3D L2S-Microscopy for Unstained Cell Clusters

该子项目将时间分辨3D显微镜作为L2S范例的应用进行研究，并在此过程中创建一种拟议的自适应L2S传感器系统。该项目旨在开发一个实验编码显微镜平台，用于对生物医学研究中具有重要意义的未染色细胞团进行三维和时变三维（=4D）显微镜观察。我们的目标是创造一个灵活的研究工具，而不是一个有针对性的显微镜解决方案。设想显微镜硬件平台可以实现不同的物理编码策略，这些策略可以通过软件完全配置。这反过来又使L2S技术能够用于特定于应用程序的优化。子项目为该应用程序开发所有必要的硬件和软件方面。对于机器学习方面，即网络架构，损失函数设计和训练方案，设想与各自的机器学习项目紧密合作。本子项目的基本研究问题是：是否有可能利用专门开发的人工智能技术结合硬件/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，子课题研究了四个主要课题：1.；训练数据与地面真值生成，数据采集，2。建模、系统仿真与可微数字孪生，3。3 .显微镜的构建、校准和验证；学习感知技术的3D折射率和吸收显微镜。主题2受益于与子项目P5 （Andreas Kolb）的联合活动。主题4将与子项目P1 （Michael Möller）、P2 （margaret Keuper）和P3 （Volker Blanz）密切合作实现。

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).