Automatic segmentation for porous bread samples using deep learning

What is it about?

This paper aims to develop a new method for training a deep neural network using synthetic data. The trained model will be used to automatically segment micro-CT images of bread dough collected at the Australian synchrotron.

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Why is it important?

This automated model for data segmentation would alleviate the time-consuming aspects of experimental workflow and would open the door to perform 4D characterization experiments with smaller time steps.