All Outputs (2)

A stacked dense denoising–segmentation network for undersampled tomograms and knowledge transfer using synthetic tomograms (2021)
Journal Article
Bellos, D., Basham, M., Pridmore, T., & French, A. P. (2021). A stacked dense denoising–segmentation network for undersampled tomograms and knowledge transfer using synthetic tomograms. Machine Vision and Applications, 32(3), Article 75. https://doi.org/10.1007/s00138-021-01196-4

Over recent years, many approaches have been proposed for the denoising or semantic segmentation of X-ray computed tomography (CT) scans. In most cases, high-quality CT reconstructions are used; however, such reconstructions are not always available.... Read More about A stacked dense denoising–segmentation network for undersampled tomograms and knowledge transfer using synthetic tomograms.

Three Dimensional Root CT Segmentation Using Multi-Resolution Encoder-Decoder Networks (2020)
Journal Article
Soltaninejad, M., Sturrock, C. J., Griffiths, M., Pridmore, T. P., & Pound, M. P. (2020). Three Dimensional Root CT Segmentation Using Multi-Resolution Encoder-Decoder Networks. IEEE Transactions on Image Processing, 29, 6667-6679. https://doi.org/10.1109/TIP.2020.2992893

© 1992-2012 IEEE. We address the complex problem of reliably segmenting root structure from soil in X-ray Computed Tomography (CT) images. We utilise a deep learning approach, and propose a state-of-the-art multi-resolution architecture based on enco... Read More about Three Dimensional Root CT Segmentation Using Multi-Resolution Encoder-Decoder Networks.