Building U-Net architecture for biomedical image segmentation.

Building U-Net architecture for biomedical image segmentation.

The U-Net architecture is built using the Fully Convolutional Network and designed in a way that it gives better segmentation results in medical imaging. It was first designed by Olaf Ronneberger, Philipp Fischer, and Thomas Brox in 2015 to process biomedical images

The U-Net architecture is built using the Fully Convolutional Network and designed in a way that it gives better segmentation results in medical imaging. It was first designed by Olaf Ronneberger, Philipp Fischer, and Thomas Brox in 2015 to process biomedical images

Convolutional neural networks are generally used for image classification problems, but in biomedical cases, we have to localize the area of abnormality as well.

It has a “U” shape. U-Net architecture is symmetric and it’s functioning is somewhat similar to auto-encoders. It can be narrowed down into three major parts — The contracting(downsampling) path, Bottleneck, and expanding(upsampling) path. In auto-encoders, the encoder part of the neural network compresses the input into a latent space representation and then a decoder constructs the output from the compressed or encoded representation. But there is a slight difference, unlike regular encoder-decoder structures, the two parts are not decoupled. Skip connections are used to transfer fine-grained information from the low-level layers of the analysis path to the high-level layers of the synthesis path as this information is needed to generate reconstructions that have accurate fine-grained details.

Contracting Path

Contracting path is composed of four blocks where each block is made of :

  • 3x3 Convolution Layer + Activation function (relu) [Dropout is optional]

  • 3x3 Convolution Layer + Activation function (relu) [Dropout is optional]

  • 2x2 Max Pooling Layer

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Each block has two convolutional layers and one max-pooling layer. The number of channels is then switched to 64. Also, the kernel size of (3,3) is used which changes the dimensions from 572x572 → 570x570→568x568. The MaxPool2D layer then reduces the dimension to 284x284 and the process is repeated three more times until we have reached the bottleneck part.

convolutional-network python deep-learning unet

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