Converting 2D Unet to 3D Unet

Before we look into converting 2D unet to 3D unet ,it is important we understand what Unet is and how it works.

UNet is a convolutional neural network (CNN) architecture commonly used for image segmentation tasks in the field of computer vision. It was introduced by Olaf Ronneberger, Philipp Fischer, and Thomas Brox in a 2015 research paper titled "U-Net: Convolutional Networks for Biomedical Image Segmentation." The network was initially designed for biomedical image segmentation but has since found applications in various domains beyond medical imaging.

UNet's architecture is characterized by a U-shaped structure, hence its name. It consists of an encoder-decoder network with skip connections. The architecture can be divided into two main parts:

1. Encoder: This part of the network captures the hierarchical features of the input image through a series of convolutional and pooling layers. As you move deeper into the encoder, the spatial resolution decreases while the number of feature channels increases.

2. Decoder: The decoder part of UNet performs upsampling and combines the high-level feature maps from the encoder with the upsampled feature maps to produce a segmentation mask. This helps recover the spatial information and achieve precise segmentation.

The code below shows a 2D unet architecture

Now to convert from 2D to 3D change every occurrence of 2D in your layers to 3D eg

Conv2D to Conv3D

Conv2DTranspose to Conv3DTranspose

Final result

Thanks for reading .