unet3d

Create 3-D U-Net convolutional neural network for semantic segmentation of volumetric images

Since R2024a

Syntax

unet3dNetwork = unet3d(inputSize,numClasses)

[unet3dNetwork,outputSize] = unet3d(inputSize,numClasses)

[___] = unet3d(inputSize,numClasses,Name=Value)

Description

unet3dNetwork = unet3d(inputSize,numClasses) returns a 3-D U-Net network. unet3d includes a pixel classification layer in the network to predict the categorical label for each pixel in an input volumetric image.

Use unet3d to create the network architecture for 3-D U-Net. Train the network using the Deep Learning Toolbox™ function trainnet (Deep Learning Toolbox).

[unet3dNetwork,outputSize] = unet3d(inputSize,numClasses) also returns the size of an output volumetric image from the 3-D U-Net network.

[___] = unet3d(inputSize,numClasses,Name=Value) specifies options using one or more name-value arguments in addition to the input arguments in previous syntax. For example, specify unet3d(inputSize,numClasses,EncoderDepth=4) to set the encoder depth to 4.

Examples

Create 3-D U-Net Network with Custom Encoder-Decoder Depth

Create a 3-D U-Net network with an encoder-decoder depth of 2. Specify the number of output channels for the first convolution layer as 16.

imageSize = [128 128 128 3];
numClasses = 5;
encoderDepth = 2;
unet3dNetwork = unet3d(imageSize,numClasses,EncoderDepth=encoderDepth,NumFirstEncoderFilters=16) 

unet3dNetwork = 
  dlnetwork with properties:

         Layers: [45×1 nnet.cnn.layer.Layer]
    Connections: [48×2 table]
     Learnables: [46×3 table]
          State: [20×3 table]
     InputNames: {'encoderImageInputLayer'}
    OutputNames: {'FinalNetworkSoftmax-Layer'}
    Initialized: 1

  View summary with summary.

Display the network.

figure(Units="normalized",Position=[0 0 0.5 0.55]);
plot(unet3dNetwork)

Use the deep learning network analyzer to visualize the 3-D U-Net network.

analyzeNetwork(unet3dNetwork);

The visualization shows the number of output channels for each encoder stage. The first convolution layers in encoder stages 1 and 2 have 16 and 32 output channels, respectively. The second convolution layers in encoder stages 1 and 2 have 32 and 64 output channels, respectively.

Input Arguments

inputSize — Network input image size

three-element vector | four-element vector

Network input image size representing a volumetric image, specified as one of these values:

• Three-element vector of the form [height width depth]

• Four-element vector of the form [height width depth channel]. channel denotes the number of image channels.

Note

Network input image size must be chosen such that the dimension of the inputs to the max-pooling layers must be even numbers.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

numClasses — Number of classes

scalar greater than 1

Number of classes to segment, specified as a scalar greater than 1.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Example: unet3d(inputSize,numClasses,EncoderDepth=4) sets the encoder depth to 4.

EncoderNetwork — Encoder network

dlnetwork object

Encoder network that unet3d uses as the encoder, specified as a dlnetwork (Deep Learning Toolbox) object. You can specify a pretrained or custom encoder network. To use a pretrained encoder network, create the network using the pretrainedEncoderNetwork function.

Note

If you specify EncoderNetwork, the unet3d function does not use these name-value arguments to configure the network:

• NumFirstEncoderFilters

• FilterSize

• ConvolutionPadding

EncoderDepth — Encoder depth

3 (default) | positive integer

Encoder depth, specified as a positive integer. The 3-D U-Net network is composed of an encoder subnetwork and a corresponding decoder subnetwork. The depth of the network determines the number of times the input volumetric image is downsampled or upsampled during processing. The encoder network downsamples the input volumetric image by a factor of 2^D, where D is the value of EncoderDepth. The decoder network upsamples the encoder network output by a factor of 2^D. The depth of the decoder subnetwork is same as that of the encoder subnetwork.

Note

If you also specify EncoderNetwork, specify the value of EncoderDepth using the depth of the EncoderNetwork input.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

NumFirstEncoderFilters — Number of output channels for first convolution layer

32 (default) | positive integer

Number of output channels for the first convolution layer in the first encoder stage, specified as a positive integer. The number of output channels for the second convolution layer and the convolution layers in the subsequent encoder stages is set based on this value.

Given stage = {1, 2, …, EncoderDepth}, the number of output channels for the first convolution layer in each encoder stage is equal to

2^stage-1 NumFirstEncoderFilters

The number of output channels for the second convolution layer in each encoder stage is equal to

2^stage NumFirstEncoderFilters

The unet3d function sets the number of output channels for convolution layers in the decoder stages to match the number in the corresponding encoder stage.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

FilterSize — Size of 3-D convolution filter

3 (default) | positive scalar integer | three-element row vector of positive integers

Size of the 3-D convolution filter, specified as a positive scalar integer or a three-element row vector of positive integers of the form [fh fw fd]. Typical values for filter dimensions are in the range [3, 7].

If you specify FilterSize as a positive scalar integer of value a, then the convolution kernel is of uniform size [a a a].

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

ConvolutionPadding — Type of padding

"same" (default) | "valid"

Type of padding, specified as "same" or "valid". The type of padding specifies the padding style for the convolution3dLayer (Deep Learning Toolbox) in the encoder and the decoder subnetworks. The spatial size of the output feature map depends on the type of padding. Specify one of these options:

• "same" — Zero padding is applied to the inputs to convolution layers such that the output and input feature maps are the same size.

• "valid" — Zero padding is not applied to the inputs to convolution layers. The convolution layer returns only values of the convolution that are computed without zero padding. The output feature map is smaller than the input feature map.

.

Note

To ensure that the height, width, and depth values of the inputs to max-pooling layers are even, choose the network input image size to confirm to any one of these criteria:

• If you specify ConvolutionPadding as "same", then the height, width, and depth of the input volumetric image must be a multiple of 2^D.

• If you specify ConvolutionPadding as "valid", then the height, width, and depth of the input volumetric image must be chosen such that $$\frac{1}{2^D}\left(height-{\displaystyle \sum _{i=1}^D{2}^i}(f_h-1)\right)$$, $$\frac{1}{2^D}\left(width-{\displaystyle \sum _{i=1}^D{2}^i}(f_w-1)\right)$$, and $$\frac{1}{2^D}\left(depth-{\displaystyle \sum _{i=1}^D{2}^i}(f_d-1)\right)$$ are multiples of 2^D.

  where f_h, f_w and f_d are the height, width, and depth of the three-dimensional convolution kernel, respectively. D is the encoder depth.

Data Types: char | string

Output Arguments

unet3dNetwork — Layers

dlnetwork object

Layers that represent the 3-D U-Net network architecture, returned as a dlnetwork (Deep Learning Toolbox) object.

outputSize — Network output image size

four-element vector

Network output image size, returned as a four-element vector of the form [height, width, depth, channels]. channels is the number of output channels and is equal to the number of classes specified at the input. The height, width, and depth of the output image from the network depend on the type of padding convolution.

• If you specify ConvolutionPadding as "same", then the height, width, and depth of the network output image are the same as that of the network input image.

• If you specify ConvolutionPadding as "valid", then the height, width, and depth of the network output image are less than that of the network input image.

Data Types: double

More About

3-D U-Net Architecture

• The 3-D U-Net architecture consists of an encoder subnetwork and decoder subnetwork that are connected by a bridge section.

• The encoder and decoder subnetworks in the 3-D U-Net architecture consist of multiple stages. EncoderDepth, which specifies the depth of the encoder and decoder subnetworks, sets the number of stages.

• Each encoder stage in the 3-D U-Net network consists of two sets of convolutional, batch normalization, and ReLU layers. The ReLU layer is followed by a 2-by-2-by-2 max pooling layer. Likewise, each decoder stage consists of a transposed convolution layer for upsampling, followed by two sets of convolutional, batch normalization, and ReLU layers.

• The bridge section consists of two sets of convolution, batch normalization, and ReLU layers.

• The bias term of all convolution layers is initialized to zero.

• Convolution layer weights in the encoder and decoder subnetworks are initialized using the 'He' weight initialization method.

Tips

• Use 'same' padding in convolution layers to maintain the same data size from input to output and enable the use of a broad set of input image sizes.

• Use patch-based approaches for seamless segmentation of large images. You can extract image patches by using the randomPatchExtractionDatastore function.

• Use 'valid' padding in convolution layers to prevent border artifacts while you use patch-based approaches for segmentation.

References

[1] Çiçek, Ö., A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger. "3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation." Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. MICCAI 2016. Lecture Notes in Computer Science. Vol. 9901, pp. 424–432. Springer, Cham.

Extended Capabilities

GPU Code Generation

Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Usage notes and limitations:

You can use the 3-D U-Net network for code generation. First, create the network using the unet3d function. Then, use the trainnet (Deep Learning Toolbox) function to train the network for segmentation. After training and evaluating the network, you can generate code for the dlnetwork object by using GPU Coder™.

Version History

Introduced in R2024a