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

职业：利用以人为本的机器学习扩大脑回路重建

• 批准号: 2239688
• 负责人: Donglai Wei
• 金额: $ 60万
• 依托单位: Boston College
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2028-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239688&HistoricalAwards=false
• 关键词: CAREER; Scaling; up; Brain; Circuit

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.

连接组的领域旨在通过极高的分辨率显微镜图像重建大脑各个部分之间的连接。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，以揭示组织原理和神经元连接的机制。此外，这些新见解可以加速神经退行性疾病的治疗发展并激发新型的AI算法。但是，仅单毫米立方体大脑区域的连接图像数据处于PB尺度上，现有的计算管道会产生太多错误，供域专家在神经元重建中纠正。缺少的不仅是一种更好的重建方法，而且是一种以人为中心的方法来自动化重建之前和之后的劳动密集型工作流程，例如，数据注释来训练模型和误差校正以完善结果。该项目将通过新颖的算法建立可扩展的以人为中心的计算管道，以模仿人类认知，从而大大减少人类的努力。如果成功的话，将部署开发的工作流程，以加快大脑倡议的雄心勃勃的全小鼠重建项目，以彻底改变对大脑的理解。该项目将集中于加速劳动密集型的数据注释，校对和机器学习管道中转移学习的工作流。受到人类认知能力的启发，该项目将开发新型的机器学习算法，以探索超出传统的通道带注释的3D神经元重建以外的各种数据源。具体而言，该项目具有以下目标。 （1）该项目将提取未标记的数据，以学习通过外观进行分组图像，以帮助域专家有效地发现注释和传播稀疏标签以致密重建的子量。 （2）该项目将建立自动代理，以从域专家的校对策略中学习，以检测和纠正自动重建结果。 （3）该项目将开发转移学习方法，以重复使用标记的连接数据集和预训练的模型，以帮助生物学实验室分析其显微镜图像。这三项研究的目标将通过对生物医学图像分析社区的基准数据集和可访问的软件资源进行全面评估来实现。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响标准来评估NSF的法定任务。