10 research outputs found
Post-acquisition image based compensation for thickness variation in microscopy section series
Serial section Microscopy is an established method for volumetric anatomy
reconstruction. Section series imaged with Electron Microscopy are currently
vital for the reconstruction of the synaptic connectivity of entire animal
brains such as that of Drosophila melanogaster. The process of removing
ultrathin layers from a solid block containing the specimen, however, is a
fragile procedure and has limited precision with respect to section thickness.
We have developed a method to estimate the relative z-position of each
individual section as a function of signal change across the section series.
First experiments show promising results on both serial section Transmission
Electron Microscopy (ssTEM) data and Focused Ion Beam Scanning Electron
Microscopy (FIB-SEM) series. We made our solution available as Open Source
plugins for the TrakEM2 software and the ImageJ distribution Fiji
Isotropic Reconstruction of Neural Morphology from Large Non-Isotropic 3D Electron Microscopy
Neuroscientists are increasingly convinced that it is necessary to reconstruct
the precise wiring and synaptic connectivity of biological nervous systems to
eventually decipher their function. The urge to reconstruct ever larger and more
complete synaptic wiring diagrams of animal brains has created an entire new
subfield of neuroscience: Connectomics. The reconstruction of connectomes is
difficult because neurons are both large and small. They project across distances
of many millimeters but each individual neurite can be as thin as a few tens of
nanomaters. In order to reconstruct all neurites in densely packed neural tissues,
it is necessary to image this tissue at nanometer resolution which, today, is only
possible with 3D electron microscopy (3D-EM).
Over the last decade, 3D-EM has become significantly more reliable than ever
before. Today, it is possible to routinely image volumes of up to a cubic millimeter,
covering the entire brain of small model organisms such as that of the fruit fly
Drosophila melanogaster. These volumes contain tens or hundreds of tera-voxels
and cannot be analyzed manually. Efficient computational methods and tools
are needed for all stages of connectome reconstruction: (1) assembling distortion
and artifact free volumes from serial section EM, (2) precise automatic recon-
struction of neurons and synapses, and (3) efficient and user-friendly solutions
for visualization and interactive proofreading. In this dissertation, I present new
computational methods and tools that I developed to address previously unsolved
problems covering all of the above mentioned aspects of EM connectomics.
In chapter 2, I present a new method to correct for planar and non-planar axial
distortion and to sort unordered section series. This method was instrumental for
the first ever acquisition of a complete brain of an adult Drosophila melanogaster
imaged with 3D-EM.
Machine learning, in particular deep learning, and the availability of public
training and test data has had tremendous impact on the automatic reconstruction
of neurons and synapses from 3D-EM. In chapter 3, I present a novel artificial
neural network architecture that predicts neuron boundaries at quasi-isotropic
resolution from non-isotropic 3D-EM. The goal is to create a high-quality over-
segmentation with large three-dimensional fragments for faster manual proof-
reading.
In chapter 4, I present software libraries and tools that I developed to support
the processing, visualization, and analysis of large 3D-EM data and connectome
reconstructions. Using this software, we generated the largest currently existing
training and test data for connectome reconstruction from non-isotropic 3D-EM.
I will particularly emphasize my flexible interactive proof-reading tool Paintera
that I built on top of the libraries and tools that I have developed over the last
four years
Robust Registration of Calcium Images by Learned Contrast Synthesis
Multi-modal image registration is a challenging task that is vital to fuse
complementary signals for subsequent analyses. Despite much research into cost
functions addressing this challenge, there exist cases in which these are
ineffective. In this work, we show that (1) this is true for the registration
of in-vivo Drosophila brain volumes visualizing genetically encoded calcium
indicators to an nc82 atlas and (2) that machine learning based contrast
synthesis can yield improvements. More specifically, the number of subjects for
which the registration outright failed was greatly reduced (from 40% to 15%) by
using a synthesized image
Recommended from our members
A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster.
Drosophila melanogaster has a rich repertoire of innate and learned behaviors. Its 100,000-neuron brain is a large but tractable target for comprehensive neural circuit mapping. Only electron microscopy (EM) enables complete, unbiased mapping of synaptic connectivity; however, the fly brain is too large for conventional EM. We developed a custom high-throughput EM platform and imaged the entire brain of an adult female fly at synaptic resolution. To validate the dataset, we traced brain-spanning circuitry involving the mushroom body (MB), which has been extensively studied for its role in learning. All inputs to Kenyon cells (KCs), the intrinsic neurons of the MB, were mapped, revealing a previously unknown cell type, postsynaptic partners of KC dendrites, and unexpected clustering of olfactory projection neurons. These reconstructions show that this freely available EM volume supports mapping of brain-spanning circuits, which will significantly accelerate Drosophila neuroscience. VIDEO ABSTRACT
saalfeldlab/paintera: macos-latest Development Build
<h2>Commits</h2>
<ul>
<li>8f73045: Add descrpition to debian/control (Mark Kittisopikul) <a href="https://github.com/saalfeldlab/paintera/pull/514">#514</a></li>
<li>72610b6: Merge 8f73045868836eb406a7c810100681394b404b4b into 86c622b488dc4ef4e866401898e29d3aac41c3db (Mark Kittisopikul)</li>
</ul>
saalfeldlab/paintera: macOS-ARM64 Development Build
<h2>Commits</h2>
<ul>
<li>8f73045: Add descrpition to debian/control (Mark Kittisopikul) <a href="https://github.com/saalfeldlab/paintera/pull/514">#514</a></li>
<li>72610b6: Merge 8f73045868836eb406a7c810100681394b404b4b into 86c622b488dc4ef4e866401898e29d3aac41c3db (Mark Kittisopikul)</li>
</ul>
saalfeldlab/paintera: windows-latest Development Build
<h2>Commits</h2>
<ul>
<li>8f73045: Add descrpition to debian/control (Mark Kittisopikul) <a href="https://github.com/saalfeldlab/paintera/pull/514">#514</a></li>
<li>72610b6: Merge 8f73045868836eb406a7c810100681394b404b4b into 86c622b488dc4ef4e866401898e29d3aac41c3db (Mark Kittisopikul)</li>
</ul>
saalfeldlab/paintera: ubuntu-latest Development Build
<h2>Commits</h2>
<ul>
<li>8f73045: Add descrpition to debian/control (Mark Kittisopikul) <a href="https://github.com/saalfeldlab/paintera/pull/514">#514</a></li>
<li>72610b6: Merge 8f73045868836eb406a7c810100681394b404b4b into 86c622b488dc4ef4e866401898e29d3aac41c3db (Mark Kittisopikul)</li>
</ul>