Segmenting Low-Contrast XCTs of Concretes: An Unsupervised Approach

TL;DR

This paper introduces an unsupervised method for training CNNs to segment low-contrast XCT images of concrete, using self-annotation and superpixel algorithms to overcome the lack of labeled data.

Contribution

It presents a novel unsupervised training approach for CNN-based segmentation of XCT scans, leveraging self-annotation and superpixels to handle low-contrast concrete images.

Findings

Effective segmentation of low-contrast XCT images achieved

Unsupervised training reduces need for labeled data

Potential for improved concrete analysis applications

Abstract

This work tests a self-annotation-based unsupervised methodology for training a convolutional neural network (CNN) model for semantic segmentation of X-ray computed tomography (XCT) scans of concretes. Concrete poses a unique challenge for XCT imaging due to similar X-ray attenuation coefficients of aggregates and mortar, resulting in low-contrast between the two phases in the ensuing images. While CNN-based models are a proven technique for semantic segmentation in such challenging cases, they typically require labeled training data, which is often unavailable for new datasets or are costly to obtain. To counter that limitation, a self-annotation technique is used here which leverages superpixel algorithms to identify perceptually similar local regions in an image and relates them to the global context in the image by utilizing the receptive field of a CNN-based model. This enables the model to learn a global-local relationship in the images and enables identification of semantically similar structures. We therefore present the performance of the unsupervised training methodology on our XCT datasets and discuss potential avenues for further improvements.