Research Projects
Small vessel segmentation and 3D visualisation

Small vessel assessment has great clinical and research significance for early disease warning, disease diagnosis, surgical planning, etc. However, the identification and analysis of small vessels is a challenging task in clinical practice due to the thin and complex structures, fuzzy boundaries, and noise interference. The comprehensive quantitative analysis of small vessels is nearly impossible for manual assessment. With the rapid development of machine learning and computer vision technology, the deep learning model is developed for a variety of medical image analysis tasks and has achieved performance comparable with the human specialist.

Through this project, we aim to develop a deep learning system for comprehensive small vessel analysis in different modalities of images, which will be further used in multiple clinical tasks, such as disease detection and prediction, surgical planning, pathology progression prediction, etc.

Our Objectives include:
1. Establish a large dataset containing multi-modality images with vessel structures annotated by specialists.
2. Develop a deep learning framework for vessel detection that has a high sensitivity for the small vessel structure.
3. A variety of clinical analysis tasks will be performed based on the results of small vessel analysis.
4. Technical and clinical validation is conducted to validate the developed system.

Professor T Zhang, Department of Orthopaedics and Traumatology

Professor Teng Grace Zhang is a biomedical engineer with a medical background. Most of Grace’s research combines both disciplines by focusing on the modelling of biological systems with direct clinical applications including telemedicine, auto-diagnosis, surgical planning and tracking to facilitate real-time feedback with minimal radiations.

Currently,  Grace is an Assistant Professor, at the Digital Health Laboratory of the Orthopaedics and Traumatology Department, Clinical Medicine School, Faculty of Medicine, The University of Hong Kong (HKU). Previously, Grace has worked for nearly seven years as a Scientific Officer at the St George Clinical School of the University of New South Wales (UNSW, Sydney, Australia). All Grace’s tertiary education including her PhD (Australian Postgraduate Award) was completed at UNSW. Grace also has experience in running multiple instruments and drug trials funded by Medtronic and Sigma. She’s experienced in managing a new system developed as she worked in Kunovus Australia for two years as a system engineer prior to coming to the University of Hong Kong. Members of IEEE, EMBS, ABEC etc. The current ongoing projects include HMRF08192266 on light-based AI-driven malalignment quantification, HMRF19200911 on AIS digital health, PRP/078/21FX for surgical planning, AOSpine for automated spine malalignment screening, MRP/038/20X for non-radiation AI diagnosis and ITS/329/19 for anti-migration bone screws.  

Biography
HKU Scholars Hub
Lab
ORCID
tgzhang@hku.hk

For more information or to express interest for this project, please email the supervisor or the specified contact point in the project description.  Interested candidates are advised to enclose with your email:

1. your CV,
2. a brief description of your research interest and experience, and
3. two reference letters (not required for HKUMed UG students seeking MRes[Med]/URIS projects).

Information on the research programme, funding support and admission documentations could be referenced online at the Research Postgraduate Admissions website. General admission enquiries should be directed to rpgmed@hku.hk.

HKUMed MBBS students interested in the Master of Research in Medicine (MRes[Med]) programme may visit the programme website for more information.  

HKUMed UG students interested in the Undergraduate Research Internship Scheme (URIS) may visit the scheme’s website for more information.