3D L2S-Microscopy for Unstained Cell Clusters

未染色细胞簇的 3D L2S 显微镜

• 批准号: 498555818
• 负责人: Professor Dr. Ivo Ihrke
• 金额: --
• 依托单位: Department Elektrotechnik und Informatik
• 依托单位国家: 德国
• 项目类别: Research Units
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/498555818?language=en
• 关键词: 3D; L2S; Microscopy; Unstained; Cell

The subproject investigates time-resolved 3D microscopy as an application of the L2S paradigm, creating one of the proposed adaptive L2S sensor systems in the process.It aims at developing an experimental coded microscopy platform for 3D and time-varying 3D (=4D) microscopy of unstained cell clusters, the imaging of which is of high importance in biomedical research. We aim at creating a flexible research tool rather than a targeted microscope solution. It is envisaged that the microscopy hardware platform enables different physical coding strategies that can be completely configured by software. This, in turn, enables the application of L2S techniques for its application-specific optimization. The subproject develops all necessary hardware and software aspects for this application. For the machine learning aspects, i.e. network architecture, loss function design and training schemes a tight collaboration with the respective machine learning projects is envisaged. The fundamental research question targeted in this subproject is: Is it possible to unlock new performance regions and/or significantly expanded conditions of applicability (thick samples, combined diffraction and absorption) in time-varying 3D microscopy using specifically developed AI techniques in conjunction with hardware/software codesign?To this end, the subproject investigates 4 main topics: 1. Training Data and Ground Truth Generation, Data Acquisition, 2. Modeling, System-Simulation and Differentiable Digital Twin, 3. Microscope Construction, Calibration, and Validation, and 4. Learning to Sense techniques for 3D Refractive Index and Absorption Microscopy.Topic 2 benefits from joint activities with subproject P5 (Andreas Kolb). Topic 4 will be realized in close collaboration with subprojects P1 (Michael Möller), P2 (Margret Keuper) and P3 (Volker Blanz).

该子项目研究时间分辨3D显微镜作为L2 S范例的应用，在此过程中创建一个拟议的自适应L2 S传感器系统，旨在开发一个实验编码显微镜平台，用于未染色细胞团的3D和时变3D（=4D）显微镜，其成像在生物医学研究中非常重要。我们的目标是创建一个灵活的研究工具，而不是有针对性的显微镜解决方案。可以设想，显微镜硬件平台能够实现可以完全由软件配置的不同物理编码策略。这反过来又使L2 S技术能够应用于其特定于应用的优化。该子项目为这一应用开发所有必要的硬件和软件。对于机器学习方面，即网络架构、损失函数设计和训练方案，设想与相应的机器学习项目进行密切合作。该子项目的基本研究问题是：是否有可能使用专门开发的AI技术结合硬件/软件协同设计，在时变3D显微镜中解锁新的性能区域和/或显着扩展的适用性条件（厚样品，组合衍射和吸收）？为此，该子项目调查了4个主要议题：1。训练数据和地面实况生成，数据采集，2.建模，系统仿真和微分数字孪生，3。显微镜构造、校准和验证，以及4.学习三维折射率和吸收显微镜的检测技术。主题2受益于与子项目P5（Andreas Kolb）的联合活动。专题4将与分项目P1（Michael Möller）、P2（Margret Keuper）和P3（Volker Blanz）密切合作实现。