deepPatchMatch with multiple starting points

High-level function for deep patch matching that uses multiple starting points to reduce sensitivity to initialization.

deepPatchMatchMultiStart(
  movingImage,
  fixedImage,
  movingImageMask,
  fixedImageMask,
  movingPatchSize = 16,
  fixedPatchSize = 16,
  block_name = 20,
  vggmodel,
  subtractor = 127.5,
  patchVarEx = 0.95,
  meanCenter = FALSE,
  numberOfStarts = 1,
  sdAffine = 5,
  transformType = "Rigid",
  verbose = FALSE
)

Arguments

movingImage	input image from which we extract patches that are transformed to the space of the fixed image
fixedImage	input image that provides the fixed reference domain.
movingImageMask	defines the object of interest in the movingImage
fixedImageMask	defines the object of interest in the fixedImage
movingPatchSize	integer greater than or equal to 32.
fixedPatchSize	integer greater than or equal to 32.
block_name	name of vgg feature block, either block2_conv2 or integer. use the former for smaller patch sizes.
vggmodel	prebuilt feature model
subtractor	value to subtract when scaling image intensity; should be chosen to match training paradigm eg 127.5 for vgg and 0.5 for resnet like.
patchVarEx	patch variance explained for ripmmarc
meanCenter	boolean to mean center each patch for ripmmarc
numberOfStarts	the number of starting points to try
sdAffine	standard deviation parameter e.g. 0.15
transformType	one of Rigid, Affine and ScaleShear
verbose	boolean

Value

matched points and transformation

Author

Avants BB

Examples

if (FALSE) {
library( tensorflow )
library( ANTsR )
nP1 = 250
nP2 = 500
psz = 12
img <- ri( 1 ) %>% iMath( "Normalize" )
img2 <- ri( 2 ) %>% iMath( "Normalize" )
mask = randomMask( getMask( img ), nP1 )
mask2 = randomMask( getMask( img2 ), nP2 )
match = deepPatchMatchMultiStart( img2, img, mask, mask2, numberOfStarts=3, verbose=FALSE )
}