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: 'Lesion',
    path: '/models/model_11ch_ACR/model.json',
    modelName: 'T2w Lesion',
    colormapPath: './models/model_11ch_ACR/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: 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:
      'Detects lesion from T2-weighted image.'
  },
] // inferenceModelsList