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

连接组学领域旨在从极高分辨率的显微镜图像中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则和机制。此外，这些新的见解可以加速神经退行性疾病的治疗开发，并激发新的人工智能算法。然而，仅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太多的错误，领域专家无法在神经元重建中纠正。缺少的不仅仅是一种更好的重建方法，而是一种以人为中心的方法，用于在重建之前和之后自动化劳动密集型工作流程，例如，数据注释用于训练模型，误差校正用于优化结果。该项目将构建一个可扩展的以人为中心的计算管道，采用新颖的算法来模拟人类认知，以显着减少管道中的人类工作。如果成功，开发的工作流程将被部署，以加快BRAIN Initiative雄心勃勃的全鼠大脑重建项目，从而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的数据注释，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，该项目将开发新型机器学习算法，以利用传统的密集注释的3D神经元重建之外的各种数据源。具体而言，该项目有以下目标。(1)该项目将提取未标记的数据，以学习按外观对图像进行分组，以帮助领域专家有效地发现用于注释的子体积，并将稀疏标签传播到密集重建。(2)本计画将建立自动代理，学习领域专家的校对策略，以侦测并修正自动重建的结果。(3)该项目将开发迁移学习方法，以重用标记的连接组学数据集和预训练模型，以帮助生物实验室分析其显微图像。这三个研究目标将伴随着对收集的基准数据集和生物医学图像分析社区可访问的软件资源的全面评估。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

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.