CAREER: Scaling up Brain Circuit Reconstruction with Human-centric Machine Learning

连接组学旨在通过极高分辨率的显微镜图像重建大脑各部分之间的连接。这种变革性的方法可以在细胞水平上提供大脑的详细渲染，以揭示神经连接的组织原理和机制。此外，这些新的见解可以加速神经退行性疾病的治疗发展，并激发新的人工智能算法。然而，仅一毫米立方体大脑区域的连接组学图像数据是pb级的，现有的计算管道产生了太多的错误，领域专家在神经元重建中无法纠正。我们缺少的不仅仅是更好的重建方法，而是一种以人为中心的方法来自动化重建前后的劳动密集型工作流程，例如，通过数据注释来训练模型，通过错误纠正来改进结果。该项目将建立一个可扩展的以人为中心的计算管道，使用新颖的算法来模拟人类的认知，从而显着减少人工在管道中的工作量。如果成功，开发的工作流程将用于加速BRAIN计划雄心勃勃的全鼠大脑重建项目，以彻底改变对大脑的理解。该项目将专注于加速机器学习管道中数据注释、校对和迁移学习的劳动密集型工作流程。受人类认知能力的启发，该项目将开发新的机器学习算法，以利用传统密集注释的3D神经元重建之外的各种数据源。具体来说，本项目有以下几个目的。(1)本项目将对未标注的数据进行提取，学习按外观对图像进行分组，协助领域专家有效发现子卷进行标注，并将稀疏标签传播到密集重建。(2)本项目将构建自动代理，学习领域专家的校对策略，对自动重构结果进行检测和纠正。(3)本项目将开发迁移学习方法，以重用标记连接组学数据集和预训练模型，以协助生物学实验室分析其显微镜图像。这三个研究目标将伴随着对收集的基准数据集和可访问的生物医学图像分析社区软件资源的综合评估。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The field of connectomics aims to reconstruct the connections between various parts of the brain from extremely high resolution microscopy images. Such a transformative approach can provide detailed renderings of the brain at the cellular level to reveal the organizing principle and the mechanism of neural connectivities. Furthermore, these new insights could accelerate the treatment development for neurodegenerative diseases and inspire novel AI algorithms. However, the connectomics image data of a mere one-millimeter cube brain region is on the petabyte scale, where existing computational pipelines produce too many errors for domain experts to correct in neuron reconstruction. What is missing is not just a better reconstruction method but a human-centric approach to automate the labor-intensive workflows before and after the reconstruction, e.g., data annotation to train the model and error correction to refine the results. This project will build a scalable human-centric computational pipeline with novel algorithms to mimic human cognition to reduce human effort in the pipeline significantly. If successful, the developed workflows will be deployed to expedite the BRAIN Initiative’s ambitious whole-mouse brain reconstruction project to revolutionize the understanding of the brain. This project will focus on accelerating the labor-intensive workflows of data annotation, proofreading, and transfer learning in the machine learning pipeline. Inspired by human cognitive abilities, this project will develop novel machine learning algorithms to exploit various data sources beyond the traditional densely annotated 3D neuron reconstruction. Concretely, this project has the following aims. (1) This project will distill the unlabeled data to learn to group images by appearance to assist domain experts in effectively discovering sub-volumes for annotation and propagating sparse labels to dense reconstruction. (2) This project will build automatic agents to learn from domain experts’ proofreading strategies to detect and correct the automatic reconstruction results. (3) This project will develop transfer learning methods to reuse labeled connectomics datasets and pre-trained models to assist biology labs in analyzing their microscopy images. These three research aims will be accompanied by comprehensive evaluations on collected benchmark datasets and accessible software resources for the biomedical image analysis community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.