createResUnetModel2D: 2-D implementation of the Resnet + U-net deep learning...

In ANTsX/ANTsRNet: Neural Networks for Medical Image Processing

2-D implementation of the Resnet + U-net deep learning architecture.

Description

Creates a keras model of the U-net + ResNet deep learning architecture for image segmentation and regression with the paper available here:

Usage

createResUnetModel2D(
  inputImageSize,
  numberOfOutputs = 1,
  numberOfFiltersAtBaseLayer = 32,
  bottleNeckBlockDepthSchedule = c(3, 4),
  convolutionKernelSize = c(3, 3),
  deconvolutionKernelSize = c(2, 2),
  dropoutRate = 0,
  weightDecay = 1e-04,
  mode = c("classification", "regression")
)

Arguments

inputImageSize	Used for specifying the input tensor shape. The shape (or dimension) of that tensor is the image dimensions followed by the number of channels (e.g., red, green, and blue). The batch size (i.e., number of training images) is not specified a priori.
numberOfOutputs	Meaning depends on the mode. For 'classification' this is the number of segmentation labels. For 'regression' this is the number of outputs.
numberOfFiltersAtBaseLayer	number of filters at the beginning and end of the ⁠'U'⁠. Doubles at each descending/ascending layer.
bottleNeckBlockDepthSchedule	vector that provides the encoding layer schedule for the number of bottleneck blocks per long skip connection.
convolutionKernelSize	2-d vector defining the kernel size during the encoding path
deconvolutionKernelSize	2-d vector defining the kernel size during the decoding
dropoutRate	float between 0 and 1 to use between dense layers.
weightDecay	weighting parameter for L2 regularization of the kernel weights of the convolution layers. Default = 0.0.
mode	'classification' or 'regression'.

Details

    \url{https://arxiv.org/abs/1608.04117}

This particular implementation was ported from the following python implementation:

    \url{https://github.com/veugene/fcn_maker/}

Value

a res/u-net keras model

Author(s)

Tustison NJ

Examples

library( ANTsR )
library( ANTsRNet )
library( keras )

imageIDs <- c( "r16", "r27", "r30", "r62", "r64", "r85" )
trainingBatchSize <- length( imageIDs )

# Perform simple 3-tissue segmentation.

segmentationLabels <- c( 1, 2, 3 )
numberOfLabels <- length( segmentationLabels )
initialization <- paste0( 'KMeans[', numberOfLabels, ']' )

domainImage <- antsImageRead( getANTsRData( imageIDs[1] ) )

X_train <- array( data = NA, dim = c( trainingBatchSize, dim( domainImage ), 1 ) )
Y_train <- array( data = NA, dim = c( trainingBatchSize, dim( domainImage ) ) )

images <- list()
segmentations <- list()

for( i in seq_len( trainingBatchSize ) )
  {
  cat( "Processing image", imageIDs[i], "\n" )
  image <- antsImageRead( getANTsRData( imageIDs[i] ) )
  mask <- getMask( image )
  segmentation <- atropos( image, mask, initialization )$segmentation

  X_train[i,,, 1] <- as.array( image )
  Y_train[i,,] <- as.array( segmentation )
  }
Y_train <- encodeUnet( Y_train, segmentationLabels )

# Perform a simple normalization

X_train <- ( X_train - mean( X_train ) ) / sd( X_train )

# Create the model

model <- createResUnetModel2D( c( dim( domainImage ), 1 ),
  numberOfOutputs = numberOfLabels )
rm(domainImage); gc()

metric_multilabel_dice_coefficient <-
  custom_metric( "multilabel_dice_coefficient",
    multilabel_dice_coefficient )

loss_dice <- function( y_true, y_pred ) {
  -multilabel_dice_coefficient(y_true, y_pred)
}
attr(loss_dice, "py_function_name") <- "multilabel_dice_coefficient"

model %>% compile( loss = loss_dice,
  optimizer = optimizer_adam( lr = 0.0001 ),
  metrics = c( metric_multilabel_dice_coefficient,
    metric_categorical_crossentropy ) )

# Comment out the rest due to travis build constraints

# Fit the model

# track <- model %>% fit( X_train, Y_train,
#              epochs = 100, batch_size = 4, verbose = 1, shuffle = TRUE,
#              callbacks = list(
#                callback_model_checkpoint( "resUnetModelInterimWeights.h5",
#                    monitor = 'val_loss', save_best_only = TRUE ),
#                callback_reduce_lr_on_plateau( monitor = "val_loss", factor = 0.1 )
#              ),
#              validation_split = 0.2 )
rm(X_train); gc()
rm(Y_train); gc()
# Save the model and/or save the model weights

# save_model_hdf5( model, filepath = 'resUnetModel.h5' )
# save_model_weights_hdf5( unetModel, filepath = 'resUnetModelWeights.h5' ) )
rm(model); gc()

ANTsX/ANTsRNet documentation built on Nov. 21, 2024, 4:07 a.m.