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白色物质的损害，这是大脑的交流途径。受损的白色物质被称为白色物质高信号，因为它在常规MRI扫描中比健康的白色物质更亮。目前的做法。目前的评估方法，称为白色物质高强度分割，确定空间位置和损害的程度。然而，这一信息提供了有限的价值量化功能障碍的性质。特别是，它们没有说明支持特定大脑功能的个体通信途径被破坏的程度。目前，要获得这些关于大脑连接的信息，需要收集常规无法获得的额外专业MRI扫描。该项目背后的关键思想是，我们可以通过从现有的大规模MRI研究中学习来获取这些信息，其中专门的MRI扫描可用于推断大脑连接。该项目将开发这种连接感知白色高强度分割的新想法，评估其量化大脑功能的性能，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.