70 research outputs found
Synaptic partner prediction from point annotations in insect brains
High-throughput electron microscopy allows recording of lar- ge stacks of
neural tissue with sufficient resolution to extract the wiring diagram of the
underlying neural network. Current efforts to automate this process focus
mainly on the segmentation of neurons. However, in order to recover a wiring
diagram, synaptic partners need to be identi- fied as well. This is especially
challenging in insect brains like Drosophila melanogaster, where one
presynaptic site is associated with multiple post- synaptic elements. Here we
propose a 3D U-Net architecture to directly identify pairs of voxels that are
pre- and postsynaptic to each other. To that end, we formulate the problem of
synaptic partner identification as a classification problem on long-range edges
between voxels to encode both the presence of a synaptic pair and its
direction. This formulation allows us to directly learn from synaptic point
annotations instead of more ex- pensive voxel-based synaptic cleft or vesicle
annotations. We evaluate our method on the MICCAI 2016 CREMI challenge and
improve over the current state of the art, producing 3% fewer errors than the
next best method
Synaptic Cleft Segmentation in Non-Isotropic Volume Electron Microscopy of the Complete Drosophila Brain
Neural circuit reconstruction at single synapse resolution is increasingly
recognized as crucially important to decipher the function of biological
nervous systems. Volume electron microscopy in serial transmission or scanning
mode has been demonstrated to provide the necessary resolution to segment or
trace all neurites and to annotate all synaptic connections.
Automatic annotation of synaptic connections has been done successfully in
near isotropic electron microscopy of vertebrate model organisms. Results on
non-isotropic data in insect models, however, are not yet on par with human
annotation.
We designed a new 3D-U-Net architecture to optimally represent isotropic
fields of view in non-isotropic data. We used regression on a signed distance
transform of manually annotated synaptic clefts of the CREMI challenge dataset
to train this model and observed significant improvement over the state of the
art.
We developed open source software for optimized parallel prediction on very
large volumetric datasets and applied our model to predict synaptic clefts in a
50 tera-voxels dataset of the complete Drosophila brain. Our model generalizes
well to areas far away from where training data was available
A Generalized Framework for Agglomerative Clustering of Signed Graphs applied to Instance Segmentation
We propose a novel theoretical framework that generalizes algorithms for
hierarchical agglomerative clustering to weighted graphs with both attractive
and repulsive interactions between the nodes. This framework defines GASP, a
Generalized Algorithm for Signed graph Partitioning, and allows us to explore
many combinations of different linkage criteria and cannot-link constraints. We
prove the equivalence of existing clustering methods to some of those
combinations, and introduce new algorithms for combinations which have not been
studied. An extensive comparison is performed to evaluate properties of the
clustering algorithms in the context of instance segmentation in images,
including robustness to noise and efficiency. We show how one of the new
algorithms proposed in our framework outperforms all previously known
agglomerative methods for signed graphs, both on the competitive CREMI 2016 EM
segmentation benchmark and on the CityScapes dataset.Comment: 19 pages, 8 figures, 6 table
Publisher Correction: Deep learning enables fast and dense single-molecule localization with high accuracy
In the version of this Article initially published, Jacob H. Macke and Jonas Ries were not listed as corresponding authors. Their contact information and designation as corresponding authors are now included. The error has been corrected in the online version of the Article
Whole-body integration of gene expression and single-cell morphology
Animal bodies are composed of cell types with unique expression programs that implement their distinct locations, shapes, structures, and functions. Based on these properties, cell types assemble into specific tissues and organs. To systematically explore the link between cell-type-specific gene expression and morphology, we registered an expression atlas to a whole-body electron microscopy volume of the nereid Platynereis dumerilii. Automated segmentation of cells and nuclei identifies major cell classes and establishes a link between gene activation, chromatin topography, and nuclear size. Clustering of segmented cells according to gene expression reveals spatially coherent tissues. In the brain, genetically defined groups of neurons match ganglionic nuclei with coherent projections. Besides interneurons, we uncover sensory-neurosecretory cells in the nereid mushroom bodies, which thus qualify as sensory organs. They furthermore resemble the vertebrate telencephalon by molecular anatomy. We provide an integrated browser as a Fiji plugin for remote exploration of all available multimodal datasets
ilastik: interactive machine learning for (bio)image analysis
We present ilastik, an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise. It contains pre-defined workflows for image segmentation, object classification, counting and tracking. Users adapt the workflows to the problem at hand by interactively providing sparse training annotations for a nonlinear classifier. ilastik can process data in up to five dimensions (3D, time and number of channels). Its computational back end runs operations on-demand wherever possible, allowing for interactive prediction on data larger than RAM. Once the classifiers are trained, ilastik workflows can be applied to new data from the command line without further user interaction. We describe all ilastik workflows in detail, including three
case studies and a discussion on the expected performance
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