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)',

    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)',

    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)',

    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)',

    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)',

    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)',

    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)',

    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)',

    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)',

    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)',

    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)',

    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)',

    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)',

    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)',

    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)',

    colormapPath: './models/model21_104class/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, // 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