A generative model of the diseased human brain

人类的大脑很复杂，很难建模。从神经解剖学上讲，大脑由许多感兴趣的区域组成。它们中的每一个都负责某些神经解剖功能和/或生物过程。神经解剖学家花费了大量的时间来描绘和定位健康人类大脑中这些感兴趣的区域，以手动分割的地图集的形式创建了健康人类解剖学的存储库。这些地图集，例如神经形态测量学35地图集，制作起来非常耗时——从高分辨率MRI图像中标记单个大脑可能需要长达1个月的人力工作。这些地图集然后被算法用来学习关键大脑区域的神经解剖位置，并使它们能够自动分割。算法需要大量的地图集，具有足够的可变性，以全面描述图像强度与组织分割之间的映射关系。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，这些算法受到病理的严重阻碍，因为从图像中预测分割的学习神经解剖学模式在异常组织的存在下会崩溃。为了使算法能够可靠地处理一种新的看不见的病理，人类神经解剖学家需要用这种病理重新标记足够数量的受试者。由于需要长时间的人为干预，该过程不能跨多种病理扩展；人们需要为广泛的病理描述足够数量的受试者，这样算法才能应对这种可变性。此外，人工分割是在具有一定对比度的图像上定义的，这意味着如果升级MRI机器，或者获得新的对比度，则需要重新开始相同的人工重新标记过程。简而言之，人为定义的标签并不是解决此类标签问题的可行方案，也不是可扩展的解决方案。与其要求人类分割多个图像、多个对比和多个病理，该项目建议通过使用生成对抗网络学习生成可信的人脑解剖模型（以分割的形式）来重新制定问题——即gan将从先前创建的手动分割中学习健康人体解剖的分布，并将能够生成大量解剖学上正确的分割。然后，语义网络将用于从合成分割中生成任何结构MRI图像对比，提供无限的MRI/分割对来源，以模拟人体解剖学的内在变化。之后，病理学将被引入GAN模型和语义合成模型，允许生成一组标记的人类大脑，这些大脑涵盖了大量的病理和表型，而不需要人类神经解剖学家标记任何更多的数据。通过将人体解剖学（分割）的生成模型与图像合成的语义模型解耦，该项目还将提供一种方法，通过只需要对语义网络进行再训练，将鲁棒性引入扫描仪和序列升级。这样的模型将是一项关键的使能技术，可以将人工智能支持的分割工具和成像生物标志物引入和部署到临床护理中，其中受试者的病理未知且差异很大。

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.