# Fully Convolutional Deep Network Architectures for Automatic Short Glass Fiber Semantic Segmentation from CT scans

arXiv: https://arxiv.org/abs/1901.01211 · HTML: https://mlsanity.com/paper/1901.01211/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2019.

## Built on by (most cited first, 1 of 1)

- [Computer Vision on X-ray Data in Industrial Production and Security Applications: A Comprehensive Survey](https://mlsanity.com/paper/2211.05565/) (2022)

## Abstract (arXiv)

We present the first attempt to perform short glass fiber semantic segmentation from X-ray computed tomography volumetric datasets at medium (3.9 μm isotropic) and low (8.3 μm isotropic) resolution using deep learning architectures. We performed experiments on both synthetic and real CT scans and evaluated deep fully convolutional architectures with both 2D and 3D kernels. Our artificial neural networks outperform existing methods at both medium and low resolution scans.