DeepAngle: Fast calculation of contact angles in tomography images using deep learning
This provides a rapid, low-cost solution for processing large tomography data in porous materials research, though it is incremental as it applies an existing deep learning approach to a specific domain problem.
The authors tackled the problem of accurately and efficiently measuring contact angles in 3D tomography images of porous materials, which is computationally intensive with traditional methods. They developed DeepAngle, a deep learning-based method that improved r-squared by 5 to 16% and reduced computational cost 20 times compared to direct measurement techniques.
DeepAngle is a machine learning-based method to determine the contact angles of different phases in the tomography images of porous materials. Measurement of angles in 3--D needs to be done within the surface perpendicular to the angle planes, and it could become inaccurate when dealing with the discretized space of the image voxels. A computationally intensive solution is to correlate and vectorize all surfaces using an adaptable grid, and then measure the angles within the desired planes. On the contrary, the present study provides a rapid and low-cost technique powered by deep learning to estimate the interfacial angles directly from images. DeepAngle is tested on both synthetic and realistic images against the direct measurement technique and found to improve the r-squared by 5 to 16% while lowering the computational cost 20 times. This rapid method is especially applicable for processing large tomography data and time-resolved images, which is computationally intensive. The developed code and the dataset are available at an open repository on GitHub (https://www.github.com/ArashRabbani/DeepAngle).