U-Net: Convolutional Networks for Biomedical Image Segmentation

1 hours
February 11, 2023

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 :

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

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