Biomedical Image Analysis
About our research
The Oxford Biomedical Image Analysis (BioMedIA) cluster is an academic group of faculty, postdoctoral researchers, software engineers, support staff and research students that develop medical imaging and image analysis algorithms and tools that aim to improve image-based diagnostics, therapies and monitoring technologies in hospitals and primary care, and for both western world and global health care settings. The breadth of our interests span all major clinical imaging modalities (particularly magnetic resonance imaging, ultrasound imaging, endoscopy imaging, histopathology), multi-modal imaging (imaging and audio, imaging and gaze tracking, imaging and electrocardiogram) and microscopy. We conduct inter-disciplinary translational research with clinical partners in Oxford and elsewhere in the UK and overseas in clinical domains of application ranging from fetal development, to oncology, respiratory medicine, gastroenterology, neurology and cardiovascular medicine. We are well-connected within Oxford with other major research initiatives such as the Oxford Biomedical Research Centre and the CRUK Oxford Centre.
We have a strong tradition of postgraduate research training and welcome enquiries from prospective research students. Current research students are enrolled on one of a number of programmes including: the DPhil in Engineering Science, the Centre for Doctoral Training in Health Data Sciences, and the National Institutes of Health Oxford-Cambridge Scholars Programme.
Our research areas
Blood Flow & Metabolism Bioimaging
Our research focuses on developing translatable clinical imaging techniques for measuring blood flow and metabolism in a broad range of diseases from dementia to cancer. Primarily using MRI and often incorporating respiratory challenges we aim to determine the links between the parameters we can measure and the physiology we wish to.
Pulmonary and Cardiovascular Imaging
Our research focuses on the development of biomedical image analysis algorithms, with an emphasis on the combination with computational models, and applications on cardiac and pulmonary medicine.
Machine Learning for Medical Imaging
We conduct advanced research on Machine Learning (ML) and Artificial Intelligence (AI) to develop the next generation of methods for medical image analysis. We push the boundaries of this technology to create more accurate, reliable and explainable methods. Our aim is to enable safe use of ML/AI in healthcare, and use the technology to tackle any challenging clinical question towards improving diagnosis and treatment.
Fetal Ultrasound Analysis
Our research focuses on machine learning applied to fetal ultrasound analysis. We are measuring at scale how operators scan, by recording eye and probe tracking, ultrasound video and audio in the clinic. This enables us to develop machine learning models to characterise operator skill, and to build multi-modal ultrasound assistive technologies to support non-experts to scan. We are also developing low-cost ultrasound technologies empowered by deep learning based algorithms for pregnancy risk assessment in collaboration with partners in Africa and India.
Motion in Bioimage Analysis
Motion is a massive barrier in cancer imaging hampering developments in cancer research and treatment. My research work has comprised the development of accurate, thus complex and realistic but still computationally efficient, models of organ motion, and has established a solid foundation to reliable quantitative cancer image analysis.
Microscopic Bioimage Analysis
The aim of our research is to enhance our understanding of complex biological processes through the analysis of image data that has been acquired at the microscopic scale. We develop algorithms and methods that enable the quantification of a broad range of phenotypical alterations, the precise localisation of signalling events, and the ability to correlate such events in the context of the biological specimen.
Multimodal Reconstruction of Digital Anatomy
Our work is primarily focused on Multimodal Reconstruction and Analysis of Digital Anatomy for Real-Time Clinical Interventions; in particular Segmentation, Landmark Detection, Tracking, Motion Modelling, Reconstruction, Registration, and Fusion of 2D/3D/3D+t Anatomical Structures and Physiological information from Multi-Modality including X-ray, Angiography, MRI, CT, US, etc.
👏🎇Congrats Qianhui Men et al! Very proud of you all! 👏🎇 https://t.co/8C79AmsRxd
Researchers from the Bulte Group at @OxfordBioMedIA and @PerspectumGroup have automated pancreas head, body, and tail segmentation, which can probe disease heterogeneity. See their results in https://t.co/7HZSn2JxDQ and try out the method on GitHub: https://t.co/NgSYCC4Vd9 https://t.co/E3bQBfNN40
Fully-funded DPhil in Deep Learning for Medical Imaging supervised by @KostasKamnitsas Apply by 3rd December! https://t.co/d8AKREjXuD
RT @miua2020ox: The accepted papers reflect the growth in development and application of biomedical imaging. See word cloud generated from…
RT @miua2020ox: Welcome to #MIUA2020, the first ever #MIUA virtual meeting!! Join us here: https://t.co/Mp5DcSpgK2 See #MIUA2020 conferen…
RT @richard_droste: Check out our @MICCAI2020 paper: "Automatic Probe Movement Guidance for Freehand Obstetric Ultrasound" https://t.co/XDU…