Project
EF3-2
Model-Based 4D Reconstruction of Subcellular Structures
Project Heads
Peter Hiesinger, Max von Kleist, Steffen Prohaska, Martin Weiser
Project Members
David Knötel (ZIB, until 03/21)
Project Duration
01.01.2019 – 31.12.2021
Located at
ZIB
Description
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
Goal
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.Please insert any kind of pictures (photos, diagramms, simulations, graphics) related to the project in the above right field (Image with Text), by choosing the green plus image on top of the text editor. (You will be directed to the media library where you can add new files.)
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