EF3 – Model-Based Imaging



Model-Based 4D Reconstruction of Subcellular Structures

Project Heads

Peter Hiesinger, Max von Kleist, Steffen Prohaska, Martin Weiser

Project Members

David Knötel (ZIB)

Project Duration

01.01.2019 – 31.12.2021

Located at



The brain development of flies (Drosophila) is being observed through 4D (3D + time) 2-photon microscopy. The reconstruction of subcellular structures, here filopodia sprouting from a growth cone, is a challenging task currently done semi-automatically with huge manual effort [1]. We aim at reducing the required human labour by using quantitative models of growth cone and filopodia geometry and dynamics [2] for a more robust and consistent algorithmic identification of subcellular structures. Some of the used methods comprise Bayesian inference, optimization, segmentation using convolutional neural networks, and stochastic modelling.

Data description

  • Brain development of flies (Drosophila) is being observed through 4D (3D + time) 2-photon microscopy
  • 60 timesteps over 1h including multiple growth cones
  • Filopodia are attached to a growth cone



Dramatic increase of image analysis throughput by model-based filopodia segmentation and tracking compared to semi-automatic reconstruction algorithm previously created at ZIB.

Combining microscopy data and growth dynamics models into a model based filopodia reconstruction loop.


Main framework

The goal is to use Bayesian inference methods for filopodia reconstruction and tracking that can respect user-provided constraints. Here, the prior compares a potential filopodia model to previously known filopodial length dynamics while the likelihood term compares the model to the dataset at hand.

Stochastic models based on reconstructed data (using the semi-automatic algorithm) are available for filopodial growth and for synapse formation. In this project, only the growth dynamics are relevant and the conditional length distribution is modeled as a sum of Laplace distributions with an exponential vanishing probability (dependent on the length in previous timestep). For the likelihood computation, we first compute a filopodia probability field using a U-Net based Deep-CNN semantic segmentation method. The training data is created using a method based on region growing that transforms the reconstructions from the semi-automatic algorithm (given as piecewise linear lines in 3D space for one timestep) into voxelized data. In a following step, we fit filopodia curves into the probability fields by optimizing an active contour based energy functional.

In a side project, we aim at classifying the endpoints of filopodia. Again, there is training data available und we therefore apply a neural network for 3D image classification.

Selected Publications

[1] J. Brummer, V.J. Dercksen, M.N. Ozel, A. Kulkarni, S. Prohaska, D. Baum, P.R. Hiesinger. Semi-automatic Reconstruction and Analysis of Filopodia Dynamics in 4D 2-Photon Microscopy Images. In preparation, 2020.

[2] M.N. Özel, A. Kulkarni, A. Hasan, J. Brummer, M. Moldenhauer, I.M. Daumann et al. Serial synapse formation through filopodial competition for synaptic seeding factors. Developmental cell, 50(4):447–461, 2019.

Selected Pictures

Transparent surface visualization of a growth cone and corresponding axon (on the left side) for one timestep. Filopodia reconstructions are represented as colored lines, e.g. the red line in the top part.

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