Use of deep learning for structural analysis of computer tomography images of soil samples
Ralf Wieland, Chinatsu Ukawa, Monika Joschko, Adrian Krolczyk, Guido Fritsch, Thomas B. Hildebrandt, Olaf Schmidt, Juliane Filser, Juan J. Jiménez
Abstract
Soil samples from several European countries were scanned using medical computer tomography (CT) device and are now available as CT images. The analysis of these samples was carried out using deep learning methods. For this purpose, a VGG16 network was trained with the CT images (X). For the annotation (y) a new method for automated annotation, 'surrogate' learning, was introduced. The generated neural networks (NNs) were subjected to a detailed analysis. Among other things, transfer learning was used to check whether the NN can also be trained to other y-values. Visually, the NN was verified using a gradient-based class activation mapping (grad-CAM) algorithm. These analyses showed that the NN was able to generalize, i.e. to capture the spatial structure of the soil sample. Possible applications of the models are discussed.