binary_surface_loss: Function for surface loss

In ANTsX/ANTsRNet: Neural Networks for Medical Image Processing

Function for surface loss

Description

https://pubmed.ncbi.nlm.nih.gov/33080507/

Usage

binary_surface_loss(y_true, y_pred, dimensionality = 3L)

Arguments

y_true	True labels (Tensor)
y_pred	Predictions (Tensor of the same shape as y_true)

Details

ported from this implementation:

https://github.com/LIVIAETS/boundary-loss/blob/master/keras_loss.py

Note: Assumption is that y_true is a one-hot representation of the segmentation batch. The background (label 0) should be included but is not used in the calculation.

Value

function value

Author(s)

Tustison NJ

Examples

library( ANTsRNet )
library( keras )

model <- createUnetModel2D( c( 64, 64, 1 ), numberOfOutputs = 2 )

model %>% compile( loss = binary_surface_loss,
 optimizer = optimizer_adam( lr = 0.0001 ),
   metrics = "accuracy" )

########################################
#
# Run in isolation
#

library( ANTsR )

r16 <- antsImageRead( getANTsRData( "r16" ) )
r16seg <- kmeansSegmentation( r16, 3 )$segmentation
r16array <- array( data = as.array( r16seg ), dim = c( 1, dim( r16seg ) ) )
r16tensor <- tensorflow::tf$convert_to_tensor( encodeUnet( r16array, c( 0, 1, 2, 3 ) ) )

r64 <- antsImageRead( getANTsRData( "r64" ) )
r64seg <- kmeansSegmentation( r64, 3 )$segmentation
r64array <- array( data = as.array( r64seg ), dim = c( 1, dim( r64seg ) ) )
r64tensor <- tensorflow::tf$convert_to_tensor( encodeUnet( r64array, c( 0, 1, 2, 3 ) ) )

surface_loss <- binary_surface_loss( r16tensor, r64tensor, dimensionality = 2L )
loss_value <- surface_loss( r16tensor, r64tensor )$numpy()

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