All Outputs (37)

Addressing multiple salient object detection via dual-space long-range dependencies (2023)
Journal Article
Deng, B., French, A. P., & Pound, M. P. (2023). Addressing multiple salient object detection via dual-space long-range dependencies. Computer Vision and Image Understanding, 235, Article 103776. https://doi.org/10.1016/j.cviu.2023.103776

Salient object detection plays an important role in many downstream tasks. However, complex real-world scenes with varying scales and numbers of salient objects still pose a challenge. In this paper, we directly address the problem of detecting multi... Read More about Addressing multiple salient object detection via dual-space long-range dependencies.

Semantic Segmentation of Spontaneous Intracerebral Hemorrhage, Intraventricular Hemorrhage, and Associated Edema on CT Images Using Deep Learning (2022)
Journal Article
Kok, Y. E., Pszczolkowski, S., Law, Z. K., Ali, A., Krishnan, K., Bath, P., …French, A. P. (2022). Semantic Segmentation of Spontaneous Intracerebral Hemorrhage, Intraventricular Hemorrhage, and Associated Edema on CT Images Using Deep Learning. Radiology: Artificial Intelligence, 4(6), https://doi.org/10.1148/ryai.220096

This study evaluated deep learning algorithms for semantic segmentation and quantification of intracerebral hemorrhage (ICH), perihematomal edema (PHE), and intraventricular hemorrhage (IVH) on noncontrast CT scans of patients with spontaneous ICH. M... Read More about Semantic Segmentation of Spontaneous Intracerebral Hemorrhage, Intraventricular Hemorrhage, and Associated Edema on CT Images Using Deep Learning.

Temporal refinement of 3D CNN semantic segmentations on 4D time-series of undersampled tomograms using hidden Markov models (2021)
Journal Article
Bellos, D., Basham, M., Pridmore, T., & French, A. P. (2021). Temporal refinement of 3D CNN semantic segmentations on 4D time-series of undersampled tomograms using hidden Markov models. Scientific Reports, 11(1), Article 23279. https://doi.org/10.1038/s41598-021-02466-x

Recently, several convolutional neural networks have been proposed not only for 2D images, but also for 3D and 4D volume segmentation. Nevertheless, due to the large data size of the latter, acquiring a sufficient amount of training annotations is mu... Read More about Temporal refinement of 3D CNN semantic segmentations on 4D time-series of undersampled tomograms using hidden Markov models.

Domain Adaptation of Synthetic Images for Wheat Head Detection (2021)
Journal Article
Hartley, Z. K., & French, A. P. (2021). Domain Adaptation of Synthetic Images for Wheat Head Detection. Plants, 10(12), Article 2633. https://doi.org/10.3390/plants10122633

Wheat head detection is a core computer vision problem related to plant phenotyping that in recent years has seen increased interest as large-scale datasets have been made available for use in research. In deep learning problems with limited training... Read More about Domain Adaptation of Synthetic Images for Wheat Head Detection.

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.

Volumetric Segmentation of Cell Cycle Markers in Confocal Images Using Machine Learning and Deep Learning (2020)
Journal Article
Khan, F. A., Voß, U., Pound, M. P., & French, A. P. (2020). Volumetric Segmentation of Cell Cycle Markers in Confocal Images Using Machine Learning and Deep Learning. Frontiers in Plant Science, 11, Article 1275. https://doi.org/10.3389/fpls.2020.01275

© Copyright © 2020 Khan, Voß, Pound and French. Understanding plant growth processes is important for many aspects of biology and food security. Automating the observations of plant development—a process referred to as plant phenotyping—is increasing... Read More about Volumetric Segmentation of Cell Cycle Markers in Confocal Images Using Machine Learning and Deep Learning.

CNN-Based Cassava Storage Root Counting Using Real and Synthetic Images (2019)
Journal Article
Atanbori, J., Montoya, M., Selvaraj, M., French, A. P., & Pridmore, T. P. (2019). CNN-Based Cassava Storage Root Counting Using Real and Synthetic Images. Frontiers in Plant Science, 10, Article 1516. https://doi.org/10.3389/fpls.2019.01516

Cassava roots are complex structures comprising several distinct types of root. The number and size of the storage roots are two potential phenotypic traits reflecting crop yield and quality. Counting and measuring the size of cassava storage roots a... Read More about CNN-Based Cassava Storage Root Counting Using Real and Synthetic Images.

A low-cost aeroponic phenotyping system for storage root development: Unravelling the below-ground secrets of cassava (Manihot esculenta) (2019)
Journal Article
Selvaraj, M. G., Montoya-P, M. E., Atanbori, J., French, A. P., & Pridmore, T. (2019). A low-cost aeroponic phenotyping system for storage root development: Unravelling the below-ground secrets of cassava (Manihot esculenta). Plant Methods, 15(1), Article 131. https://doi.org/10.1186/s13007-019-0517-6

© 2019 The Author(s). Background: Root and tuber crops are becoming more important for their high source of carbohydrates, next to cereals. Despite their commercial impact, there are significant knowledge gaps about the environmental and inherent reg... Read More about A low-cost aeroponic phenotyping system for storage root development: Unravelling the below-ground secrets of cassava (Manihot esculenta).

A convolutional neural network for fast upsampling of undersampled tomograms in X-ray CT time-series using a representative highly sampled tomogram (2019)
Journal Article
Bellos, D., Basham, M., Pridmore, T., & French, A. P. (2019). A convolutional neural network for fast upsampling of undersampled tomograms in X-ray CT time-series using a representative highly sampled tomogram. Journal of Synchrotron Radiation, 26(3), 839-853. https://doi.org/10.1107/s1600577519003448

We designed a convolutional neural network to quickly and accurately upscale the sinograms of x-ray tomograms captured with a low number of projections; effectively increasing the number of projections. This is particularly useful for tomograms that... Read More about A convolutional neural network for fast upsampling of undersampled tomograms in X-ray CT time-series using a representative highly sampled tomogram.

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.

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.

Plant phenotyping: an active vision cell for three-dimensional plant shoot reconstruction (2018)
Journal Article
Gibbs, J., Pound, M., French, A. P., Wells, D. M., Murchie, E., & Pridmore, T. (2018). Plant phenotyping: an active vision cell for three-dimensional plant shoot reconstruction. Plant Physiology, 178(2), 524-534. https://doi.org/10.1104/pp.18.00664

© 2018 American Society of Plant Biologists. All rights reserved. Three-dimensional (3D) computer-generated models of plants are urgently needed to support both phenotyping and simulation-based studies such as photosynthesis modeling. However, the co... Read More about Plant phenotyping: an active vision cell for three-dimensional plant shoot reconstruction.

Enhancing supervised classifications with metamorphic relations (2018)
Presentation / Conference Contribution
Xu, L., Towey, D., French, A. P., Benford, S., Zhou, Z. Q., & Chen, T. Y. (2018). Enhancing supervised classifications with metamorphic relations. In MET '18: Proceedings of the 3rd International Workshop on Metamorphic Testing (46-53). https://doi.org/10.1145/3193977.3193978

We report on a novel use of metamorphic relations (MRs) in machine learning: instead of conducting metamorphic testing, we use MRs for the augmentation of the machine learning algorithms themselves. In particular, we report on how MRs can enable enha... Read More about Enhancing supervised classifications with metamorphic relations.

Root gravitropism: quantification, challenges, and solutions (2018)
Journal Article
Muller, L., Bennett, M. J., French, A., Wells, D. M., & Swarup, R. (2018). Root gravitropism: quantification, challenges, and solutions. Methods in Molecular Biology, 1761, 103-112. https://doi.org/10.1007/978-1-4939-7747-5_8

© 2018, Springer Science+Business Media, LLC. Better understanding of root traits such as root angle and root gravitropism will be crucial for development of crops with improved resource use efficiency. This chapter describes a high-throughput, autom... Read More about Root gravitropism: quantification, challenges, and solutions.

Recognizing the Presence of Hidden Visual Markers in Digital Images (2017)
Presentation / Conference Contribution
Xu, L., French, A. P., Towey, D., & Benford, S. (2017). Recognizing the Presence of Hidden Visual Markers in Digital Images. In Thematic Workshops '17: Proceedings of the on Thematic Workshops of ACM Multimedia 2017 (210-218). https://doi.org/10.1145/3126686.3126761

As the promise of Virtual and Augmented Reality (VR and AR) becomes more realistic, an interesting aspect of our enhanced living environment includes the availability — indeed the potential ubiquity — of scannable markers. Such markers could represen... Read More about Recognizing the Presence of Hidden Visual Markers in Digital Images.

Deep learning for multi-task plant phenotyping (2017)
Presentation / Conference Contribution
Pound, M. P., Atkinson, J. A., Wells, D. M., Pridmore, T. P., & French, A. P. (2017). Deep learning for multi-task plant phenotyping. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017 (2055-2063). https://doi.org/10.1109/ICCVW.2017.241

Plant phenotyping has continued to pose a challenge to computer vision for many years. There is a particular demand to accurately quantify images of crops, and the natural variability and structure of these plants presents unique difficulties. Recent... Read More about Deep learning for multi-task plant phenotyping.

Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress (2017)
Journal Article
Lowe, A., Harrison, N., & French, A. P. (2017). Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress. Plant Methods, 13, Article 80. https://doi.org/10.1186/s13007-017-0233-z

This review explores how imaging techniques are being developed with a focus on deployment for crop monitoring methods. Imaging applications are discussed in relation to both field and glasshouse-based plants, and techniques are sectioned into ‘healt... Read More about Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress.

Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench (2017)
Journal Article
Darrow, M. C., Luengo, I., Basham, M., Spink, M. C., Irvine, S., French, A. P., …Duke, E. M. (2017). Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench. Journal of Visualized Experiments, Article e56162. https://doi.org/10.3791/56162

Segmentation is the process of isolating specific regions or objects within an imaged volume, so that further study can be undertaken on these areas of interest. When considering the analysis of complex biological systems, the segmentation of three-d... Read More about Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench.

The Microphenotron: a robotic miniaturized plant phenotyping platform with diverse applications in chemical biology (2017)
Journal Article
Burrell, T., Fozard, S., Holroyd, G. H., French, A. P., Pound, M. P., Bigley, C. J., …Forde, B. G. (2017). The Microphenotron: a robotic miniaturized plant phenotyping platform with diverse applications in chemical biology. Plant Methods, 13(1), Article 10. https://doi.org/10.1186/s13007-017-0158-6

Background
Chemical genetics provides a powerful alternative to conventional genetics for understanding gene function. However, its application to plants has been limited by the lack of a technology that allows detailed phenotyping of whole-seedling... Read More about The Microphenotron: a robotic miniaturized plant phenotyping platform with diverse applications in chemical biology.

SuRVoS: Super-Region Volume Segmentation workbench (2017)
Journal Article
Luengo, I., Darrow, M. C., Spink, M. C., Sun, Y., Dai, W., He, C. Y., …French, A. P. (2017). SuRVoS: Super-Region Volume Segmentation workbench. Journal of Structural Biology, 198(1), 43-53. https://doi.org/10.1016/j.jsb.2017.02.007

Segmentation of biological volumes is a crucial step needed to fully analyse their scientific content. Not having access to convenient tools with which to segment or annotate the data means many biological volumes remain under-utilised. Automatic seg... Read More about SuRVoS: Super-Region Volume Segmentation workbench.