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

Source: [Fully Convolutional Deep Network Architectures for Automatic Short Glass Fiber Semantic Segmentation from CT scans](<https://www.sota2.com/research/paper/68fa653c1e209131cea093ee>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:22:52.893Z

Authors: Tomasz Konopczy\\'nski, Danish Rathore, Jitendra Rathore, Thorben Kr\\"oger, Lei Zheng, Christoph S. Garbe, Simone Carmignato, J\\"urgen Hesser

Published: 2019-01-04T17:00:54.000Z

## Abstract

We present the first attempt to perform short glass fiber semantic segmentation from X-ray computed tomography volumetric datasets at medium (3.9 {\\mu}m isotropic) and low (8.3 {\\mu}m 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1901.01211>)

[PDF](<https://arxiv.org/pdf/1901.01211.pdf>)