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Science for tomorrow's neurosurgery: Patient & Public Involvement (PPI) group - September 2023 group meeting

On 21st September we held our fourth ‘Science for Tomorrow’s Neurosurgery’ PPI group meeting online, with presentations from Oscar, Matt and Silvère. Presentations focused on an update from the NeuroHSI trial, with clear demonstration of improvements in resolution of the HSI images we are now able to acquire; this prompted real praise from our patient representatives, which is extremely reassuring for the trial going forward. We also took this opportunity to announce the completion of the first phase of NeuroPPEYE, in which we aim to use HSI to quantify tumour fluorescence beyond that which the human eye can see. Discussions were centered around the theme of “what is an acceptable number of participants for proof of concept studies,” generating very interesting points of view that ultimately concluded that there was no “hard number” from the patient perspective, as long as a thorough assessment of the technology had been carried out. This is extremely helpful in how we progress with the trials, particularly NeuroPPEYE, which will begin recruitment for its second phase shortly. Once again, the themes and discussions were summarized in picture format by our phenomenal illustrator, Jenny Leonard (see below) and we are already making plans for our next meeting in February 2024!

Jonathan Shapey delivers the Hunterian Lecture at the Society of British Neurological Surgeons autumn congress

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.

MICCAI 2023 Presentation for Deep Homography Prediction for Endoscopic Camera Motion Imitation Learning

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).

Presentation Video for IEEE IUS

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.

Presentation Video for OpTaS

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).

Our crossMoDA challenge to be held MICCAI 2023 is now live!

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.

Hypervision Surgical awarded Cutlers' Surgical Prize for HyperSnap hyperspectral imaging system

The four co-founders of Hypervison Surgical, a King’s spin-out company, have been awarded the Cutlers’ Surgical Prize for outstanding work in the field of instrumentation, innovation and technical development.

Hypervision Surgical receives the Cutlers’ Surgical Prize.
Hypervision Surgical receives the Cutlers’ Surgical Prize.

The Cutlers’ Surgical Prize is one of the most prestigious annual prizes for original innovation in the design or application of surgical instruments, equipment or practice to improve the health and recovery of surgical patients.