Medical image computing for next-generation healthcare technology

下一代医疗保健技术的医学图像计算

• 批准号: EP/M020533/1
• 负责人: Daniel Alexander
• 金额: $ 187.6万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FM020533%2F1
• 关键词: Medical; image; computing; next; generation

At the heart of this platform is a vision of the future of medical technology enabled by the increasing availability of rich and diverse data from large-scale data collection initiatives. The vision is to exploit this data mass to enrich sparse data acquired at point of care. For example, a single clinical MRI or ultrasound scan, together with subject-specific clinical data (age, sex, symptoms, genetics) can index a centralised data mass to infer likely diagnosis, prognosis, and treatment outcomes by matching to similar individuals about whom much more is known. The same paradigm further enables cheap and/or small and/or low-power devices able to acquire data in non-specialist locations, such as portable or hand-held scanners, or tiny imaging devices in surgical instruments. Platform funding will maintain our world-leading activities in a range of medical image computing topics, support alignment of currently distinct strands of work for mutual short-term benefit, and develop key enabling technology and demonstrators of our long-term vision. Our current work develops state of the art imaging technology, image analysis techniques, and mathematical and computational models that maximise the information contained in and derived from large data sets. We also develop a range of automated diagnostic systems and surgical support systems that can demonstrate the benefits of the data-driven paradigm.Platform funding supports the career development of the applicants, a pool of associated academic staff, talented post-doctoral researchers, and students coming through an associated EPSRC Centre for Doctoral Training in Medical Imaging. The platform provides opportunities for all to develop new ideas crossing boundaries between different topics, while contributing to a central long-term vision that supports a variety of future research careers. Competitive allocation of resource under close mentorship of senior colleagues instils early-career researchers with essential academic skills required for successful future careers. It further enhances the career progression of our senior staff by supporting opportunities to acquire new skills, establish new collaborations, explore commercialisation, and recruit the best staff from around the world when opportunities arise.

该平台的核心是对医疗技术未来的愿景，这是由大规模数据收集计划中丰富多样的数据的日益可用性所实现的。我们的愿景是利用这些数据来丰富在护理点采集的稀疏数据。例如，一个单一的临床MRI或超声扫描，连同受试者特定的临床数据（年龄，性别，症状，遗传学）可以索引一个集中的数据质量，以推断可能的诊断，预后和治疗结果，通过匹配到相似的个人，他们知道得更多。相同的范例还使得能够在非专业位置获取数据的廉价和/或小型和/或低功率设备成为可能，诸如便携式或手持式扫描仪，或者手术器械中的微小成像设备。平台资金将保持我们在一系列医学图像计算主题方面的世界领先活动，支持目前不同工作领域的协调，以实现共同的短期利益，并开发关键的使能技术和我们长期愿景的示范者。我们目前的工作开发了最先进的成像技术，图像分析技术以及数学和计算模型，这些模型最大限度地提高了包含在大型数据集中的信息。我们还开发了一系列自动化诊断系统和手术支持系统，可以展示数据驱动模式的好处。平台资金支持申请人的职业发展，一个相关的学术人员，有才华的博士后研究人员，以及来自相关EPSRC医学影像博士培训中心的学生。该平台为所有人提供了跨越不同主题之间界限的新想法的机会，同时为支持各种未来研究事业的中心长期愿景做出贡献。在资深同事的密切指导下，竞争性的资源分配为早期职业研究人员灌输了未来成功职业所需的基本学术技能。它通过支持获得新技能、建立新合作、探索商业化以及在机会出现时从世界各地招聘最优秀员工的机会，进一步促进我们高级员工的职业发展。

项目成果

Identifying and evaluating clinical subtypes of Alzheimer's disease in care electronic health records using unsupervised machine learning.

• DOI: 10.1186/s12911-021-01693-6
• 发表时间: 2021-12-08
• 期刊: BMC medical informatics and decision making
• 影响因子: 3.5
• 作者: Alexander N;Alexander DC;Barkhof F;Denaxas S
• 通讯作者: Denaxas S

SPHERIOUSLY? The challenges of estimating sphere radius non-invasively in the human brain from diffusion MRI.

• DOI: 10.1016/j.neuroimage.2021.118183
• 发表时间: 2021-08-15
• 期刊: NeuroImage
• 影响因子: 5.7
• 作者: Afzali M;Nilsson M;Palombo M;Jones DK
• 通讯作者: Jones DK

Additional file 1 of Identifying and evaluating clinical subtypes of Alzheimer's disease in care electronic health records using unsupervised machine learning

使用无监督机器学习识别和评估护理电子健康记录中阿尔茨海默病临床亚型的附加文件 1

• DOI: 10.6084/m9.figshare.17149693
• 发表时间: 2021
• 作者: Alexander N

Application of Convolutional Neural Network for Semantic Segmentation of Bandung Urban Scenes

卷积神经网络在万隆城市场景语义分割中的应用

• DOI: 10.1109/icodse56892.2022.9972006
• 发表时间: 2022
• 作者: Alexander D

Mid-space-independent deformable image registration.

• DOI: 10.1016/j.neuroimage.2017.02.055
• 发表时间: 2017-05-15
• 期刊: NeuroImage
• 影响因子: 5.7
• 作者: Aganj I;Iglesias JE;Reuter M;Sabuncu MR;Fischl B
• 通讯作者: Fischl B