CAI4CAI

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

About us

We are based at King’s College London

Surgical assistance

Our engineering research aims at improving surgical & interventional sciences

Multidisciplinarity

We take a multidisciplinary, collaborative approach to solve clinical challenges (📖)

London

Our labs are located in St Thomas’ hospital, a prominent London landmark

Machine learning

We design learning-based approaches for multi-modal reasoning

Medical imaging

Medical imaging is a core source of information in our research

Computational biophotonics

We design intelligent systems exploiting information captured by safe light

Contextual AI

We thrive at providing the right information at the right time to the surgical team and embrace human/AI interactions (📖)

Translational research

Strong industrial links are key to accelerate translation of cutting-edge research into clinical impact

Open culture

We support open source, open access and involve patients in our research (👋)

Collaborative research

NeuroHSI

A prospective observational study to evaluate the use of an intraoperative hyperspectral imaging system in neurosurgery.

NeuroPPEye

A prospective observational study to evaluate intraoperative hyperspectral imaging for real-time quantitative fluorescence-guided surgery of low-grade glioma.

The EPSRC Centre for Doctoral Training in Advanced Engineering in Personalised Surgery & Intervention (CDT AE-PSI) is an innovative three-and-a-half year PhD training program aiming to deliver translational research and transform patient pathways.

CDT SMI

Through a comprehensive, integrated training programme, the Centre for Doctoral Training in Smart Medical Imaging trains the next generation of medical imaging researchers.

FAROS

The Functionally Accurate RObotic Surgery (FAROS) H2020 project aims at improving functional accuracy through embedding physical intelligence in surgical robotics.

GIFT-Surg

The GIFT-Surg project is an international research effort developing the technology, tools and training necessary to make fetal surgery a viable possibility.

icovid

The icovid project focuses on AI-based lung CT analysis providing accurate quantification of disease and prognostic information in patients with suspected COVID-19 disease.

K-CSC

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.

MRC DTP BiomedSci

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.

TRABIT

The 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 CME

The Wellcome / EPSRC Centre for Medical Engineering combines fundamental research in engineering, physics, mathematics, computing, and chemistry with medicine and biomedical research.

Spin-outs and industry collaborations

Pathways to clinical impact

Moon Surgical

Moon Surgical has partnered with us to develop machine learning for computer-assisted surgery. More information on our press release.

Hypervision Surgical Ltd

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.

icometrix

icometrix is the consortium lead of the icovid project.

Intel (previously COSMONiO)

Intel is the industrial sponsor of Theo Barfoot’s’s PhD on Active and continual learning strategies for deep learning assisted interactive segmentation of new databases.

Mauna Kea Technologies

Tom Vercauteren worked for 10 years with Mauna Kea Technologies (MKT) before resuming his academic career.

Medtronic

Medtronic is the industrial sponsor of Tom Vercauteren’s Medtronic / Royal Academy of Engineering Research Chair in Machine Learning for Computer-Assisted Neurosurgery.

Open research

Exemplar outputs of our research

CAI4CAI on github

We support open source and typically use github to disseminate our research. Repositories can be found on our group organisation (CAI4CAI) or individual member github profiles.

Transferring Relative Monocular Depth to Surgical Vision with Temporal Consistency

This work makes significant strides in the field of monoocular endoscopic depth perception, drastically improving on previous methods. The task of monocular depth perception demonstrates a nuanced understanding of the surgical scene, and could act as a vital building block in future technologies. To acheive our results, we leverage large vision transformers trained on huge natural image datasets and fine tuned to our ensembled meta dataset of sugical videos. Read more about it in our pre-print, or get our model here.

FastGeodis: Fast generalised geodesic distance transform

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.

A spatio-temporal atlas of the developing fetal brain with spina bifida aperta

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.

Image Compositing for Segmentation of Surgical Tools without Manual Annotations

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.

MONAI (Medical Open Network for AI): PyTorch for medical imaging

We contribute to MONAI, a PyTorch-based, open-source framework for deep learning in healthcare imaging, part of PyTorch Ecosystem.

Learning joint Segmentation of Tissues And Brain Lesions (jSTABL) from task-specific hetero-modal domain-shifted datasets

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: Medical image registration using deep learning

DeepReg is a freely available, community-supported open-source toolkit for research and education in medical image registration using deep learning (📖).

Intrapapillary Capillary Loop (IPCL) Classification

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 (📖).”

Fetal brain MRI reconstruction (NiftyMIC)

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 (📖).”

NiftyNet: Open-source convolutional neural networks platform for research in medical image analysis and image-guided therapy

[unmaintained] NiftyNet is a TensorFlow 1.x based open-source convolutional neural networks (CNN) platform for research in medical image analysis and image-guided therapy (📖). It has been superseeded by MONAI.

Python Unified Multi-tasking API (PUMA)

PUMA provides a simultaneous multi-tasking framework that takes care of managing the complexities of executing and controlling multiple threads and/or processes.

GIFT-Grab: Simple frame grabbing API

GIFT-Grab is an open-source C++ and Python API for acquiring, processing and encoding video streams in real time (📖).

The CAI4CAI team

Principal investigators

Tom Vercauteren

CAI4CAI lead

Professor of Interventional Image Computing

King's College London, United Kingdom

Sébastien Ourselin

Professor of Healthcare Engineering

King's College London, United Kingdom

Jonathan Shapey

Clinical Academic and Consultant Neurosurgeon

King's College London, United Kingdom

King's College Hospital NHS Foundation Trust, United Kingdom

Research team members

Aaron Kujawa

Research Associate

King's College London, United Kingdom

Groups: Tom, Jonathan

Charlie Budd

Research Software Engineer

King's College London, United Kingdom

Group: Tom

Junwen Wang

Research Associate

King's College London, United Kingdom

Groups: Tom, Jonathan

Lorena Garcia-Foncillas Macias

MRes/PhD Student

King's College London, United Kingdom

Groups: Jonathan, Tom

Marina Ivory

Research Associate

King's College London, United Kingdom

Groups: Jonathan, Tom

Matthew Elliot

Neurosurgical Clinical Research Fellow and PhD Student

King's College London, United Kingdom

King's College Hospital NHS Foundation Trust, United Kingdom

Groups: Jonathan, Tom

Maxence Boels

PhD Student

King's College London, United Kingdom

Groups: Seb, Prokar

Meng Wei

PhD Student

King's College London, United Kingdom

CDT Smart Medical Imaging

Groups: Tom, Miaojing

Navodini Wijethilake

PhD Student

King's College London, United Kingdom

Groups: Jonathan, Tom

Oluwatosin Alabi

PhD Student

King's College London, United Kingdom

EPSRC CDT in Smart Medical Imaging

Groups: Miaojing, Tom

Oscar MacCormac

Neurosurgery Clinical Research Fellow and PhD Student

King's College London, United Kingdom

King's College Hospital NHS Foundation Trust, United Kingdom

Groups: Jonathan, Tom

Peichao Li

PhD Student

King's College London, United Kingdom

Groups: Tom, Jonathan

Silvère Ségaud

Research Associate

King's College London, United Kingdom

Groups: Tom, Jonathan, Yijing

Soumya Kundu

PhD Student

King's College London, United Kingdom

Groups: Jonathan, Tom

Tangqi Shi

PhD Student

King's College London, United Kingdom

Group: Tom Booth, Tom

Theo Barfoot

PhD Student

King's College London, United Kingdom

Imperial College London, United Kingdom

EPSRC CDT in Smart Medical Imaging

Groups: Tom, Ben, Jonathan

Yijing Xie

L’Oréal UK&I and UNESCO for Women in Science Rising Talent Fellow

King's College London, United Kingdom

Affiliated research team members

Helena Williams

Postdoctoral fellow

KU Leuven, Belgium

King's College London, United Kingdom

Groups: Jan, Jan, Tom

Martin Huber

PhD Student

King's College London, United Kingdom

Groups: Christos, Tom

Michael Ebner

CEO and co-founder, Hypervision Surgical

Hypervision Surgical Ltd, United Kingdom

King's College London, United Kingdom

Alumni

CAI4CAI alumni

See our previous lab members

Open positions

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.

CAI4CAI research to be presented at MICCAI 2024 - 4 full papers (including 2 early accept), one CLINICCAI oral presentation

The main MICCAI 2024 outcomes are now out and it’s a pleasure to announce that our research will be prominently featured. Congrats to the main authors from the group and all co-authors!

Tom Vercauteren, Charlie Budd, Aaron Kujawa, Theo Barfoot

Jun 23, 2024 2 min read