3D deep convolutional neural network segmentation model for precipitate and porosity identification in synchrotron X-ray tomograms.

ABSTRACT: New developments at synchrotron beamlines and the ongoing upgrades of synchrotron facilities allow the possibility to study complex structures with a much better spatial and temporal resolution than ever before. However, the downside is that the data collected are also significantly larger (more than several terabytes) than ever before, and post-processing and analyzing these data is very challenging to perform manually. This issue can be solved by employing automated methods such as machine learning, which show significantly improved performance in data processing and image segmentation than manual methods. In this work, a 3D U-net deep convolutional neural network (DCNN) model with four layers and base-8 characteristic features has been developed to segment precipitates and porosities in

SUBMITTER: Gaudez S 

PROVIDER: S-EPMC9455210 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature