan18cls/model.json',
    modelName: '\u{1FA93} Subcortical + GWM (Low Mem, Slow)',
    labelsPath: './models/model30chan18cls/labels.json',
    colorsPath: './models/model30chan18cls/colorLUT.json',
    colormapPath: './models/model30chan18cls/colormap.json',
    preModelId: null, // Model run first e.g.  crop the brain  { null, 1, 2, ..  }
    preModelPostProcess: false, // If true, perform postprocessing to remove noisy regions after preModel inference generate output.
    isBatchOverlapEnable: false, // create extra overlap batches for inference
    numOverlapBatches: 200, // Number of extra overlap batches for inference
    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: 0, // 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
    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: true, // For low memory system and low configuration, enable sequential convolution instead of last layer
    textureSize: 0, // Requested Texture size for the model, if unknown can be 0.
    warning:
      "This model may need dedicated graphics card.  For more info please check with Browser Resources <i class='fa fa-cogs'></i>.", // Warning message to show when select the model.
    inferenceDelay: 100, // Delay in ms time while looping layers applying.
    description:
      'Parcellation of the brain into 17 regions: gray and white matter plus subcortical areas. This is a robust model able to handle range of data quality, including varying saturation, and even clinical scans. It may work on infant brains, but your mileage may vary.'
  },
  {
    id: 6,
    type: 'Atlas',
    path: '/models/model18cls/model.json',
    modelName: '\u{1FA93} Subcortical + GWM (Low Mem, Faster)',
    labelsPath: './models/model18cls/labels.json',
    colorsPath: './models/model18cls/colorLUT.json',
    colormapPath: './models/model18cls/colormap.json',
    preModelId: null, // model run first e.g.  Brain_Extraction  { null, 1, 2, ..  }
    preModelPostProcess: false, // If true, perform postprocessing to remove noisy regions after preModel inference generate output.
    isBatchOverlapEnable: false, // create extra overlap batches for inference
    numOverlapBatches: 200, // Number of extra overlap batches for inference
    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: 0, // 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
    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: true, // For low memory system and low configuration, enable sequential convolution instead of last layer
    textureSize: 0, // Requested Texture size for the model, if unknown can be 0.
    warning:
      "This model may need dedicated graphics card.  For more info please check with Browser Resources <i class='fa fa-cogs'></i>.", // Warning message to show when select the model.
    inferenceDelay: 100, // Delay in ms time while looping layers applying.
    description:
      'Parcellation of the brain into 17 regions: gray and white matter plus subcortical areas. This is a robust model able to handle range of data quality, including varying saturation, and even clinical scans. It may work on infant brains, but your mileage may vary.'
  },
  {
    id: 7,
    type: 'Atlas',
    path: '/models/model30chan18cls/model.json',
    modelName: '\u{1F52A}\u{1FA93} Subcortical + GWM (Failsafe, Less Acc)',
    labelsPath: './models/model30chan18cls/labels.json',
    colorsPath: './models/model30chan18cls/colorLUT.json',
    colormapPath: './models/model30chan18cls/colormap.json',
    preModelId: 1, // model run first e.g.  Brain_Extraction  { null, 1, 2, ..  }
    preModelPostProcess: false, // If true, perform postprocessing to remove noisy regions after preModel inference generate output.
    isBatchOverlapEnable: false, // create extra overlap batches for inference
    numOverlapBatches: 200, // Number of extra overlap batches for inference
    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: 0, // Padding size add to cropped brain
    autoThreshold: 0, // 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
    textureSize: 0, // Requested Texture size for the model, if unknown can be 0.
    warning:
      "This model may need dedicated graphics card.  For more info please check with Browser Resources <i class='fa fa-cogs'></i>.", // Warning message to show when select the model.
    inferenceDelay: 100, // Delay in ms time while looping layers applying.
    description:
      'Parcellation of the brain into 17 regions: gray and white matter plus subcortical areas. This is not a robust model, it may work on low data quality, including varying saturation, and even clinical scans. It may work also on infant brains, but your mileage may vary.'
  },
  {
    id: 8,
    type: 'Atlas',
    path: '/models/model30chan50cls/model.json',
    modelName: '\u{1F52A} Aparc+Aseg 50 (High Mem, Fast)',
    labelsPath: './models/model30chan50cls/labels.json',
    colorsPath: './models/model30chan50cls/colorLUT.json',
    colormapPath: './models/model30chan50cls/colormap.json',
    preModelId: 1, // Model run first e.g.  crop the brain  { null, 1, 2, ..  }
    preModelPostProcess: false, // If true, perform postprocessing to remove noisy regions after preModel inference generate output.
    isBatchOverlapEnable: false, // create extra overlap batches for inference
    numOverlapBatches: 200, // Number of extra overlap batches for inference
    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: 0, // Padding size add to cropped brain
    autoThreshold: 0, // 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: true, // 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
    textureSize: 0, // Requested Texture size for the model, if unknown can be 0.
    warning:
      "This model may need dedicated graphics card.  For more info please check with Browser Resources <i class='fa fa-cogs'></i>.", // Warning message to show when select the model.
    inferenceDelay: 100, // Delay in ms time while looping layers applying.
    description:
      'This is a 50-class model, that segments the brain into the Aparc+Aseg Freesurfer Atlas but one where cortical homologues are merged into a single class.'
  },
  {
    id: 9,
    type: 'Atlas',
    path: '/models/model30chan50cls/model.json',
    modelName: '\u{1F52A} Aparc+Aseg 50 (Low Mem, Slow)',
    labelsPath: './models/model30chan50cls/labels.json',
    colorsPath: './models/model30chan50cls/colorLUT.json',
    colormapPath: './models/model30chan50cls/colormap.json',
    preModelId: 1, // Model run first e.g.  crop the brain  { null, 1, 2, ..  }
    preModelPostProcess: false, // If true, perform postprocessing to remove noisy regions after preModel inference generate output.
    isBatchOverlapEnable: false, // create extra overlap batches for inference
    numOverlapBatches: 200, // Number of extra overlap batches for inference
    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: 0, // Padding size add to cropped brain
    autoThreshold: 0, // 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: true, // Some models needs Quantile Normaliztion.
    filterOutWithPreMask: false, // Can be used to multiply final output with premodel output mask to crean noisy areas
    enableSeqConv: true, // For low memory system and low configuration, enable sequential convolution instead of last laye
    textureSize: 0, // Requested Texture size for the model, if unknown can be 0.
    warning:
      "This model may need dedicated graphics card.  For more info please check with Browser Resources <i class='fa fa-cogs'></i>.", // Warning message to show when select the model.
    inferenceDelay: 100, // Delay in ms time while looping layers applying.
    description:
      'This is a 50-class model, that segments the brain into the Aparc+Aseg Freesurfer Atlas but one where cortical homologues are merged into a single class. The model use sequential convolution for inference to overcome browser memory limitations but leads to longer computation time.'
  },
  // './models/model5_gw_ae/colorLUT.json',
  {
    id: 10,
    type: 'Brain_Extraction',
    path: '/models/model5_gw_ae/model.json',
    modelName: '\u26A1 Extract the Brain (FAST)',
    labelsPath: null,
    colorsPath: null,
    preModelId: null, // Model run first e.g.  crop the brain  { null, 1, 2, ..  }
    preModelPostProcess: false, // If true, perform postprocessing to remove noisy regions after preModel inference generate output.
    isBatchOverlapEnable: false, // create extra overlap batches for inference
    numOverlapBatches: 0, // Number of extra overlap batches for inference
    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: 18, // Padding size add to cropped brain
    autoThreshold: 0, // 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
    textureSize: 0, // Requested Texture size for the model, if unknown can be 0.
    warning: null, // Warning message to show when select the model.
    inferenceDelay: 100, // Delay in ms time while looping layers applying.
    description:
      'Extract the brain fast model operates on full T1 image in a single pass, but uses only 5 filters per layer. Can work on integrated graphics cards but is barely large enough to provide good accuracy. Still more accurate than the failsafe version.'
  },
  {
    id: 11,
    type: 'Brain_Extraction',
    path: '/models/model11_gw_ae/model.json',
    modelName: '\u{1F52A} Extract the Brain (High Acc, Slow)',
    labelsPath: null,
    colorsPath: null,
    preModelId: null, // Model run first e.g.  crop the brain  { null, 1, 2, ..  }
    preModelPostProcess: false, // If true, perform postprocessing to remove noisy regions after preModel inference generate output.
    isBatchOverlapEnable: false, // create extra overlap batches for inference
    numOverlapBatches: 0, // Number of extra overlap batches for inference
    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: 0, // Padding size add to cropped brain
    autoThreshold: 0, // 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: true, // For low memory system and low configuration, enable sequential convolution instead of last layer
    textureSize: 0, // Requested Texture size for the model, if unknown can be 0.
    warning:
      "This model may need dedicated graphics card.  For more info please check with Browser Resources <i class='fa fa-cogs'></i>.",
    inferenceDelay: 100, // Delay in ms time while looping layers applying.
    description:
      'Extract the brain high accuracy model operates on full T1 image in a single pass, but uses only 11 filters per layer. Can work on dedicated graphics cards. Still more accurate than the fast version.'
  },
  {
    id: 12,
    type: 'Brain_Masking',
    path: '/models/model5_gw_ae/model.json',
    modelName: '\u26A1 Brain Mask (FAST)',
    labelsPath: null,
    colorsPath: null,
    colormapPath: './models/model5_gw_ae/colormap.json',
    preModelId: null, // Model run first e.g.  crop the brain  { null, 1, 2, ..  }
    preModelPostProcess: false, // If true, perform postprocessing to remove noisy regions after preModel inference generate output.
    isBatchOverlapEnable: false, // create extra overlap batches for inference
    numOverlapBatches: 0, // Number of extra overlap batches for inference
    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: 17, // Padding size add to cropped brain
    autoThreshold: 0, // 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
    textureSize: 0, // Requested Texture size for the model, if unknown can be 0.
    warning: null, // Warning message to show when select the model.
    inferenceDelay: 100, // Delay in ms time while looping layers applying.
    description:
      'This fast masking model operates on full T1 image in a single pass, but uses only 5 filters per layer. Can work on integrated graphics cards but is barely large enough to provide good accuracy. Still more accurate than failsafe version.'
  },
  {
    id: 13,
    type: 'Brain_Masking',
    path: '/models/model11_gw_ae/model.json',
    modelName: '\u{1F52A} Brain Mask (High Acc, Low Mem)',
    labelsPath: null,
    colorsPath: null,
    preModelId: null, // Model run first e.g.  crop the brain  { null, 1, 2, ..  }
    preModelPostProcess: false, // If true, perform postprocessing to remove noisy regions after preModel inference generate output.
    isBatchOverlapEnable: false, // create extra overlap batches for inference
    numOverlapBatches: 0, // Number of extra overlap batches for inference
    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: 0, // Padding size add to cropped brain
    autoThreshold: 0, // 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: true, // Some models needs Quantile Normaliztion.
    filterOutWithPreMask: false, // Can be used to multiply final output with premodel output mask to crean noisy areas
    enableSeqConv: true, // For low memory system and low configuration, enable sequential convolution instead of last layer
    textureSize: 0, // Requested Texture size for the model, if unknown can be 0.
    warning:
      "This model may need dedicated graphics card.  For more info please check with Browser Resources <i class='fa fa-cogs'></i>.",
    inferenceDelay: 100, // Delay in ms time while looping layers applying.
    description:
      'This masking model operates on full T1 image in a single pass, but uses 11 filters per layer. Can work on dedicated graphics cards. Still more accurate than fast version.'
  },
  {
    id: 14,
    type: 'Atlas',
    path: '/models/model21_104class/model.json',
    modelName: '\u{1F52A} Aparc+Aseg 104 (High Mem, Fast)',
    labelsPath: './models/model21_104class/labels.json',
    colorsPath: './models/model21_104class/colorLUT.json',
    colormapPath: './models/model21_104class/colormap.json',
    preModelId: 1, // model run first e.g.  Brain_Extraction  { null, 1, 2, ..  }
    preModelPostProcess: false, // If true, perform postprocessing to remove noisy regions after preModel inference generate output.
    isBatchOverlapEnable: false, // create extra overlap batches for inference
    numOverlapBatches: 200, // Number of extra overlap batches for inference
    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: 0, // Padding size add to cropped brain
    autoThreshold: 0, // 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
    textureSize: 0, // Requested Texture size for the model, if unknown can be 0.
    warning:
      "This model may need dedicated graphics card.  For more info please check with Browser Resources <i class='fa fa-cogs'></i>.", // Warning message to show when select the model.
    inferenceDelay: 100, // Delay in ms time while looping layers applying.
    description:
      'FreeSurfer aparc+aseg atlas 104 parcellate brain areas into 104 regions. It contains a combination of the Desikan-Killiany atlas for cortical area and also segmentation of subcortical regions.'
  },
  {
    id: 15,
    type: 'Atlas',
    path: '/models/model21_104class/model.json',
    modelName: '\u{1F52A} Aparc+Aseg 104 (Low Mem, Slow)',
    labelsPath: './models/model21_104class/labels.json',
    colorsPath: './models/model21_104class/colorLUT.json',
    colormapPath: './models/model21_104class/colormap.json',
    preModelId: 1, // model run first e.g.  Brain_Extraction  { null, 1, 2, ..  }
    preModelPostProcess: false, // If true, perform postprocessing to remove noisy regions after preModel inference generate output.
    isBatchOverlapEnable: false, // create extra overlap batches for inference
    numOverlapBatches: 200, // Number of extra overlap batches for inference
    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: 0, // Padding size add to cropped brain
    autoThreshold: 0, // 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: true, // For low memory system and low configuration, enable sequential convolution instead of last layer
    textureSize: 0, // Requested Texture size for the model, if unknown can be 0.
    warning:
      "This model may need dedicated graphics card.  For more info please check with Browser Resources <i class='fa fa-cogs'></i>.", // Warning message to show when select the model.
    inferenceDelay: 100, // Delay in ms time while looping layers applying.
    description:
      'FreeSurfer aparc+aseg atlas 104 parcellate brain areas into 104 regions. It contains a combination of the Desikan-Killiany atlas for cortical area and also segmentation of subcortical regions. The model use sequential convolution for inference to overcome browser memory limitations but leads to longer computation time. '
  }
] // inferenceModelsList