Synthesis and Segmentation of 3D Fluorescence Microscopy Image Data

 

Motivation

Developmental biology focuses on studying the processes of how cells migrate, interact and divide. Specifically, cell location and morphology, analysed by detection and segmentation approaches, constitute one of the key tasks in understanding the underlying principles of development. Technological advances allow biologists to capture and store unprecedented amounts of highly detailed microscopy image data, which renders the application of automated approaches essential. However, the potential of recent deep learning-based approaches is severely limited by the scarcity of fully-annotated image data sets. Consequently, this project focuses on two different research topics:

  • Robust and generalist deep learning-based instance segmentation approaches, allowing to obtain accurate results with limited available data.
  • Simulation and synthesis approaches for an automated generation of fully-annotated data sets rendering human annotation efforts obsolete.

Scientific Questions

To increase robustness and generalizability of instance segmentation approaches, new principles of deep learning-based approaches are integrated into current state-of-the-art approaches and new concepts to increase robustness are developed.Simulation and synthesis approaches with improved sampling speed and optimized control over generated structures are studied, allowing for the generation of fully-annotated image data sets on demand. Generated data and their practical usage are assessed by determining if they can replace manually annotated image data sets in segmentation training pipelines.

Partners

External Funding

  • DFG Research Grant, “On-the-fly data synthesis for deep learning-based analysis of 3D+t microscopy experiments”, Project nr. 447699143

Contact

M.Sc.
Dennis Eschweiler
 +49 241 80 22917
 Dennis.Eschweiler@lfb.rwth-aachen.de

Publications

2022

D. Eschweiler, I. Laube, J. Stegmaier
Spatiotemporal Image Generation for Embryomics Applications
In: Biomedical Image Synthesis and Simulation: Methods and Applications

2022

Dennis Eschweiler, Justus Schock and Johannes Stegmaier
Probabilistic Image Diversification to Improve Segmentation in 3D Microscopy Image Data
In: MICCAI International Workshop on Simulation and Synthesis in Medical Imaging (SASHIMI)

2021

Dennis Eschweiler, Malte Rethwisch, Mareike Jarchow, Simon Koppers and Johannes Stegmaier
3D fluorescence microscopy data synthesis for segmentation and benchmarking
In: PLOS ONE 16 (12)

2022

Dennis Eschweiler, Richard S. Smith and Johannes Stegmaier
Robust 3D Cell Segmentation: Extending the View of Cellpose
In: IEEE International Conference in Image Processing (ICIP)

2021

C. Yang, D. Eschweiler, J. Stegmaier
Semi-and Self-Supervised Multi-View Fusion of 3D Microscopy Images using Generative Adversarial Networks
In: MICCAI Workshop on Machine Learning for Medical Image Reconstruction (MLMIR)

2021

Dennis Eschweiler, Malte Rethwisch, Simon Koppers and Johannes Stegmaier
Spherical Harmonics for Shape-Constrained 3D Cell Segmentation
In: IEEE International Symposium on Biomedical Imaging (ISBI)

2021

D. Bähr, D. Eschweiler, A. Bhattacharyya, D. Moreno-Andrés, W. Antonin and J. Stegmaier
CellCycleGAN: Spatiotemporal Microscopy Image Synthesis of Cell Populations using Statistical Shape Models and Conditional GANs
In: IEEE International Symposium on Biomedical Imaging (ISBI)

2019

Dennis Eschweiler, Tim Klose, Florian Nicolas Müller-Fouarge, Marcin Kopaczka, Johannes Stegmaier
Towards Annotation-Free Segmentation of Fluorescently Labeled Cell Membranes in Confocal Microscopy Images
In: MICCAI International Workshop on Simulation and Synthesis in Medical Imaging (SASHIMI)

2019

Dennis Eschweiler, Thiago V. Spina, Rohan C. Choudhury, Elliot Meyerowitz, Alexandre Cunha, Johannes Stegmaier
CNN-based Preprocessing to Optimize Watershed-based Cell Segmentation in 3D Confocal Microscopy Images
In: IEEE International Symposium on Biomedical Imaging (ISBI)