• Focus: Vision-language models for neuroimaging
• Primary supervisor: Tom Vercauteren
• Secondary supervisor: Alexander Hammers
• Funding type: 4-year fully-funded MRC DTP studentship including a stipend, tuition fees, research training and support grant (RTSG), and a travel and conference allowancep.
• Application closing date: 8 November 2023
• Start date: October 2024

Project Overview

Semantic segmentation of brain structures from medical images, in particular Magnetic Resonance Imaging (MRI), plays an important role in many neuroimaging applications. Deep learning based segmentation algorithms are now achieving state-of-the-art segmentation results but currently require large amounts of annotated data under predefined segmentation protocols and data inclusion/exclusion criteria. The rigidity of such approaches forbids natural interactions by humans and thus limits the usefulness for non-routine questions.

• Title: Incorporating Expert-consistent Spatial Structure Relationships in Learning-based Brain Parcellation
• First supervisor: Tom Vercauteren
• Second supervisor: Rachel Sparks
• Clinical Supervisor: Jonathan Shapey
• Start date: February 2024

Aim of the PhD Project

• Develop trustworthy deep learning-based brain segmentation/parcellation.
• Formalise trustworthiness as contracts on spatial relationships between labels that the algorithm must fulfil.
• Establish mathematical/algorithmic frameworks to guarantee that the proposed segmentation/parcellation respect the contracts of trust.
• Implement, validate, and disseminate the proposed algorithms using open-access datasets.

Project summary

Automated segmentation and labelling of brain structures from medical images, in particular Magnetic Resonance Imaging (MRI), plays an important role in many applications ranging from surgical planning to neuroscience studies. For example, in Deep Brain Stimulation (DBS) procedures used to treat some movement disorders, segmentation of the basal ganglia and structures such as the subthalamic nucleus (STN) can help with precise targeting of the neurostimulation electrodes being implanted in the patient’s brain. Going beyond segmentation of a few discrete structures, some applications required a full brain parcellation, i.e., a partition of the entire brain into a set of non-overlapping spatial regions of anatomical or functional significance. Brain parcellation have notably been used to automate the trajectory planning of multiple intracranial electrodes for epilepsy surgery or to support the assessment of brain atrophy patterns for dementia monitoring.

• Title: Artificial intelligence-driven radiosurgery planning for brain metastases
• First supervisor: Jonathan Shapey
• Second supervisor: Tom Vercauteren
• Clinical Supervisor: Ian Paddick
• Start date: October 2023

Aim of the PhD Project

• Implement learning-based registration to curate a spatially-normalised dataset of MR images previously used to deliver stereotactic radiosurgery to brain metastases
• Develop data-driven deep learning frameworks to automatically detect and segment brain metastases while allowing for interactive corrections
• Develop imaging biomarkers to predict tumour response and behaviour following treatment

Project summary

Approximately 25,000 patients are diagnosed with a brain tumour every year in the UK. Brain metastases affect up to 40% of patients with extracranial primary cancer. Furthermore, although there are presently no reliable data, metastatic brain tumours are thought to outnumber primary malignant brain tumours by at least 3:1. Patients with brain metastases require individualized patient management and may include surgery, stereotactic radiosurgery, fractionated radiotherapy and chemotherapy, either alone or in combination.

• Title: Accurate automated quantification of spine evolution — it’s about time!
• First supervisor: Marc Modat
• Second supervisor: Tom Vercauteren
• Clinical Supervisor: Amanda Isaac
• Start date: February 2024

Aim of the PhD Project

• Complement radiological expertise with automated analysis of longitudinal spine changes
• Development of longitudinal registration algorithms to align spine images from different imaging modalities
• Development of processing tools robust to the presence of metal artefact and various fields of view
• Extraction of imaging biomarkers of spine degeneration.

Project summary

Back pain presents because of a wide range of conditions in the spine and is often multifactorial. Spine appearances also change after surgery, and postoperative changes depend on the specific interventions offered to patients and human factors such as healing and mechanical adaptations, which are also unique to each patient. When reviewing medical images for diagnostic, monitoring or prognosis purpose, radiologists are required to evaluate multiple structures, including bone, muscles, and nerves, as well as the surrounding soft tissues and any instrumentation used in surgery. They must use their expertise to assess how each structure has evolved over time visually. Such readings are, therefore, both time-consuming and require dedicated expertise, which is limited to large regional spine centres throughout the UK.

• Title: Physically-informed learning-based beamforming for multi-transducer ultrasound imaging
• First supervisor: Laura Maria Peralta Pereira
• Second supervisor: Tom Vercauteren
• Clinical Supervisor: Dean Huang
• Start date: February 2024

Aim of the PhD Project

• Pursue sparse solutions to handle the channel count required to coherently operate multiple ultrasound transducer and design and implement machine learning strategies to avoid the sparsity-related artefacts in the images.
• Develop advanced beamforming techniques using machine learning approaches informed by ultrasound physics to address the concern of the flexible geometry in a multi-transducer imaging system and achieve unprecedented image quality.
• Explore the application of the techniques on healthy volunteers.

Project description

Medical Ultrasound (US) is a low-cost imaging method that is long-established and widely used for screening, diagnosis, therapy monitoring, and guidance of interventional procedures. However, the usefulness of conventional US systems is limited by physical constraints mainly imposed by the small size of the handheld probe that lead to low-resolution images with a restricted field of view and view-dependent artefacts.

• Title: Artificial intelligence-driven management of brain tumours
• Project ID: BE-MI2023_15
• First supervisor: Jonathan Shapey
• Second supervisor: Tom Vercauteren
• Start date: October 2023
• Application deadline: Wednesday 9 November 2022, 1:00 PM (GMT)

Project description

Approximately 25,000 patients are diagnosed with a brain tumour every year in the UK. Meningiomas and pituitary adenomas are the first and third most common primary tumour, accounting for over 50% of all primary brain tumours. Brain metastases affect up to 40% of patients with extracranial primary cancer.

• 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: