dicePixelClassificationLayer

Create pixel classification layer using generalized Dice loss for semantic segmentation

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Description

A Dice pixel classification layer provides a categorical label for each image pixel or voxel using generalized Dice loss.

The layer uses generalized Dice loss to alleviate the problem of class imbalance in semantic segmentation problems. Generalized Dice loss controls the contribution that each class makes to the loss by weighting classes by the inverse size of the expected region.

Creation

Syntax

layer = dicePixelClassificationLayer
layer = dicePixelClassificationLayer(Name,Value)

Description

example

layer = dicePixelClassificationLayer creates a Dice pixel classification output layer for semantic image segmentation networks. The layer outputs the categorical label for each image pixel or voxel processed by a CNN. The layer automatically ignores undefined pixel labels during training.

layer = dicePixelClassificationLayer(Name,Value) returns a Dice pixel classification output layer using Name,Value pair arguments to set the optional Classes and Name properties. You can specify multiple name-value pairs. Enclose each property name in quotes.

For example, dicePixelClassificationLayer('Name','pixclass') creates a Dice pixel classification layer with the name 'pixclass'.

Properties

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Classes of the output layer, specified as a categorical vector, string array, cell array of character vectors, or 'auto'. If Classes is 'auto', then the software automatically sets the classes at training time. If you specify the string array or cell array of character vectors str, then the software sets the classes of the output layer to categorical(str,str). The default value is 'auto'.

Data Types: char | categorical | string | cell

This property is read-only.

The output size of the layer. The value is 'auto' prior to training, and is specified as a numeric value at training time.

This property is read-only.

Loss function used for training, specified as 'generalizedDiceLoss'.

Layer name, specified as a character vector or a string scalar. To include a layer in a layer graph, you must specify a nonempty unique layer name. If you train a series network with the layer and Name is set to '', then the software automatically assigns a name to the layer at training time.

Data Types: char | string

Number of inputs of the layer. This layer accepts a single input only.

Data Types: double

Input names of the layer. This layer accepts a single input only.

Data Types: cell

Examples

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This example uses:

Open Live Script

Predict the categorical label of every pixel in an input image using a generalized Dice loss function. 

layers = [
      imageInputLayer([480 640 3])
      convolution2dLayer(3,16,'Stride',2,'Padding',1)
      reluLayer
      transposedConv2dLayer(2,4,'Stride',2)
      softmaxLayer
      dicePixelClassificationLayer
      ]  
layers = 
  6x1 Layer array with layers:

     1   ''   Image Input                       480x640x3 images with 'zerocenter' normalization
     2   ''   Convolution                       16 3x3 convolutions with stride [2  2] and padding [1  1  1  1]
     3   ''   ReLU                              ReLU
     4   ''   Transposed Convolution            4 2x2 transposed convolutions with stride [2  2] and cropping [0  0  0  0]
     5   ''   Softmax                           softmax
     6   ''   Dice Pixel Classification Layer   Generalized Dice loss

More About

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References

[1] Crum, William R., Oscar Camara, and Derek LG Hill. "Generalized overlap measures for evaluation and validation in medical image analysis." IEEE Transactions on Medical Imaging. 25.11, 2006, pp. 1451–1461.

[2] Sudre, Carole H., et al. "Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations." Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Springer, Cham, 2017, pp. 240–248.

[3] Milletari, Fausto, Nassir Navab, and Seyed-Ahmad Ahmadi. "V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation". Fourth International Conference on 3D Vision (3DV). Stanford, CA, 2016: pp. 565–571.

Extended Capabilities

See Also

| | | | | | (Deep Learning Toolbox)

Topics

Introduced in R2019b

Computer Vision Toolbox Documentation

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