Mirja Granfors, Alex Lech, Jiacheng Huang, Eduard Duta, Guillem Guigo i Corominas, Harshith Bachimanchi, Benjamin Midtvedt, Jesús Pineda, Carlo Manzo, and Giovanni Volpe
Date: 7 October 2026
Time: 11:45 – 13:00 and 17:20 – 19:00
Place: Biblioteket, Arbetets museum, Norrköping
Deep learning has become an increasingly powerful tool for microscopy image analysis, but training robust models often requires large and diverse datasets that are difficult and time-consuming to acquire experimentally. Physics-based simulations provide a way to generate synthetic microscopy data while retaining control over the optical and physical properties of the imaging process.
DeepTrack2 is a flexible and scalable Python library designed to generate physics-based synthetic microscopy datasets for training deep learning models. It supports a wide range of imaging modalities, including brightfield, fluorescence, darkfield, and holography, and allows users to customize optical systems, incorporate optical aberrations and noise, simulate diverse objects and imaging conditions, and apply image augmentations.
Its modular architecture also enables simulations to be executed using a PyTorch backend, providing GPU acceleration and allowing backpropagation through the optical system. This makes it possible to optimize the optical setup and the deep learning model jointly. The DeepTrack2 repository further provides tutorials covering the complete workflow, from physics-based simulation to deep-learning-based analysis using its companion library, deeplay.
Together, these capabilities provide a flexible framework for developing and benchmarking deep learning methods for microscopy while reducing the need for large experimentally acquired training datasets.
