Commercial Feasibility of Deep Learning based Medical Image Registration

基于深度学习的医学图像配准的商业可行性

• 批准号: 133070
• 金额: $ 7.2万
• 依托单位: MIRADA MEDICAL LIMITED
• 依托单位国家: 英国
• 项目类别: Feasibility Studies
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=133070
• 关键词: Commercial; Feasibility; Deep; Learning; based

Various methods of imaging are used within medicine to provide different information for the diagnosis and treatment of various diseases. Image Registration is a valuable tool that establishes an alignment of images to one another and, as a result, permits meaningful comparison and visualisation of multiple images. For example, a patient’s response to treatment can be assessed by overlaying (fusing) images captured before and after treatment. Alternatively, anatomical and functional information can be displayed together, providing more information than if the two were viewed separately. For these uses and many others, such as automated quantification of certain statistics, an accurate registration is clearly important. While a number of registration schemes are currently available for use in clinical routines, this project aims to develop a new and more effective scheme.

医学中使用各种成像方法，为各种疾病的诊断和治疗提供不同的信息。图像配准是一种有价值的工具，它可以建立图像之间的对齐，从而允许对多个图像进行有意义的比较和可视化。例如，可以通过叠加（融合）治疗前后捕获的图像来评估患者对治疗的反应。另外，解剖和功能信息可以一起显示，提供比分开查看更多的信息。对于这些用途和许多其他用途，例如某些统计数据的自动量化，准确的注册显然是重要的。虽然目前有许多注册方案可用于临床常规，但本项目旨在开发一种新的更有效的方案。