U-Net: Convolutional Networks for Biomedical Image Segmentation

Medium
1 hours
February 11, 2023
Youssef MENJOUR

This example aim to explain how to design, train and integrate in LabVIEW environment a biomedical Unet model architecture.

Front panel overview

Diagram global overview

This section show how the model and HAIBAL functionalities are integrated inside a LabVIEW architecture design.

The architecture is composed of 3 sequential states. (Design model, Prepare data, Training model).

During the training process, we display the results in a parallel loop.

Model design

HAIBAL architecture

We note that this model has a layer composition with convolution – batch normalization and leaky relu activation used repeatedly.

Forward Test / Train process

Display process

The model train process is “classic”, we repeat a sequence of Forward – Loss – Backward to process to the train of the model for each couple of inputs/outputs.

Model testΒ 

Testing model consist to forward and display one featureΒ  during the training (this example is not optimized, the best practice is to forward the test image after a full batch – outside of the Batch loop, we purposely did it like this to display the whole code on one page without subVI).

How to acces to this example ?

The Multi inputs outputs model train example is available in the LabVIEW find example session. Use the Keywords “Unet”Β  and launch it.

The LabVIEWΒ  U-Net: Convolutional Networks for Biomedical Image Segmentation is now available with the HAIBAL deep learning toolkit.

 

References :
https://haibal.com/support-community/topic/using-unet-for-automatic-segmentation-of-ct-lung-images/

Thanks to Peter Herrmann from Medical Center GΓΆttingen University for providing us with the architecture of this model and the dataset of 42 images.

Need help from the community

Join the GRAIPHIC community as part of a vibrant Ecosystem. This is your place to
network, ask questions, and collaborate on code with users all over the world.

Need access to the official instruction manual ?

Visit the HAIBAL knowledge base as the documentation from which you can
start from scratch and learn the advanced features

Index