This paper introduces CholecInstanceSeg, the largest open-access dataset for surgical tool instance segmentation to date. It addresses key gaps in existing datasets by providing high-quality instance annotations for over 41,000 frames derived from clinical laparoscopic cholecystectomy procedures.

Tom Vercauteren has been invited to be a keynote speaker at the MICAD 2025 conference (The 6th International Conference on Medical Imaging and Computer-Aided Diagnosis) to be held in London in November.

Tom Vercauteren has been appointed as Senior Editor for the highly-ranked Medical Image Analysis journal, an official journal of the MICCAI society. Sebastien Ourselin also features on the editorial board of the publication.

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. The work addresses and tries to go beyond the common tool following assumption in endoscopic camera motion automation. This work will be presented at the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023).

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).

You can read the preprint on arXiv: 2306.11034 and get the code from GitHub.

This video presents work lead by Christopher E. Mower. OpTaS is an OPtimization-based TAsk Specification library for trajectory optimization and model predictive control. This work will be presented at the 2023 IEEE International Conference on Robotics and Automation (ICRA).

CAI4CAI members and alumni are leading the organization of the new edition of the cross-modality Domain Adaptation challenge (crossMoDA) for medical image segmentation Challenge, which will runs as an official challenge during the Medical Image Computing and Computer Assisted Interventions (MICCAI) 2023 conference.

This video presents work lead by Christopher E. Mower. OpTaS is an OPtimization-based TAsk Specification Python library for trajectory optimization and model predictive control. The code can be found at https://github.com/cmower/optas. This work will be presented at the 2023 IEEE International Conference on Robotics and Automation (ICRA).

This video presents work lead by Christopher E. Mower. The ROS-PyBullet Interface is a framework between the reliable contact simulator PyBullet and the Robot Operating System (ROS) with additional utilities for Human-Robot Interaction in the simulated environment. This work was presented at the Conference on Robot Learning (CoRL), 2022. The corresponding paper can be found at PMLR.

Muhammad led the development of FastGeodis, 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. This package is able to handle 2D as well as 3D data, where it achieves up to a 20x speedup on a CPU and up to a 74x speedup on a GPU as compared to an existing open-source library that uses a non-parallelisable single-thread CPU implementation. Further in-depth comparison of performance improvements is discussed in the FastGeodis documentation.

Lucas led on the development of the first fetal brain atlas for spina bifida aperta (SBA). This first-time atlas will allow researchers to perform measurements of the brain’s anatomy and to study its development in a large population of unborn babies with SBA

WiM-WILL is a digital platform that provides MICCAI members to share their career pathways to the outside world in parallel to MICCAI conference. Muhammad Asad (interviewer) and Navodini Wijethilake (interviewee) from our lab group participated in this competition this year and secured the second place. Their interview was focused on overcoming challenges in research as a student. The link to the complete interview is available below and on youtube.

Yijing has been awarded the prestigious Royal Academy of Engineering Research Fellowship for her research in the development of tools to help neurosurgeons during surgery.

Recent release of MONAI Label v0.4.0 extends support for multi-label scribbles interactions to enable scribbles-based interactive segmentation methods.

CAI4CAI team member Muhammad Asad contributed to the development, testing and review of features related to scribbles-based interactive segmentation in MONAI Label.

The FAROS consortium had a fantastic and highly productive time working at the labs of Balgrist Campus AG and the operating room at Balgrist University Hospital this week.

A great milestone today for Rémi Delaunay who passed his PhD viva with minor corrections! His thesis is entitled “Computational ultrasound tissue characterisation for brain tumour resection”.

Thanks to Pierre Gélat and Greg Slabaugh for their role examining the thesis.

The Surgical & Interventional Engineering Centre for Doctoral Training delivers translational research to transform patient pathways. Meet some of our talented PhD students is this programme who are engineering better health!

CAI4CAI will be presenting their work at MICCAI 2021, the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, held from 27 September to 1 October 2021 as a virtual event.

Join us at the IEEE International Ultrasonics Symposium where CAI4CAI members will present their work.

Christian Baker will be presenting on “Real-Time Ultrasonic Tracking of an Intraoperative Needle Tip with Integrated Fibre-optic Hydrophone” as part of the Tissue Characterization & Real Time Imaging (AM) poster session.

Join us for the TRABIT conference (7-10 Sept 2021) with outstanding speakers and fun networking events. Registration is free but mandatory.

You can check all the videos made by the PhD students of TRABIT to present their research projects on youtube.

CAI4CAI members are contributing to guest editing the Medical Image Analysis Special Issue on Explainable and Generalizable Deep Learning Methods for Medical Image Computing. The final submission dealine is on December 1, 2021 but papers will be reviewed as they arrive.

CAI4CAI members are leading the organization of the cross-modality Domain Adaptation challenge (crossMoDA) for medical image segmentation Challenge, which runs as an official challenge during the Medical Image Computing and Computer Assisted Interventions (MICCAI) 2021 conference.

King’s College London, School of Biomedical Engineering & Imaging Sciences and Moon Surgical announced a new strategic partnership to develop Machine Learning applications for Computer-Assisted Surgery, which aims to strengthen surgical artificial intelligence (AI), data and analytics, and accelerate translation from King’s College London research into clinical usage.

Yijing will develop a 3D functional optical imaging system for guiding brain tumour resection.

She will engineer two emerging modalities, light field and multispectral imaging into a compact device, and develop novel image reconstruction algorithm to produce and display high-dimensional images. The CME fellowship will support her to carry out proof-of-concept studies, start critical new collaborations within and outside the centre. She hopes the award will act as a stepping stone to enable future long-term fellowship and grants, thus to establish an independent research programme.

Miguel will collaborate with Fang-Yu Lin and Shu Wang to create activities to engage school students with ultrasound-guidance intervention and fetal medicine. In the FETUS project, they will develop interactive activities with 3D-printed fetus, placenta phantoms as well as the integreation of a simulator that explain principles of needle enhancement of an ultrasound needle tracking system.

A great milestone today for Luis Garcia Peraza Herrera who passed his PhD viva with minor corrections! His thesis is entitled “Deep Learning for Real-time Image Understanding in Endoscopic Vision”.

Thanks to Ben Glocker and Enrico Grisan for their role examining the thesis.

Read about our research vision in our Proceedings of the IEEE paper.