The projects and published results in this group deal with prostate cancer.

We investigate the use of multi-parametric magnetic resonance imaging and spectroscopy for prostate and brain cancer detection, focused on early-stage cancer diagnosis, anatomical segmentation, region of interest identification, machine learning and characterising biomarkers.

People

• Sophie Shermer, Physics, Swansea University
• Rhodri Evans, Institute of Life Science, Swansea University
• Asmail Muftah, School of Computer Science and Informatics, Cardiff University
• Frank C Langbein, School of Computer Science and Informatics, Cardiff University; langbein.org
• Jing Wu, School of Computer Science and Informatics, Cardiff University
• Kirill Sidorov, School of Computer Science and Informatics, Cardiff University
• Alexia Zoumpoulaki, School of Computer Science and Informatics, Cardiff University
• Andrew Nightingale, School of Computer Science and Informatics, Cardiff University
• Daniel Morgan, School of Computer Science and Informatics, Cardiff University
• Xianfang Sun, School of Computer Science and Informatics, Cardiff University
• Yipeng Qin, School of Computer Science and Informatics, Cardiff University
• Ebtihal J Alwadee, School of Computer Science and Informatics, Cardiff University
• Ogechuwku Ukwandu, School of Computer Science and Informatics, Cardiff University
• Alia Abdelmoty, School of Computer Science and Informatics, Cardiff University
• Mihail Bors, School of Computer Science and Informatics, Cardiff University
• Anthony Rees, Swansea University

Publications

• Asmail Muftah, SM Shermer, Frank C Langbein. Texture Feature Analysis for Classification of Early-Stage Prostate Cancer in mpMRI. Proc AI in Healthcare (AIiH), Swansea, UK, September 2024. PDF(https://ca.qyber.dev/paper-aiih2024/aiih2024.pdf) arXiv:2406.15571(https://arxiv.org/abs/2406.15571)
• E Alwadee, X Sun, Y Qin, FC Langbein. Assessing and Enhancing the Robustness of Brain Tumor Segmentation using a Probabilistic Deep Learning Architecture. Proc 2024 ISMRM & ISMRT Annual Meeting & Exhibition, Singapore, May 2024. PDF(https://ca.qyber.dev/paper-ismrm2024/ismrm2024.pdf) Slides:PDF(https://ca.qyber.dev/paper-ismrm2024/slides.pdf) Video(https://youtu.be/GS7cMQyv1RE)
• E Alwadee, X Sun, Y Qin, FC Langbein. LATUP-Net: A Lightweight 3D Attention U-Net with Parallel Convolutions for Brain Tumor Segmentation. Preprint, 2024. arxiv:2404.05911(https://arxiv.org/abs/2404.05911) PDF(https://ca.qyber.dev/paper-latup-net/latupnet.pdf)
• I Papadopoulos, J Phillips, R Evans, N Fenn, S Shermer. Evaluation of Diffusion Weighted Imaging in the Context of Multi-Parametric MRI of the Prostate in the assessment of suspected low volume prostatic carcinoma. Magnetic Resonance Imaging, 47, 131-136, 2018. arxiv:1711.09703(https://arXiv.org/abs/1711.09703) DOI:10.1016/j.mri.2017.11.014(https://doi.org/10.1016/j.mri.2017.11.014)
• ZG Portakal, S Shermer, E Spezi, T Perrett, J Phillips. Effect of Noise Floor Suppression on Diffusion Kurtosis for Prostate Brachytherapy. In: Radiotherapy and Oncology, 123, S938-S938, 2017. PDF:poster(https://www.postersessiononline.eu/173580348_eu/congresos/ESTRO36/aula/-EP_1711_ESTRO36.pdf)
• S Shermer, I Papadopoulos, G Portakal, J Phillips, R Evans. Multimodal magnetic resonance imaging and spectroscopy for prostate cancer screening and staging. Physica Medica, 32, Suppl. 3, 324, 2016. DOI:10.1016/j.ejmp.2016.07.217(https://doi.org/10.1016/j.ejmp.2016.07.217)
• ZG Portakal, JW Phillips, CE Richards, E Spezi, T Perrett, DG Lewis, Z Yegingil. EP-1878: Feasibility of gel phantoms in MRI for the assessment of kurtosis for prostate brachytherapy. J Radiotherapy and Oncology, 119, S887-S888, 2016. PDF:paper(https://core.ac.uk/download/pdf/82160462.pdf)

Presentations

• S Shermer. Quantitative MRI and Spectroscopy: from quantification of chemicals in the brain to diagnostic tools for prostate cancer. Healthcare Technologies Research Group seminar at the School of Computer Science and Informatics, Cardiff University, 16/2/2022. YouTube:video(https://youtu.be/b0UtiSOnhTk)

PhDs

• I Papadopoulos. Multi-parametric MRI of Prostate Cancer: Assessmnet of Spectroscopy, Diffusion, Dynamic Contrast and Relaxometry for Active Surveillance and Staging. PhD thesis, Swansea University, 2018. DOI:10.23889/Suthesis.51146(http://dx.doi.org/10.23889/Suthesis.51146)
• AAS Muftah. Machine Learning and Image Analysis for Prostate Cancer Detection. School of Computer Science and Informatics, Cardiff University.
• EJ Alwadee. Novel Adaptive Down-sample Neural Network Classification for Detecting Brain Tumour from MRI Brain Images. School of Computer Science and Informatics, Cardiff University.
• O Ukwandu. Developing A Robust Artificial Intelligence System for Precision Diagnosis of Prostate Cancer Using Magnetic Resonance Imaging. School of Computer Science and Informatics, Cardiff University.

Reports

• A Nightingale. Delineating regions of interest in MRI/S prostate scans for cancer diagnosis. MSc Computing dissertation, Cardiff University. 2020.
• D Morgan. Delineating regions of interest in MRI prostate scans. BSc Computer Science project, Cardiff University, 2019. Archive(https://pats.cs.cf.ac.uk/!archive_desc?p=1156)

Code

• BCa - Brain Cancer Segmentation Python Package - Code for brain cancer segmentation.
• PCaNet - Prostate cancer segmentation and classification with machine learning.
• QDicom Utilities - Utilities to deal with dicom files and data repositories.
• MRI Delineator - A tool for delineating polygons upon stacks of MRI slices.

Data

• BCa Segmentation Results: LATUPNet
• Results - PCaNet Models - Classification - Trained PCANet models for patch classification.
• Swansea University PCa data set.

Workshops

• PCa Workshop, Swansea University, 15th November 2018.
• VLunch Seminar, Cardiff University, August 2018: Rhodri Evans, Prostate Cancer Diagnosis.

Locations

The wiki is written and maintained on Qyber\black at https://qyber.black/ca/info-cancer/

Contact

For any general enquiries relating to this project group, send an e-mail.

License

This wiki is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

The publications, code, data, etc. may be under a different license. Check the relevant information provided with these products.