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A generative model of the diseased human brain

患病人脑的生成模型

基本信息

批准号：
2271386
负责人：
金额：
--
依托单位：
King's College London
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2019
资助国家：
英国
起止时间：
2019 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2271386
关键词：
generative model diseased human brain

项目摘要

The human brain is complex and hard to model. Neuroanatomically, the brain is comprised of many regions of interest. Each of these is responsible for certain neuroanatomical functions and/or biological processes. Neuroanatomists have spent a tremendous amount of time delineating and localising these regions of interest in heathy human brains, creating a repository of heathy human anatomy in the form of manually segmented atlases. These atlases, for example the Neuromorphometrics 35 atlas, are time consuming to create - it can take up to 1 month of human work to label a single brain from a high-resolution MRI image.These atlases are then used by algorithms to learn the neuroanatomical location of key brain regions, and enable their automatically segmentation. Algorithms require large number of atlases, with sufficient variability to comprehensively describe the mapping between image intensity and tissue segmentation. Thus, the same time-consuming process needs to be repeated in very large numbers to enable algorithms to perform accurately. Unfortunately, these algorithms are severely hampered by the presence of pathology, as the learned neuroanatomical patter that predicts segmentations from images breaks down in the presence of abnormal tissues. In order for the algorithms to reliably cope with a new type of unseen pathology, human neuroanatomists are required to relabel a sufficient number of subjects with this pathology. Due to the need of lengthy human intervention, this process is not scalable across multiple pathologies; one would need to delineate a sufficient number of subjects for a wide range of pathologies so that algorithms can cope with such variability. Furthermore, manual segmentations are defined on images with certain contrasts, meaning that if the MRI machine is upgraded, or a new contrast is acquired, the same process of human relabelling needs to start all over again. In short, human-defined labels are not a feasible, nor scalable, solution to such labelling problems.Rather than asking humans to segment multiple images, with multiple contrasts, and with multiple pathologies, this project proposes to reformulate the problem by learning to generate plausible human brain anatomical models (in the form of segmentations) using generative adversarial networks - i.e. GANs will learn the distribution of healthy human anatomy from previously-created manual segmentation, and will be able to generate a multitude of segmentations that are anatomically correct. A semantic network will then be used to generate any structural MRI image contrast from the synthetic segmentations, providing an unlimited source of MRI/segmentation pairs that model the intrinsic variation of human anatomy. Afterwards, pathology will be introduced into both the GAN model and the semantic synthesis model, allowing the generation of a set of labelled human brains that cover a large amount of pathologies and phenotypes without the need for a human neuroanatomist to label any more data. By decoupling the generative model of human anatomy (segmentations) from the semantic model of image synthesis, this project will also provide a way to introduce robustness to scanner and sequence upgrades, by requiring only the retraining of the semantic network.Such a model would be a key enabling technology that would allow the introduction and deployment of AI-enabled segmentation tools and imaging biomarkers into clinical care, where subject's pathology is unknown and widely varying.

人类的大脑是复杂的，很难建模。神经解剖学上，大脑由许多感兴趣的区域组成。每一个都负责某些神经解剖功能和/或生物过程。神经解剖学家花了大量的时间来描绘和定位健康人类大脑中的这些感兴趣的区域，以手动分割地图集的形式创建健康人类解剖学的知识库。这些图谱，例如Neuromorphometrics 35图谱，创建起来非常耗时--从高分辨率MRI图像中标记单个大脑可能需要长达1个月的人工工作。然后算法使用这些图谱来学习关键大脑区域的神经解剖位置，并实现其自动分割。算法需要大量的图谱，具有足够的可变性，以全面描述图像强度和组织分割之间的映射。因此，同样耗时的过程需要大量重复，以使算法能够准确地执行。不幸的是，这些算法受到病理学存在的严重阻碍，因为在存在异常组织的情况下，预测图像分割的学习神经解剖模式会崩溃。为了使算法能够可靠地科普一种新型的看不见的病理，人类神经解剖学家需要重新标记足够数量的具有这种病理的受试者。由于需要长时间的人为干预，该过程不能跨多种病理进行扩展;需要为广泛的病理描绘足够数量的受试者，使得算法可以科普这种变化。此外，手动分割是在具有特定对比度的图像上定义的，这意味着如果MRI机器升级或获取新的对比度，则需要重新开始相同的人工重新标记过程。简而言之，人类定义的标签不是解决这些标签问题的可行的，也不是可扩展的解决方案。该项目提出通过学习生成合理的人脑解剖模型来重新表述问题（以分割的形式）使用生成对抗网络-即GAN将从先前创建的手动分割中学习健康人体解剖结构的分布，并且将能够生成解剖学上正确的多个分割。然后，语义网络将用于从合成分割生成任何结构MRI图像对比度，提供对人体解剖结构的内在变化进行建模的MRI/分割对的无限来源。之后，病理学将被引入到GAN模型和语义合成模型中，从而允许生成一组标记的人类大脑，这些大脑覆盖了大量的病理学和表型，而不需要人类神经解剖学家标记任何更多的数据。通过解耦人体解剖学的生成模型该项目还将提供一种方法，只需重新训练语义网络，即可为扫描仪和序列升级引入鲁棒性。这样的模型将是一项关键的使能技术，允许引入和部署人工智能支持的分割工具和成像生物标志物进入临床护理，其中受试者的病理学是未知的并且变化很大。