Outputs (24)

Classification of osteoarthritic and healthy cartilage using deep learning with Raman spectra (2024)
Journal Article
Kok, Y. E., Crisford, A., Parkes, A., Venkateswaran, S., Oreffo, R., Mahajan, S., & Pound, M. (2024). Classification of osteoarthritic and healthy cartilage using deep learning with Raman spectra. Scientific Reports, 14(1), Article 15902. https://doi.org/10.1038/s41598-024-66857-6

Raman spectroscopy is a rapid method for analysing the molecular composition of biological material. However, noise contamination in the spectral data necessitates careful pre-processing prior to analysis. Here we propose an end-to-end Convolutional... Read More about Classification of osteoarthritic and healthy cartilage using deep learning with Raman spectra.

Root architecture and leaf photosynthesis traits and associations with nitrogen-use efficiency in landrace-derived lines in wheat (2022)
Journal Article
Kareem, S. H., Hawkesford, M. J., DeSilva, J., Weerasinghe, M., Wells, D. M., Pound, M. P., …Foulkes, M. J. (2022). Root architecture and leaf photosynthesis traits and associations with nitrogen-use efficiency in landrace-derived lines in wheat. European Journal of Agronomy, 140, Article 126603. https://doi.org/10.1016/j.eja.2022.126603

Root system architecture (RSA) is important in optimizing the use of nitrogen. High-throughput phenotyping techniques may be used to study root system architecture traits under controlled environments. A root phenotyping platform, consisting of germi... Read More about Root architecture and leaf photosynthesis traits and associations with nitrogen-use efficiency in landrace-derived lines in wheat.

Identification of QTL and underlying genes for root system architecture associated with nitrate nutrition in hexaploid wheat (2022)
Journal Article
GRIFFITHS, M., ATKINSON, J. A., Gardiner, L. J., SWARUP, R., POUND, M. P., WILSON, M. H., …WELLS, D. M. (2022). Identification of QTL and underlying genes for root system architecture associated with nitrate nutrition in hexaploid wheat. Journal of Integrative Agriculture, 21(4), 917-932. https://doi.org/10.1016/s2095-3119%2821%2963700-0

The root system architecture (RSA) of a crop has a profound effect on the uptake of nutrients and consequently the potential yield. However, little is known about the genetic basis of RSA and resource adaptive responses in wheat (Triticum aestivum L.... Read More about Identification of QTL and underlying genes for root system architecture associated with nitrate nutrition in hexaploid wheat.

A fusion spatial attention approach for few-shot learning (2021)
Journal Article
Song, H., Deng, B., Pound, M., Özcan, E., & Triguero, I. (2022). A fusion spatial attention approach for few-shot learning. Information Fusion, 81, 187-202. https://doi.org/10.1016/j.inffus.2021.11.019

Few-shot learning is a challenging problem in computer vision that aims to learn a new visual concept from very limited data. A core issue is that there is a large amount of uncertainty introduced by the small training set. For example, the few image... Read More about A fusion spatial attention approach for few-shot learning.

RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures (2019)
Journal Article
Yasrab, R., Atkinson, J. A., Wells, D. M., French, A. P., Pridmore, T. P., & Pound, M. P. (2019). RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures. GigaScience, 8(11), Article giz123. https://doi.org/10.1093/gigascience/giz123

BACKGROUND: In recent years quantitative analysis of root growth has become increasingly important as a way to explore the influence of abiotic stress such as high temperature and drought on a plant's ability to take up water and nutrients. Segmentat... Read More about RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures.

Supporting data for "RootNav 2.0: Deep Learning for Automatic Navigation of Complex Plant Root Architectures" (2019)
Data
French, A., Wells, D. M., Atkinson, J., Pound, M., Yasrab, R., & Pridmore, T. (2019). Supporting data for "RootNav 2.0: Deep Learning for Automatic Navigation of Complex Plant Root Architectures". [Data]. https://doi.org/10.5524/100651

We present a new image analysis approach that provides fully-automatic extraction of complex root system architectures from a range of plant species in varied imaging setups. Driven by modern deep-learning approaches, RootNav 2.0 replaces previously... Read More about Supporting data for "RootNav 2.0: Deep Learning for Automatic Navigation of Complex Plant Root Architectures".

Recovering Wind-induced Plant motion in Dense Field Environments via Deep Learning and Multiple Object Tracking (2019)
Journal Article
Gibbs, J. A., Burgess, A. J., Pound, M. P., Pridmore, T. P., & Murchie, E. H. (2019). Recovering Wind-induced Plant motion in Dense Field Environments via Deep Learning and Multiple Object Tracking. Plant Physiology, 181, 28-42. https://doi.org/10.1104/pp.19.00141

Understanding the relationships between local environmental conditions and plant structure and function is critical for both fundamental science and for improving the performance of crops in field settings. Wind-induced plant motion is important in m... Read More about Recovering Wind-induced Plant motion in Dense Field Environments via Deep Learning and Multiple Object Tracking.

Active Vision and Surface Reconstruction for 3D Plant Shoot Modelling (2019)
Journal Article
Gibbs, J., French, A., Murchie, E., Wells, D., Pound, M., & Pridmore, T. (2020). Active Vision and Surface Reconstruction for 3D Plant Shoot Modelling. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(6), 1907-1917. https://doi.org/10.1109/TCBB.2019.2896908

Plant phenotyping is the quantitative description of a plant’s physiological, biochemical and anatomical status which can be used in trait selection and helps to provide mechanisms to link underlying genetics with yield. Here, an active vision- based... Read More about Active Vision and Surface Reconstruction for 3D Plant Shoot Modelling.

Identification of nitrogen-dependent QTL and underlying genes for root system architecture in hexaploid wheat (2019)
Other
Griffiths, M., Atkinson, J. A., Gardiner, L.-J., Swarup, R., Pound, M. P., Wilson, M. H., …Wells, D. M. (2019). Identification of nitrogen-dependent QTL and underlying genes for root system architecture in hexaploid wheat

The root system architecture (RSA) of a crop has a profound effect on the uptake of nutrients and consequently the potential yield. However, little is known about the genetic basis of RSA and resource dependent response in wheat (Triticum aestivum L.... Read More about Identification of nitrogen-dependent QTL and underlying genes for root system architecture in hexaploid wheat.

Deep Hourglass for Brain Tumor Segmentation (2019)
Book Chapter
Benson, E., Pound, M. P., French, A. P., Jackson, A. S., & Pridmore, T. P. (2019). Deep Hourglass for Brain Tumor Segmentation. In BrainLes 2018: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (419-428). Springer. https://doi.org/10.1007/978-3-030-11726-9_37

The segmentation of a brain tumour in an MRI scan is a challenging task, in this paper we present our results for this problem via the BraTS 2018 challenge, consisting of 210 high grade glioma (HGG) and 75 low grade glioma (LGG) volumes for training.... Read More about Deep Hourglass for Brain Tumor Segmentation.