Developing next-generation, AI-enabled, medical image processing for multiple sclerosis clinical trials and routine care.

项目背景：多发性硬化症（MS）是一种致残疾病，导致英国年轻人残疾，每年花费超过30亿英镑。从大脑和脊髓中提取的图像可以告诉我们病人的当前状态以及他们可能如何演变。目前用于测量脑和脊髓变化的图像处理工具耗时，需要昂贵且多步骤的管道，并且依赖于高质量数据的可用性，而这些数据在现实世界的临床实践中经常缺失。该候选人将使用计算机视觉领域的最新进展开发一套图像处理工具，以分割大脑MRI扫描，脊髓MRI，并使用来自英国各地临床试验和医院的数据检测这些结构随时间的变化。该项目将使用女王广场多发性硬化症中心独特的临床试验数据集，来自英国各地8多家医院的MRI数据，并能够与我们的合作者（IXICO）一起测试其在行业临床试验中的应用潜力。这是学术界和工业界之间开发下一代人工智能算法以影响药物开发和患者护理的独特机会。研究目标：-开发基于深度学习的图像模拟器，为大脑和脊髓MRI提供高效和半监督的图像处理（一年级和二年级）-开发超高效的图像分割模型，用于使用纵向图像处理检测多发性硬化症的疾病活动和疾病进展（二年级和三年级）-在临床试验中验证开发的模型和来自我们的合作伙伴医院（UCLH信托/国家神经病学医院）的真实世界数据该项目的结果将是一个独特的模型，将接受大脑或脊髓NHS MRI，并将输出相关结构的体积。

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 UKThe 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.