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Use of Deep Learning for structural analysis of CT-images of soil samples

Citation

Wieland, Ralf et al. (2021), Use of Deep Learning for structural analysis of CT-images of soil samples, Dryad, Dataset, https://doi.org/10.5061/dryad.h44j0zpjf

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 (NN) 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.

Methods

The dataset is provided in DICOM format. A set of Python scripts is provided to process the data.

Funding

COST Action ES1406 (KEYSOM)

COST Action ES1406 (KEYSOM)