Commercial Feasibility of Deep Learning based Medical Image Registration

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。图像配准是一个有价值的工具，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，患者对治疗的反应可以通过叠加（融合）在治疗之前和之后捕获的图像来评估。或者，解剖和功能信息可以一起显示，提供比单独查看两者更多的信息。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，准确的登记显然很重要。虽然目前有许多注册计划可用于临床常规，但本项目旨在开发一种新的更有效的计划。

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.