Spotting signs of damage in the brain: connectivity-aware white matter hyperintensity segmentation from routine MRI using deep learning

该项目旨在彻底改变我们评估大脑损伤的方式，并通过常规核磁共振扫描了解其对大脑功能的影响。尤其重要的损伤是对脑白质和大脑通讯通路的损伤。受损的白质被称为白质高信号，因为在常规MRI扫描中，它看起来比健康的白质更亮。目前的方法。目前的评估方法，即白质高强度分割，可以确定损伤的空间位置和程度。然而，这些信息对量化功能损害的性质提供的价值有限。特别是，他们不会告知支持特定大脑功能的个人沟通途径受到干扰的程度。目前，获取这些关于大脑连接的信息将需要收集常规无法获得的额外的专门MRI扫描。这个项目背后的关键思想是，我们可以通过学习现有的大规模核磁共振研究来获得这些信息，这些研究可以通过专门的核磁共振扫描来推断大脑的连接。该项目将发展这一新的连接感知白质高强度分割的想法，评估其在量化大脑功能方面的性能，并最终产生一个经过充分测试和记录的软件工具。

This project aims to revolutionise the way we assess damage in the brain and understand its impact on brain functions from routine MRI scans. The damage of particular importance is the damage to white matter, brain's communication pathways. Damaged white matter is known as white matter hyperintensity because it appears brighter than healthy white matter in routine MRI scans. Current approaches. Current approaches to assess this, known as white matter hyperintensity segmentation, identify spatial locations and extent of the damage. However, this information offers limited value for quantifying the nature of functional impairment. Particularly, they do not inform on the extent to which individual communication pathways supporting specific brain functions have been disrupted. Currently, accessing this information on brain connectivity would require collection of additional specialised MRI scans unavailable routinely. The key idea behind this project is that we can gain access to this information by learning from existing large-scale MRI studies for which specialised MRI scans are available to infer brain connectivity. This project will develop this novel idea of connectivity-aware white matter hyperintensity segmentation, evaluate its performance on quantifying brain functions, and ultimately produce a well-tested and documented software tool.