We are an academic research group focusing on Contextual Artificial Intelligence for Computer Assisted Interventions.
CAI4CAI is embedded in the School of Biomedical Engineering & Imaging Sciences at King’s College London, UK
Our engineering research aims at improving surgical & interventional sciences
We take a multidisciplinary, collaborative approach to solve clinical challenges (📖)
Our labs are located in St Thomas’ hospital, a prominent London landmark
We design learning-based approaches for multi-modal reasoning
Medical imaging is a core source of information in our research
We design intelligent systems exploiting information captured by safe light
We thrive at providing the right information at the right time to the surgical team and embrace human/AI interactions (📖)
Strong industrial links are key to accelerate translation of cutting-edge research into clinical impact
We support open source, open access and involve patients in our research
Jonathan Shapey had the great honour to deliver the Hunterian Lecture at the Society of British Neurological Surgeons autumn congress (SBNS London 2023). Jonathan presented his work in developing a label-free real-time intraoperative hyperspectralimaging system for neurosurgery.
This video presents work lead by Martin Huber. Deep Homography Prediction for Endoscopic Camera Motion Imitation Learning investigates a fully self-supervised method for learning endoscopic camera motion from readily available datasets of laparoscopic interventions.
This video presents work lead by Mengjie Shi focusing on learning-based sound-speed correction for dual-modal photoacoustic/ultrasound imaging. This work will be presented at the 2023 IEEE International Ultrasonics Symposium (IUS).
Recently, we organized a Public and Patient Involvement (PPI) group with Vestibular Schwannoma patients to understand their perspectives on an patient-centered automated report. Partnering with the British Acoustic Neuroma Association (BANA), we recruited participants by circulating a form within the BANA community through their social media platforms.
Post overview: Focus: Translational research on hyperspectral-based quantitative fluorescence imaging linked with a neurosurgery clinical study Line manager: Tom Vercauteren Clinical collaborator: King’s College Hospital Industry collaborator: Hypervision Surgical Salary: Grade 6, £41,386-£48,414 or Grade 7 £49,737-£55,306 per annum, including London Weighting Allowance State-of-the-art fluorescence imaging system for neurosurgical guidance.
A prospective observational study to evaluate the use of an intraoperative hyperspectral imaging system in neurosurgery.
A prospective observational study to evaluate intraoperative hyperspectral imaging for real-time quantitative fluorescence-guided surgery of low-grade glioma.
Centre for Doctoral Training in Surgical & Interventional Engineering (CDT SIE) is an innovative three-and-a-half year PhD training program aiming to deliver translational research and transform patient pathways.
Through a comprehensive, integrated training programme, the
Centre for Doctoral Training in Smart Medical Imaging trains the next generation of medical imaging researchers.
Functionally Accurate RObotic Surgery (FAROS) H2020 project aims at improving functional accuracy through embedding physical intelligence in surgical robotics.
GIFT-Surg project is an international research effort developing the technology, tools and training necessary to make fetal surgery a viable possibility.
icovid project focuses on AI-based lung CT analysis providing accurate quantification of disease and prognostic information in patients with suspected COVID-19 disease.
Up to 100
King’s-China Scholarship Council PhD Scholarship programme (K-CSC) joint scholarship awards are available per year to support students from China who are seeking to start an MPhil/PhD degree at King’s College London.
The integrated and multi-disciplinary approach of the
MRC Doctoral Training Partnership in Biomedical Sciences (MRC DTP BiomedSci) to medical research offers a wealth of cutting-edge PhD training training opportunities in fundamental discovery science, translational research and experimental medicine.
Translational Brain Imaging Training Network (TRABIT) is an interdisciplinary and intersectoral joint PhD training effort of computational scientists, clinicians, and the industry in the field of neuroimaging.
Wellcome / EPSRC Centre for Medical Engineering combines fundamental research in engineering, physics, mathematics, computing, and chemistry with medicine and biomedical research.
Pathways to clinical impact
Following successful in-patient clinical studies of CAI4CAI’s translational research on computational hyperspectral imaging system for intraoperative surgical guidance,
Hypervision Surgical Ltd was founded by Michael Ebner, Tom Vercauteren, Jonathan Shapey, and Sébastien Ourselin.
In collaboration with CAI4CAI,
Hypervision Surgical’s goal is to convert the AI-powered imaging prototype system into a commercial medical device to equip clinicians with advanced computer-assisted tissue analysis for improved surgical precision and patient safety.
Exemplar outputs of our research
FastGeodis is an open-source package that provides efficient implementations for computing Geodesic and Euclidean distance transforms (or a mixture of both), targetting efficient utilisation of CPU and GPU hardware.
We make publicly available a spatio-temporal fetal brain MRI atlas for SBA. This atlas can support future research on automatic segmentation methods for brain 3D MRI of fetuses with SBA.
We have recently published the paper “Garcia-Peraza-Herrera, L. C., Fidon, L., DEttorre, C. Stoyanov, D., Vercauteren, T., Ourselin, S. (2021). Image Compositing for Segmentation of Surgical Tools without Manual Annotations. Transactions in Medical Imaging (📖)”. Inspired by special effects, we introduce a novel deep-learning method to segment surgical instruments in endoscopic images.
We contribute to MONAI, a PyTorch-based, open-source framework for deep learning in healthcare imaging, part of PyTorch Ecosystem.
Open source PyTorch implementation of “Dorent, R., Booth, T., Li, W., Sudre, C. H., Kafiabadi, S., Cardoso, J., … & Vercauteren, T. (2020). Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets. Medical Image Analysis, 67, 101862 (📖).”
DeepReg is a freely available, community-supported open-source toolkit for research and education in medical image registration using deep learning (📖).
We provide open source code and open access data for our paper “García-Peraza-Herrera, L. C., Everson, M., Lovat, L., Wang, H. P., Wang, W. L., Haidry, R., … & Vercauteren, T. (2020). Intrapapillary capillary loop classification in magnification endoscopy: Open dataset and baseline methodology. International journal of computer assisted radiology and surgery, 1-9 (📖).”
NiftyMIC is a Python-based open-source toolkit for research developed within the GIFT-Surg project to reconstruct an isotropic, high-resolution volume from multiple, possibly motion-corrupted, stacks of low-resolution 2D slices. Read “Ebner, M., Wang, G., Li, W., Aertsen, M., Patel, P. A., Aughwane, R., … & David, A. L. (2020). An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI. NeuroImage, 206, 116324 (📖).”
PUMA provides a simultaneous multi-tasking framework that takes care of managing the complexities of executing and controlling multiple threads and/or processes.
You can browse our list of open positions (if any) here, as well as get an insight on the type of positions we typically advertise by browsing through our list of previous openings. We are also supportive of hosting strong PhD candidates and researchers supported by a personal fellowship/grant.
Please note that applications for the listed open positions need to be made through the University portal to be formally taken into acount..js-id-open-position