The Centre has a long history of developing new techniques for medical imaging (particularly in magnetic resonance imaging), transforming them from a primarily diagnostic modality into an interventional and therapeutic platform. This is facilitated by the Centre's strong engineering background in practical imaging and image analysis platform development, as well as advances in minimal access and robotic assisted surgery. Hamlyn has a strong tradition in pursuing basic sciences and theoretical research, with a clear focus on clinical translation.

In response to the current paradigm shift and clinical demand in bringing cellular and molecular imaging modalities to an in vivo – in situ setting during surgical intervention, our recent research has also been focussed on novel biophotonics platforms that can be used for real-time tissue characterisation, functional assessment, and intraoperative guidance during minimally invasive surgery. This includes, for example, SMART confocal laser endomicroscopy, time-resolved fluorescence spectroscopy and flexible FLIM catheters.

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  • Journal article
    Yang X, Zhang Y, Lo B, Wu D, Liao H, Zhang Y-Tet al., 2021,

    DBAN: Adversarial Network With Multi-Scale Features for Cardiac MRI Segmentation

    , IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 25, Pages: 2018-2028, ISSN: 2168-2194
  • Journal article
    Wales DJ, Miralles-Comins S, Franco-Castillo I, Cameron JM, Cao Q, Karjalainen E, Alves Fernandes J, Newton GN, Mitchell SG, Sans Vet al., 2021,

    Decoupling manufacturing from application in additive manufactured antimicrobial materials

    , BIOMATERIALS SCIENCE, Vol: 9, Pages: 5467-5476, ISSN: 2047-4830
  • Journal article
    Davids J, Makariou S-G, Ashrafian H, Darzi A, Marcus HJ, Giannarou Set al., 2021,

    Automated Vision-Based Microsurgical Skill Analysis in Neurosurgery Using Deep Learning: Development and Preclinical Validation

    , WORLD NEUROSURGERY, Vol: 149, Pages: E669-E686, ISSN: 1878-8750
  • Journal article
    Qiu J, Lo FP-W, Jiang S, Tsai Y-Y, Sun Y, Lo Bet al., 2021,

    Counting bites and recognizing consumed food from videos for passive dietary monitoring.

    , IEEE Journal of Biomedical and Health Informatics, Vol: 25, Pages: 1471-1482, ISSN: 2168-2194

    Assessing dietary intake in epidemiological studies are predominantly based on self-reports, which are subjective, inefficient, and also prone to error. Technological approaches are therefore emerging to provide objective dietary assessments. Using only egocentric dietary intake videos, this work aims to provide accurate estimation on individual dietary intake through recognizing consumed food items and counting the number of bites taken. This is different from previous studies that rely on inertial sensing to count bites, and also previous studies that only recognize visible food items but not consumed ones. As a subject may not consume all food items visible in a meal, recognizing those consumed food items is more valuable. A new dataset that has 1,022 dietary intake video clips was constructed to validate our concept of bite counting and consumed food item recognition from egocentric videos. 12 subjects participated and 52 meals were captured. A total of 66 unique food items, including food ingredients and drinks, were labelled in the dataset along with a total of 2,039 labelled bites. Deep neural networks were used to perform bite counting and food item recognition in an end-to-end manner. Experiments have shown that counting bites directly from video clips can reach 74.15% top-1 accuracy (classifying between 0-4 bites in 20-second clips), and a MSE value of 0.312 (when using regression). Our experiments on video-based food recognition also show that recognizing consumed food items is indeed harder than recognizing visible ones, with a drop of 25% in F1 score.

  • Journal article
    Dryden SD, Anastasova S, Satta G, Thompson AJ, Leff DR, Darzi Aet al., 2021,

    Rapid uropathogen identification using surface enhanced Raman spectroscopy active filters.

    , Scientific Reports, Vol: 11, Pages: 1-10, ISSN: 2045-2322

    Urinary tract infection is one of the most common bacterial infections leading to increased morbidity, mortality and societal costs. Current diagnostics exacerbate this problem due to an inability to provide timely pathogen identification. Surface enhanced Raman spectroscopy (SERS) has the potential to overcome these issues by providing immediate bacterial classification. To date, achieving accurate classification has required technically complicated processes to capture pathogens, which has precluded the integration of SERS into rapid diagnostics. This work demonstrates that gold-coated membrane filters capture and aggregate bacteria, separating them from urine, while also providing Raman signal enhancement. An optimal gold coating thickness of 50 nm was demonstrated, and the diagnostic performance of the SERS-active filters was assessed using phantom urine infection samples at clinically relevant concentrations (105 CFU/ml). Infected and uninfected (control) samples were identified with an accuracy of 91.1%. Amongst infected samples only, classification of three bacteria (Escherichia coli, Enterococcus faecalis, Klebsiella pneumoniae) was achieved at a rate of 91.6%.

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