Developing next-generation, AI-enabled, medical image processing for multiple sclerosis clinical trials and routine care.
Lead Research Organisation:
University College London
Department Name: Medical Physics and Biomedical Eng
Abstract
Project Background:
Multiple sclerosis (MS) is a disabling disease that causes disability in young persons costing the UK more than £3 billion per year. Images taken from brain and spinal cord can tell us what the current status of a patient is and how they may evolve. Current image processing tools to measure changes on brain and spinal cord are time consuming, require expensive and multi-step pipelines, and rely on the availability of high quality data which are often missing in the real world clinical practice. The candidate will develop a set of image processing tools using the latest advances in computer vision field to segment brain MRI scans, spinal cord MRI, and detect changes in these structures over time using data from both clinical trials and hospitals from across the UK. This project will use unique clinical trial datasets at the Queen Square Multiple Sclerosis Centre, MRI data from more than 8 hospitals from across the UK, and be able to test their potential for their applications in clinical trials in the industry with our collaborator (IXICO). This is a unique opportunity in between the academia and industry to develop the next generation of AI algorithms to impact drug development and patient care.
Research aims:
- Develop deep-learning based image simulators to enable efficient and semi-supervised image processing for the brain and spinal cord MRI (year 1 and year2)
- Develop ultra-efficient image segmentation models for detecting disease activity and disease progression in multiple sclerosis using longitudinal image processing (year 2 and year 3)
- Validate the developed models in clinical trials and real world data from our partner hospital (UCLH Trust / National Hospital for Neurology and Neurosurgery) and collaborating hospitals from across the UK
The outcome of the project will be a unique model that will receive either brain or spinal cord NHS MRI and will output volumes of relevant structures.
Multiple sclerosis (MS) is a disabling disease that causes disability in young persons costing the UK more than £3 billion per year. Images taken from brain and spinal cord can tell us what the current status of a patient is and how they may evolve. Current image processing tools to measure changes on brain and spinal cord are time consuming, require expensive and multi-step pipelines, and rely on the availability of high quality data which are often missing in the real world clinical practice. The candidate will develop a set of image processing tools using the latest advances in computer vision field to segment brain MRI scans, spinal cord MRI, and detect changes in these structures over time using data from both clinical trials and hospitals from across the UK. This project will use unique clinical trial datasets at the Queen Square Multiple Sclerosis Centre, MRI data from more than 8 hospitals from across the UK, and be able to test their potential for their applications in clinical trials in the industry with our collaborator (IXICO). This is a unique opportunity in between the academia and industry to develop the next generation of AI algorithms to impact drug development and patient care.
Research aims:
- Develop deep-learning based image simulators to enable efficient and semi-supervised image processing for the brain and spinal cord MRI (year 1 and year2)
- Develop ultra-efficient image segmentation models for detecting disease activity and disease progression in multiple sclerosis using longitudinal image processing (year 2 and year 3)
- Validate the developed models in clinical trials and real world data from our partner hospital (UCLH Trust / National Hospital for Neurology and Neurosurgery) and collaborating hospitals from across the UK
The outcome of the project will be a unique model that will receive either brain or spinal cord NHS MRI and will output volumes of relevant structures.
People |
ORCID iD |
| Arman Eshaghi (Primary Supervisor) | |
| Barbara Brito Vega (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/S021930/1 | 01/10/2019 | 31/03/2028 | |||
| 2877679 | Studentship | EP/S021930/1 | 01/10/2023 | 30/09/2027 | Barbara Brito Vega |