A Generic Image Segmentation Platform for Novel Feature Exploration in Multimodal MR Imaging using Minimally Supervised Machine Learning

这位博士将开发新的人工智能图像分割方法，以探索多模式磁共振成像(MRI)中有意义的临床特征。我们将通过解决几个重要的成像场景来推动这一点，这些场景的特点是数据集小，图像质量具有挑战性。通常，在先进的磁共振成像中，我们可以通过视觉识别感兴趣的特征，并使用关于解剖特征和预期的MR对比度的先验知识来手动分割它。然而，这可能是非常困难和耗时的，特别是当我们对分割特征感兴趣时，例如，具有低对比度的非常小的对象，或者具有模糊的解剖结构或可变的图像对比度的对象。例如，大脑皮层的第四层是这个非常精细的连续层，贯穿大脑的大部分灰质，在进一步了解大脑功能和功能障碍方面起着关键作用。然而，很难在所有受试者中可靠地区分，目前还没有从磁共振图像中自动识别它的方法。这个博士项目的目标是：-开发机器学习方法，在最低限度的高层监督下分割感兴趣的组织(例如，形状、拓扑、连通性等)。-在适当的情况下，用基于模型的方法开发补充机器学习结果，例如，确保结果保持完整的表面而不是破碎的斑块。-优化多模式磁共振成像采集，根据初始人工智能结果，在图像质量和数据协调方面最大限度地提高自动分割的效率。

This PhD will develop novel AI approaches to image segmentation to explore meaningful clinical features in multimodal Magnetic Resonance Imaging (MRI). We will drive this by tackling several important imaging scenarios characterised by small datasets and challenging image quality. Often in advanced MRI we can visually identify a feature of interest and segment it manually with a-priori knowledge about anatomical features and expected MR contrast. However, this can be extremely difficult and time consuming, particularly when we are interested in 'difficult' to segment features, for instance very small objects with low contrast, or objects that have ill-defined anatomy or variable image contrast. For example, layer 4 of the cortex is this very fine continuous layer that runs through much of the grey matter of the brain and is pivotal in furthering our understanding of brain function and dysfunction. However, it is hard to distinguish reliably in all subjects and there are currently no methods to automatically identify it from MR images. The aim of this PhD project is to:- Develop machine learning methods to segment the tissue of interest with minimal high-level supervision (e.g. shape, topology, connectivity etc.).- Supplement the machine learning results with model-based approach development where appropriate, e.g. to ensure the results maintain a complete surface rather than broken patches.- Optimise the multimodal MRI acquisition, informed by initial AI results to maximize the efficiency of the automatic segmentation, in terms of image quality and data harmonisation.