Hierarchical Deep Representations of Anatomy (HiDRA)

了解小鼠等动物模型中基因的功能，使研究人员能够了解它们在人类疾病中的作用。高通量表型分析用于通过结合定性和定量分析对基因功能进行广泛评估，旨在测量或可视化特定的解剖结构或器官系统。了解基因型与表型之间的关系至关重要，并指导了发育、心血管、神经退行性和感觉疾病治疗靶点的发展。从公共卫生的角度来看，骨骼表型尤为重要，在英国，肌肉骨骼疾病占全科医生就诊总量的12%，花费1080万个工作日，每年给NHS带来约47亿英镑的损失。由于其解剖复杂性和可能观察到的潜在异常范围，对骨骼的广泛评估特别费力和主观。在小鼠表型分析中，通常从多个视点、方向和尺度获取普通x射线图像，以确保完全覆盖整个动物。表型是通过领域专家的手工检查来确定的，这在大规模执行时非常耗时。国际表型联盟（IMPC）由八家机构组成，他们收集小鼠的x射线图像，并注释了影响头骨、牙齿、肋骨、脊柱、骨盆和四肢的52种不同表型。这是一项艰巨的任务，迄今为止，从34000只动物中产生了大约16.6万张带注释的图像。然而，到目前为止，这些数据只代表了IMPC表型化的2万个基因中的7500个。手工注释的瓶颈是该项目令人望而生畏的前景，并且代表了生命科学界对自动图像分析方法的未满足需求。近年来，卷积神经网络（cnn）因其似乎普遍适用于广泛的图像分类问题而备受关注。这部分是由于它们需要很少的先验领域知识来实现，并且数据需要最少的预处理顺序来实现最先进的性能。然而，仍然存在阻碍它们大规模表型化的挑战。传统的cnn自然不适合输入图像数量可变的数据集；在实践中，一只单独的动物可能从一个到六个不同的角度被捕获。此外，cnn被训练一次执行多个任务（即，一个动物可能表现出多种表型），由于解剖上的接近，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（分层解剖深度表征）将通过开发一种“多视图多任务”方法来解决这些挑战，该方法对输入数据的变化具有鲁棒性，在解剖相关任务之间共享信息，并学习个体动物的解剖特定特征表征。来自多个视点的信息融合将允许提供任意数量的图像，并有助于指出哪个视图对注释提供的信息最多。为了解释个体表型的相对稀缺性，将开发分层训练方案，根据解剖接近度在相关任务之间共享信息。学习到的表征也将受到约束，使解剖结构之间的相关性最小化，允许使用数据挖掘技术以特定解剖结构的方式在动物之间进行比较。这项研究的产出将包括为更广泛的生命科学界提供的计算工具，用于分析任何规模的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.