Class Quicknat

monai/networks/nets/quicknat.py:280–441

Model for "Quick segmentation of NeuroAnaTomy (QuickNAT) based on a deep fully convolutional neural network. Refer to: "QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy by Abhijit Guha Roya, Sailesh Conjetib, Nassir Navabb, Christian Wachingera

class Quicknat(nn.Module):
    """
    Model for "Quick segmentation of NeuroAnaTomy (QuickNAT) based on a deep fully convolutional neural network.
    Refer to: "QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy by
    Abhijit Guha Roya, Sailesh Conjetib, Nassir Navabb, Christian Wachingera"

    QuickNAT has an encoder/decoder like 2D F-CNN architecture with 4 encoders and 4 decoders separated by a bottleneck layer.
    The final layer is a classifier block with softmax.
    The architecture includes skip connections between all encoder and decoder blocks of the same spatial resolution,
    similar to the U-Net architecture.
    All Encoder and Decoder consist of three convolutional layers all with a Batch Normalization and ReLU.
    The first two convolutional layers are followed by a concatenation layer that concatenates
    the input feature map with outputs of the current and previous convolutional blocks.
    The kernel size of the first two convolutional layers is 5*5, the third convolutional layer has a kernel size of 1*1.

    Data in the encode path is downsampled using max pooling layers instead of upsamling like UNet and in the decode path
    upsampled using max un-pooling layers instead of transpose convolutions.
    The pooling is done at the beginning of the block and the unpool afterwards.
    The indices of the max pooling in the Encoder are forwarded through the layer to be available to the corresponding Decoder.

    The bottleneck block consists of a 5 * 5 convolutional layer and a batch normalization layer
    to separate the encoder and decoder part of the network,
    restricting information flow between the encoder and decoder.

    The output feature map from the last decoder block is passed to the classifier block,
    which is a convolutional layer with 1 * 1 kernel size that maps the input to an N channel feature map,
    where N is the number of segmentation classes.

    To further explain this consider the first example network given below. This network has 3 layers with strides
    of 2 for each of the middle layers (the last layer is the bottom connection which does not down/up sample). Input
    data to this network is immediately reduced in the spatial dimensions by a factor of 2 by the first convolution of
    the residual unit defining the first layer of the encode part. The last layer of the decode part will upsample its
    input (data from the previous layer concatenated with data from the skip connection) in the first convolution. this
    ensures the final output of the network has the same shape as the input.

    The original QuickNAT implementation included a `enable_test_dropout()` mechanism for uncertainty estimation during
    testing. As the dropout layers are the only stochastic components of this network calling the train() method instead
    of eval() in testing or inference has the same effect.

    Args:
        num_classes: number of classes to segmentate (output channels).
        num_channels: number of input channels.
        num_filters: number of output channels for each convolutional layer in a Dense Block.
        kernel_size: size of the kernel of each convolutional layer in a Dense Block.
        kernel_c: convolution kernel size of classifier block kernel.
        stride_convolution: convolution stride. Defaults to 1.
        pool: kernel size of the pooling layer,
        stride_pool: stride for the pooling layer.
        se_block: Squeeze and Excite block type to be included, defaults to None. Valid options : NONE, CSE, SSE, CSSE,
        droup_out: dropout ratio. Defaults to no dropout.
        act: activation type and arguments. Defaults to PReLU.
        norm: feature normalization type and arguments. Defaults to instance norm.
        adn_ordering: a string representing the ordering of activation (A), normalization (N), and dropout (D).
            Defaults to "NA". See also: :py:class:`monai.networks.blocks.ADN`.

    Examples::

        from monai.networks.nets import QuickNAT