Hierarchical Deep Representations of Anatomy (HiDRA)

了解小鼠等动物模型中基因的功能有助于研究人员了解它们在人类疾病中的作用。高通量表型分析被用来通过定性和定量相结合的方法对基因功能进行广泛的评估，这些分析试图测量或可视化特定的解剖结构或器官系统。它对于理解基因-表型之间的关系是至关重要的，并且已经指导了针对发育、心血管、神经退行性和感觉障碍的治疗靶点的开发。从公共卫生的角度来看，骨骼表型尤其重要，在英国，肌肉骨骼疾病占所有全科医生就诊的12%，花费1080万个工作日，每年向NHS支付约47亿GB。由于骨骼的解剖学复杂性和人们可能观察到的潜在异常的范围，对骨骼进行广泛的评估特别费力和主观。在小鼠的表型鉴定中，普通的X射线图像通常是从多个视角、方向和尺度获取的，以确保完全覆盖整个动物。表型是通过领域专家的手动检查来确定的，这在规模上执行起来非常耗时。国际表型鉴定联盟(IMPC)由八个机构组成，它们收集老鼠的X射线图像，并对影响头骨、牙齿、肋骨、脊柱、骨盆和四肢的52种不同表型进行注释。这是一项艰巨的任务，到目前为止，从34,000只动物中生成了大约166,000张带注释的图像。然而，这一组数据只代表了迄今为止IMPC表型鉴定的20,000个基因中的7,500个。人工注释的瓶颈对该项目来说是一个令人望而生畏的前景，代表着生命科学界对自动图像分析方法的需求尚未得到满足。近年来，卷积神经网络(CNN)因其对广泛的图像分类问题的普遍适用性而备受关注。这在一定程度上是因为它们几乎不需要先前的领域知识来实现，并且数据只需要最小的预处理顺序就可以实现最先进的性能。然而，仍然存在一些挑战，阻碍了它们在大规模表型鉴定中的采用。传统的CNN不适合输入图像数量可变的数据集；在实践中，单个动物可能从一个或六个不同的视角捕获。此外，被训练为一次执行多个任务的CNN(即，一个动物可能表现出多种表型)由于解剖上的接近而对任务之间的关系几乎没有欣赏或了解。除了CNN用于自动化的直接用例之外，还有机会利用CNN学习的内部表示来执行大规模数据挖掘和支持生物发现。HiDRA(解剖学的分层深度表示)将通过开发一种对输入数据的变化具有健壮性、在与解剖学相关的任务之间共享信息并学习针对单个动物的解剖特定特征表示的方法来解决这些挑战。来自多个视点的信息融合将允许提供任意数量的图像，并有助于指示哪个视点对于注释最具信息性。为了解决个体表型的相对稀缺性，将开发一种分层培训计划，根据解剖邻近程度在相关任务中共享信息。学习到的表示也将受到限制，将解剖结构之间的相关性降至最低，从而允许使用数据挖掘技术以特定于解剖学的方式在动物之间进行比较。这项研究的成果之一将包括向更广泛的生命科学界提供的计算工具，用于分析任何尺度的X射线数据。

Understanding the functions of genes in animal models such as mice allows researchers to learn about their roles in human disease. High-throughput phenotyping is used to conduct broad assessments of gene function through a combination of qualitative and quantitative assays, which seek to measure or visualise specific anatomical structures or organ systems. It is essential to understanding genotype-phenotype relationships and has guided the development of therapeutic targets for developmental, cardiovascular, neurodegenerative, and sensory disorders. Skeletal phenotyping is particularly crucial from a public health standpoint, with musculoskeletal disorders being responsible for 12% of all general practitioner visits in the UK at a cost of 10.8 million working days and some £4.7 billion to the NHS per year.Broad assessments of the skeleton are particularly laborious and subjective due to its anatomical complexity and the range of potential anomalies one might observe. In mouse phenotyping, plain x-ray images are routinely acquired from multiple viewpoints, orientations and scales to ensure complete coverage of the whole animal. Phenotypes are identified through manual inspection by domain experts, which is prohibitively time-consuming to perform at scale. The International Phenotyping Consortium (IMPC) comprises eight institutions that collect x-ray images of mice and annotate up 52 different phenotypes affecting skull, teeth, ribs, spine, pelvis and limbs. This represents a monumental task, generating some 166,000 annotated images from 34,000 animals to date. However, this body of data only represents 7,500 of the 20,000 genes phenotyped so far by the IMPC. The bottleneck of manual annotation is a daunting prospect for the project and represents an unmet need for automated image analysis methods within the life sciences community. In recent years, convolutional neural networks (CNNs) have risen to prominence for their seemingly universal applicability to a wide range of image classification problems. This partly due to the fact they require little prior domain knowledge to implement, and the data require minimal pre-processing order to achieve state-of-the-art performance. However, there are nevertheless challenges that preclude their adoption for large-scale phenotyping. Traditional CNNs are not naturally suited to datasets where the number of input images is variable; an individual animal may be captured from one and up six different viewpoints in practice. Furthermore, CNNs trained to perform multiple tasks at once (i.e., one animal may exhibit multiple phenotypes) have little appreciation or knowledge of the relationships between the tasks due to anatomical proximity. Beyond the immediate use-case of CNNs for automation, there is an opportunity to leverage the internal representations learned by CNNs to perform large-scale data mining and support biological discovery.HiDRA (Hierarchical Deep Representations of Anatomy) will address these challenges by developing a "multi-view-multi-task" approach that is robust to variations in the input data, shares information between anatomically-related tasks, and learns anatomy-specific feature representations for individual animals. Information fusion from multiple viewpoints will allow for any number of images to be provided and help to indicate which view was most informative for annotation. To account for the relative scarcity of individual phenotypes, a hierarchical training scheme will be developed to share information across related tasks according to anatomical proximity. The learned representations will also be subject to constraints that minimise correlations between anatomical structures, allowing for comparisons to be made between animals in an anatomy-specific fashion using data mining techniques. Among the outputs of this research will include computational tools made available to the wider life sciences community for analysis of x-ray data at any scale.