English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

X-ray Computed Tomography (XCT) techniques have evolved to a point thathigh-resolution data can be acquired so fast that classic segmentation methodsare prohibitively cumbersome, demanding automated data pipelines capable ofdealing with non-trivial 3D images. Deep learning has demonstrated success inmany image processing tasks, including material science applications, showing apromising alternative for a humanfree segmentation pipeline. In this paper amodular interpretation of UNet (Modular U-Net) is proposed and trained tosegment 3D tomography images of a three-phased glass fiber-reinforced Polyamide66. We compare 2D and 3D versions of our model, finding that the former isslightly better than the latter. We observe that human-comparable results canbe achievied even with only 10 annotated layers and using a shallow U-Netyields better results than a deeper one. As a consequence, Neural Network (NN)show indeed a promising venue to automate XCT data processing pipelines needingno human, adhoc intervention.

A modular U-Net for automated segmentation of X-ray tomography images in composite materials