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SIRT2 Inhibition by AGK2 Promotes Perinuclear Cytoskeletal Organisation and Reduces Invasiveness of MDA-MB-231 Triple-Negative Breast Cancer Cells in Confined In Vitro Models (2024)
Journal Article
Jessop, E., Young, N., Garcia-Del-Valle, B., Crusher, J. T., Obara, B., & Karakesisoglou, I. (2024). SIRT2 Inhibition by AGK2 Promotes Perinuclear Cytoskeletal Organisation and Reduces Invasiveness of MDA-MB-231 Triple-Negative Breast Cancer Cells in Confined In Vitro Models. Cells, 13(23), Article 2023. https://doi.org/10.3390/cells13232023

Triple-negative breast cancer (TNBC) is a highly aggressive breast cancer subtype characterised by the absence of targetable hormone receptors and increased metastatic rates. As nuclear softening strongly contributes to TNBC’s enhanced metastatic cap... Read More about SIRT2 Inhibition by AGK2 Promotes Perinuclear Cytoskeletal Organisation and Reduces Invasiveness of MDA-MB-231 Triple-Negative Breast Cancer Cells in Confined In Vitro Models.

Robust 3D U-Net Segmentation of Macular Holes (2021)
Presentation / Conference Contribution
Frawley, J., Willcocks, C. G., Habib, M., Geenen, C., Steel, D. H., & Obara, B. (2021, December). Robust 3D U-Net Segmentation of Macular Holes. Presented at The 29th Irish Conference on Artificial Intelligence and Cognitive Science 2021, Dublin, Republic of Ireland, December 9-10, 2021, Dublin, Ireland

Macular holes are a common eye condition which result in visual impairment. We look at the application of deep convolutional neural networks to the problem of macular hole segmentation. We use the 3D U-Net architecture as a basis and experiment with... Read More about Robust 3D U-Net Segmentation of Macular Holes.

Segmentation of macular edema datasets with small residual 3D U-Net architectures (2020)
Presentation / Conference Contribution
Frawley, J., Willcocks, C. G., Habib, M., Geenen, C., Steel, D. H., & Obara, B. (2020, October). Segmentation of macular edema datasets with small residual 3D U-Net architectures. Presented at 20th IEEE International Conference on BioInformatics and BioEngineering, Cincinnati, OH

This paper investigates the application of deep convolutional neural networks with prohibitively small datasets to the problem of macular edema segmentation. In particular, we investigate several different heavily regularized architectures. We find t... Read More about Segmentation of macular edema datasets with small residual 3D U-Net architectures.

The relationship between curvilinear structure enhancement and ridge detection approaches (2020)
Journal Article
Alhasson, H., Willcocks, C. G., Alharbi, S. S., Kasim, A., & Obara, B. (2021). The relationship between curvilinear structure enhancement and ridge detection approaches. Visual Computer, 37(8), 2263-2283. https://doi.org/10.1007/s00371-020-01985-4

Curvilinear structure detection and quantification is a large research area with many imaging applications in fields such as biology, medicine, and engineering. Curvilinear enhancement is often used as a pre-processing stage for ridge detection, but... Read More about The relationship between curvilinear structure enhancement and ridge detection approaches.

Three-dimensional data capture and analysis of intact eye lenses evidences emmetropia-associated changes and strain-dependent differences in epithelial cell organization (2020)
Journal Article
Kalligeraki, A. A., Isted, A., Pal, R., Saunter, C., Girkin, J., Jarrin, M., Uwineza, A., Obara, B., & Quinlan, R. A. (2020). Three-dimensional data capture and analysis of intact eye lenses evidences emmetropia-associated changes and strain-dependent differences in epithelial cell organization. Scientific Reports, 10, Article 16898. https://doi.org/10.1038/s41598-020-73625-9

Organ and tissue development are highly coordinated processes; lens growth and functional integration into the eye (emmetropia) is a robust example. An epithelial monolayer covers the anterior hemisphere of the lens, and its organization is the key t... Read More about Three-dimensional data capture and analysis of intact eye lenses evidences emmetropia-associated changes and strain-dependent differences in epithelial cell organization.

Macular hole morphology and measurement using an automated three dimensional image segmentation algorithm (2020)
Journal Article
Chen, Y., Nasrulloh, A., Wilson, I., Caspar, G., Maged, H., Obara, B., & Steel, D. (2020). Macular hole morphology and measurement using an automated three dimensional image segmentation algorithm. BMJ Open Ophthalmology, 5(1), Article e000404. https://doi.org/10.1136/bmjophth-2019-000404

Objective: Full-thickness macular holes (MH) are classified principally by size, which is one of the strongest predictors of anatomical and visual success. Using a three-dimensional (3D) automated image processing algorithm, we analysed optical coher... Read More about Macular hole morphology and measurement using an automated three dimensional image segmentation algorithm.

Convolutional networks for appearance based recommendation and visualisation of mascara products (2020)
Journal Article
Holder, C., Ricketts, S., & Obara, B. (2020). Convolutional networks for appearance based recommendation and visualisation of mascara products. Machine Vision and Applications, 31, Article 5. https://doi.org/10.1007/s00138-019-01053-5

In this work, we explore the problems of recommending and visualising makeup products based on images of customers. Focusing on mascara, we propose a two-stage approach that first recommends products to a new customer based on the preferences of othe... Read More about Convolutional networks for appearance based recommendation and visualisation of mascara products.

Coarse annotation refinement for segmentation of dot-matrix batchcodes (2019)
Presentation / Conference Contribution
Jia, N., Holder, C., Bonner, S., & Obara, B. (2019, December). Coarse annotation refinement for segmentation of dot-matrix batchcodes. Presented at IEEE International Conference on Machine Learning and Applications, Boca Raton, FL, USA

Deep Convolutional Neural Networks (CNN) have been extensively applied in various computer vision tasks. Although such approaches have demonstrated exceptionally high performance in various open challenges, adapting them to more specialised tasks can... Read More about Coarse annotation refinement for segmentation of dot-matrix batchcodes.

On the use of neural text generation for the task of optical character recognition (2019)
Presentation / Conference Contribution
Mohammadi, M., Jaf, S., Breckon, T., Matthews, P., McGough, A. S., Theodoropoulos, G., & Obara, B. (2019, November). On the use of neural text generation for the task of optical character recognition. Presented at 16th ACS/IEEE International Conference on Computer Systems and Applications AICCSA 2019., Abu Dhabi, UAE

Optical Character Recognition (OCR), is extraction of textual data from scanned text documents to facilitate their indexing, searching, editing and to reduce storage space. Although OCR systems have improved significantly in recent years, they still... Read More about On the use of neural text generation for the task of optical character recognition.

Temporal neighbourhood aggregation: predicting future links in temporal graphs via recurrent variational graph convolutions (2019)
Presentation / Conference Contribution
Bonner, S., Atapour-Abarghouei, A., Jackson, P., Brennan, J., Kureshi, I., Theodoropoulos, G., McGough, S., & Obara, B. (2019, December). Temporal neighbourhood aggregation: predicting future links in temporal graphs via recurrent variational graph convolutions. Presented at IEEE International Conference on Big Data (Deep Graph Learning: Methodologies and Applications), Los Angeles, CA, USA

Graphs have become a crucial way to represent large, complex and often temporal datasets across a wide range of scientific disciplines. However, when graphs are used as input to machine learning models, this rich temporal information is frequently di... Read More about Temporal neighbourhood aggregation: predicting future links in temporal graphs via recurrent variational graph convolutions.

Analysing social media as a hybrid tool to detect and interpret likely radical behavioural traits for national security (2019)
Presentation / Conference Contribution
Cardenas-Canto, P., Theodoropoulos, G., Obara, B., & Kureshi, I. (2019, December). Analysing social media as a hybrid tool to detect and interpret likely radical behavioural traits for national security. Presented at IEEE International Conference on Big Data (Human-in-the-loop Methods and Human Machine Collaboration in BigData), Los Angeles, CA, USA

The study of National Security and its associated considerations is a sensitive and complex paradigm. It encapsulates both the protection of the territorial integrity and sovereignty of a state, as well as guaranteeing the security of its population.... Read More about Analysing social media as a hybrid tool to detect and interpret likely radical behavioural traits for national security.

Exploring the semantic content of unsupervised graph embeddings: an empirical study (2019)
Journal Article
Bonner, S., Kureshi, I., Brennan, J., Theodoropoulos, G., McGough, S., & Obara, B. (2019). Exploring the semantic content of unsupervised graph embeddings: an empirical study. Data Science and Engineering, 4(3), 269-289. https://doi.org/10.1007/s41019-019-0097-5

Graph embeddings have become a key and widely used technique within the field of graph mining, proving to be successful across a broad range of domains including social, citation, transportation and biological. Unsupervised graph embedding techniques... Read More about Exploring the semantic content of unsupervised graph embeddings: an empirical study.

The multiscale top-hat tensor enables specific enhancement of curvilinear structures in 2D and 3D images (2019)
Journal Article
Alharbi, S. S., Sazak, C., Alhasson, H., Nelson, C. J., & Obara, B. (2020). The multiscale top-hat tensor enables specific enhancement of curvilinear structures in 2D and 3D images. Methods, 173, 3-15. https://doi.org/10.1016/j.ymeth.2019.05.025

Quantification and modelling of curvilinear structures in 2D and 3D images is a common challenge in a wide range of biomedical applications. Image enhancement is a crucial pre-processing step for curvilinear structure quantification. Many of the exis... Read More about The multiscale top-hat tensor enables specific enhancement of curvilinear structures in 2D and 3D images.

Sequential graph-based extraction of curvilinear structures (2019)
Journal Article
Alharbi, S. S., Willcocks, C., Jackson, P. T., Alhasson, H. F., & Obara, B. (2019). Sequential graph-based extraction of curvilinear structures. Signal, Image and Video Processing, 13(5), 941-949. https://doi.org/10.1007/s11760-019-01431-6

In this paper, a new approach is proposed to extract an ordered sequence of curvilinear structures in images, capturing the largest and most influential paths first and then progressively extracting smaller paths until a prespecified size is reached.... Read More about Sequential graph-based extraction of curvilinear structures.

Style Augmentation: Data Augmentation via Style Randomization (2019)
Presentation / Conference Contribution
Jackson, P., Atapour-Abarghouei, A., Bonner, S., Breckon, T., & Obara, B. (2019, June). Style Augmentation: Data Augmentation via Style Randomization. Presented at IEEE/CVF Conference on Computer Vision and Pattern Recognition, Deep Vision, Long Beach, CA, USA

We introduce style augmentation, a new form of data augmentation based on random style transfer, for improving the robustness of Convolutional Neural Networks (CNN) over both classification and regression based tasks. During training, style augmentat... Read More about Style Augmentation: Data Augmentation via Style Randomization.

TMIXT: A process flow for Transcribing MIXed handwritten and machine-printed Text (2018)
Presentation / Conference Contribution
Medhat, F., Mohammadi, M., Jaf, S., Willcocks, C., Breckon, T., Matthews, P., McGough, A. S., Theodoropoulos, G., & Obara, B. (2018, December). TMIXT: A process flow for Transcribing MIXed handwritten and machine-printed Text. Presented at IEEE International Conference on Big Data., Seattle, WA, USA

—Text recognition of scanned documents is usually dependent upon the type of text, being handwritten or machine-printed. Accordingly, the recognition involves prior classification of the text category, before deciding on the recognition method to be... Read More about TMIXT: A process flow for Transcribing MIXed handwritten and machine-printed Text.

Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation Learning (2018)
Presentation / Conference Contribution
Bonner, S., Brennan, J., Kureshi, I., Theodoropoulos, G., McGough, S., & Obara, B. (2018, December). Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation Learning. Presented at IEEE International Conference on Big Data., Seattle, WA, USA

Graphs are a commonly used construct for representing relationships between elements in complex high dimensional datasets. Many real-world phenomenon are dynamic in nature, meaning that any graph used to represent them is inherently temporal. However... Read More about Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation Learning.

The multiscale bowler-hat transform for blood vessel enhancement in retinal images (2018)
Journal Article
Sazak, C., Nelson, C. J., & Obara, B. (2019). The multiscale bowler-hat transform for blood vessel enhancement in retinal images. Pattern Recognition, 88, 739-750. https://doi.org/10.1016/j.patcog.2018.10.011

Enhancement, followed by segmentation, quantification and modelling of blood vessels in retinal images plays an essential role in computer-aided retinopathy diagnosis. In this paper, we introduce the bowler-hat transform method a new approach based o... Read More about The multiscale bowler-hat transform for blood vessel enhancement in retinal images.