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.


BibTex format

author = {Cartucho, J and Tukra, S and Li, Y and S, Elson D and Giannarou, S},
doi = {10.1080/21681163.2020.1835546},
journal = {Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization},
pages = {1--8},
title = {VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery},
url = {},
year = {2020}

RIS format (EndNote, RefMan)

AB - Surgical robots rely on robust and efficient computer vision algorithms to be able to intervene in real-time. The main problem, however, is that the training or testing of such algorithms, especially when using deep learning techniques, requires large endoscopic datasets which are challenging to obtain, since they require expensive hardware, ethical approvals, patient consent and access to hospitals. This paper presents VisionBlender, a solution to efficiently generate large and accurate endoscopic datasets for validating surgical vision algorithms. VisionBlender is a synthetic dataset generator that adds a user interface to Blender, allowing users to generate realistic video sequences with ground truth maps of depth, disparity, segmentation masks, surface normals, optical flow, object pose, and camera parameters. VisionBlender was built with special focus on robotic surgery, and examples of endoscopic data that can be generated using this tool are presented. Possible applications are also discussed, and here we present one of those applications where the generated data has been used to train and evaluate state-of-the-art 3D reconstruction algorithms. Being able to generate realistic endoscopic datasets efficiently, VisionBlender promises an exciting step forward in robotic surgery.
AU - Cartucho,J
AU - Tukra,S
AU - Li,Y
AU - S,Elson D
AU - Giannarou,S
DO - 10.1080/21681163.2020.1835546
EP - 8
PY - 2020///
SN - 2168-1163
SP - 1
TI - VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery
T2 - Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization
UR -
UR -
UR -
ER -