An easy-to-use software for 3D behavioral tracking from multi-view cameras

易于使用的软件，用于通过多视图摄像机进行 3D 行为跟踪

• 批准号: 10609129
• 负责人: Bence P Olveczky
• 金额: $ 25.35万
• 依托单位: HARVARD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10609129
• 关键词: 3-Dimensional; Address; Administrative Supplement; Adopted; Adoption; Affect; Algorithms; Animal Model; Animals; Architecture; Behavior; Behavioral; Brain; Cloud Computing; Communities; Complex; Computer software; Data; Data Set; Development; Dimensions; Disease; Dystonia; Educational workshop; Ensure; Environment; Ethology; Geometry; Goals; Grant; High Performance Computing; Image; Infrastructure; Institutes; Intuition; Lead; Learning; Libraries; Light; Measurement; Memory; Methods; Modeling; Monitor; Movement; Names; Nervous System Physiology; Parents; Parkinson Disease; Performance; Positioning Attribute; Process; Provider; Publications; Pythons; Readiness; Reproducibility; Research; Research Personnel; Resolution; Resources; Retinal Degeneration; Running; Software Engineering; Stroke; System; Techniques; Technology; Time; Training; United States National Institutes of Health; Work; appendage; artificial neural network; autism spectrum disorder; body position; cluster computing; computational platform; computing resources; cost; data format; data standards; deep learning algorithm; deep neural network; experience; flexibility; graphical user interface; improved; innovation; interest; kinematics; motor control; new technology; open source; parent grant; programs; relating to nervous system; spatiotemporal; tool; usability; user-friendly

Project Summary The overarching goal of this administrative supplement is to increase the adoption and utilization an exciting new technology for 3D pose tracking, DANNCE, that we are developing in the parent R01 (R01GM136972). DANNCE uses a deep neural network to predict pose and appendage positions from multi-view video of behaving animals, achieving state-of-the-art performance across a variety of species and environments. While several labs have already adopted this technology to study how the brain generates movement and to probe the mechanisms underlying a variety of disorders, its wider use is hampered by a siloed, unintegrated software that fails to conform to best software engineering practices. This leads to bottlenecks and errors for non-expert users, severely limiting the reach and applicability of our transformative technology. Even for expert users, the current software is slow to use, difficult to optimize for specific applications, and burdensome to maintain. This also impedes contributions and feature development by the open-source community. Furthermore, our tool has high compute demands that further constrain its use to labs and institutes with access to high-performance computing infrastructure. To improve the scalability, useability, and robustness of our technology and to allow for its dissemination to a wider research community, we will work with professional software engineers to implement best software engineering and community open-source development practices. Specifically, we will address the following aims. 1. To eliminate system bottlenecks, we will significantly refactor and improve the software to integrate all components into a common backend Python library with community standard data formats, thus eliminating common pitfalls, and build a modular and flexible platform that can be easily extended and augmented. 2. To ensure that non-experts can easily adopt our system and explore the large-scale behavioral data it generates, we will create an easy-to-use, centralized graphical user interface. 3. Lastly, to enhance cloud readiness and the utilization of a wide range of hardware accelerators, we will rewrite the backend integration to allow users to train and run our system at scale on different computing platforms, both on-premise high-performance computing clusters and commercial cloud providers. By streamlining and improving the software package underlying our state-of the art method, we will dramatically increase its usability, flexibility, and scalability, thus accelerating many ongoing research programs and removing a main barrier for initiating many others.

项目概要 该行政补充的总体目标是增加令人兴奋的新产品的采用和利用 3D 姿态跟踪技术 DANNCE，我们正在母版 R01 (R01GM136972) 中开发。舞蹈 使用深度神经网络从行为动物的多视图视频中预测姿势和附肢位置， 在各种物种和环境中实现最先进的性能。虽然一些实验室已经 已经采用这项技术来研究大脑如何产生运动并探究其机制 由于存在多种疾病，其广泛使用受到孤立的、未集成的软件的阻碍，该软件不符合 最佳软件工程实践。这会给非专家用户带来严重的瓶颈和错误 限制了我们变革性技术的范围和适用性。即使对于专家用户来说，当前的软件也是如此 使用缓慢，难以针对特定应用程序进行优化，并且维护起来很麻烦。这也阻碍了 开源社区的贡献和功能开发。此外，我们的工具具有高计算能力 要求进一步限制其在能够获得高性能计算的实验室和机构中的使用 基础设施。提高我们技术的可扩展性、可用性和稳健性，并允许其 传播到更广泛的研究社区，我们将与专业的软件工程师合作实施 最佳软件工程和社区开源开发实践。具体来说，我们将解决 以下目标。 1. 为了消除系统瓶颈，我们将大幅重构和改进软件以集成所​​有系统 将组件集成到具有社区标准数据格式的公共后端 Python 库中，从而消除 常见的陷阱，并构建一个可以轻松扩展和增强的模块化、灵活的平台。 2. 确保非专家也能轻松采用我们的系统并探索其大规模行为数据 生成后，我们将创建一个易于使用的集中式图形用户界面。 3. 最后，为了增强云就绪性和各种硬件加速器的利用率，我们将 重写后端集成，以允许用户在不同的计算上大规模训练和运行我们的系统 平台，包括本地高性能计算集群和商业云提供商。 通过简化和改进我们最先进方法背后的软件包，我们将 显着提高其可用性、灵活性和可扩展性，从而加速许多正在进行的研究项目 并消除发起许多其他活动的主要障碍。