Closed Position

• Focus: Translational research on hyperspectral-based quantitative fluorescence imaging linked with a neurosurgery clinical study
• Primary supervisor: Tom Vercauteren
• Secondary supervisor: Jonathan Shapey
• Industry collaborator: Hypervision Surgical
• Funding type: Tuition fee, stipend (Home Fee status only).
• Application closing date: 11 April 2022
• Start date: June 2022

Aim of the project

This project aims at enabling wide-field and real-time quantitative assessment of tumour-specific fluorescence by designing novel deep-learning-based computational algorithms. The project will leverage a compact hyperspectral imaging (HSI) system developed by Hypervision Surgical Ltd initially designed for contrast-free imaging.

• Focus: Translational research on real-time computing for hyperspectral imaging linked with an active neurosurgery clinical study
• Line manager: Tom Vercauteren
• Industry collaborator: Hypervision Surgical
• Clinical collaborator: King’s College Hospital
• Salary: Grade 6, £38,826 - £45,649 per annum, including London Weighting Allowance

Job description

We are seeking an interventional image computing researcher to design and translate the next generation of real-time AI-assisted hyperspectral imaging systems for surgical guidance. The postholder, based within the Department of Surgical & Interventional Engineering at King’s College London, will play a key role in a collaborative project with King’s College Hospital and Hypervision Surgical, a recently founded King’s spin-out company. A clinical neurosurgery study has been set up to underpin this collaboration. The successful candidate will work on the resulting neurosurgical data as well as retrospective data. They will also have the opportunity to provide insight on how to best acquire prospective data.

• 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, £38,826 - £45,649 per annum, including London Weighting Allowance

Job description

We are seeking an interventional image computing researcher to design and translate the next generation of AI-assisted hyperspectral imaging systems for surgical guidance using quantitative fluorescence. The postholder, based within the Department of Surgical & Interventional Engineering at King’s College London, will play a key role in a collaborative project with King’s College Hospital and work closely with the project’s industrial collaborator Hypervision Surgical, a recently founded King’s spin-out company. A clinical neurosurgery study has been set up to underpin this collaboration. The successful candidate will work on the resulting neurosurgical data as well as controlled phantom data. They will also have the opportunity to provide insight on how to best acquire prospective data.

• Focus: Real-time learning-based processing of hyperspectral imaging and its integration in complex robotic systems
• Line manager: Tom Vercauteren
• Related research project: FAROS
• Salary: Grade 6, £38,826 - £45,649 or Grade 7, £46,934 - £50,919 per annum, including London Weighting Allowance

Job description

We are seeking a highly motivated individual to join us and work on FAROS, a European research project dedicated to advancing Functionally Accurate RObotic Surgery, https://h2020faros.eu, in collaboration with KU Leuven, Sorbonne University, Balgrist Hospital and SpineGuard.

• Title: Joint learning of stereo vision reconstruction and hyperspectral imaging upsampling for binocular surgical guidance
• First supervisor: Tom Vercauteren
• Second supervisor: Christos Bergeles
• Clinical Supervisor: Jonathan Shapey
• Start date: October 2022

Project summary

Optimal outcomes in oncology surgery are hindered by the difficulty of differentiating between tumour and surrounding tissues during surgery. Real-time hyperspectral imaging (HSI) provides rich high-dimensional intraoperative information that has the potential to significantly improve tissue characterisation and thus benefit patient outcomes. Yet taking full advantage of HSI data in clinical indication performed under binocular guidance (e.g. microsurgery and robotic surgery) poses several methodological challenges which this project aims to address. Real-time HSI sensors are limited in the spatial resolution they can capture. This further impacts the usefulness of such HSI sensors in multi-view capture settings. In this project, we will take advantage of a stereo-vision combination with a high-resolution RGB viewpoint and a HSI viewpoint. The student will develop bespoke learning-based computational approaches to reconstruct high-quality 3D scenes combining the intuitiveness of RGB guidance and the rich semantic information extracted from HSI.

• Title: Exploiting multi-task learning for endoscopic vision in robotic surgery
• First supervisor: Miaojing Shi
• Second supervisor: Tom Vercauteren
• Clinical Supervisor: Asit Arora
• Start date: October 2022

Project summary

Multi-task learning is common in deep learning, where clear evidence shows that jointly learning correlated tasks can improve on individual performances. Notwithstanding, in reality, many tasks are processed independently. The reasons are manifold: