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

使用最小监督机器学习进行多模态 MR 成像新特征探索的通用图像分割平台

• 批准号: 2749493
• 金额: --
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2749493
• 关键词: Generic; Image; Segmentation; Platform; Novel

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.

该博士将开发新的人工智能图像分割方法，以探索多模态磁共振成像（MRI）中有意义的临床特征。我们将通过解决以小数据集和具有挑战性的图像质量为特征的几个重要成像场景来推动这一目标。通常在高级MRI中，我们可以直观地识别感兴趣的特征，并通过先验的解剖学特征和期望的MR对比度知识手动分割它。然而，这可能是非常困难和耗时的，特别是当我们对“难以”分割的特征感兴趣时，例如，非常小的物体具有低对比度，或者具有不明确的解剖结构或可变图像对比度的物体。例如，皮层的第四层是非常精细的连续层，它贯穿了大脑的大部分灰质，对我们进一步了解大脑功能和功能障碍至关重要。然而，很难在所有受试者中进行可靠的区分，目前还没有从MR图像中自动识别它的方法。这个博士项目的目的是：-开发机器学习方法，以最小的高层监督（例如形状，拓扑，连通性等）分割感兴趣的组织。-在适当的地方用基于模型的方法开发来补充机器学习结果，例如，确保结果保持完整的表面而不是破碎的补丁。根据初始人工智能结果优化多模态MRI采集，在图像质量和数据协调方面最大化自动分割的效率。