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MRI扫描了解其对大脑功能的影响。特别重要的损害是对大脑沟通通路白质的损害。受损的白质被称为白质高强度，因为在常规MRI扫描中，它比健康的白质更亮。当前的方法。目前评估这种情况的方法被称为白质高强度分割，它可以确定损伤的空间位置和程度。然而，这一信息对量化功能损伤的性质提供了有限的价值。特别是，它们并没有说明支持特定大脑功能的个人交流途径在多大程度上被破坏了。目前，获取大脑连接的这些信息需要收集额外的专门的MRI扫描，这些扫描通常是不可用的。这个项目背后的关键思想是，我们可以通过从现有的大规模核磁共振研究中学习来获取这些信息，这些研究可以通过专门的核磁共振扫描来推断大脑的连通性。该项目将开发这种连接感知白质高强度分割的新想法，评估其在量化大脑功能方面的表现，并最终产生一个经过良好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.