56 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
Globally optimal stitching of tiled 3D microscopic image acquisitions
Motivation: Modern anatomical and developmental studies often require high-resolution imaging of large specimens in three dimensions (3D). Confocal microscopy produces high-resolution 3D images, but is limited by a relatively small field of view compared with the size of large biological specimens. Therefore, motorized stages that move the sample are used to create a tiled scan of the whole specimen. The physical coordinates provided by the microscope stage are not precise enough to allow direct reconstruction (Stitching) of the whole image from individual image stacks
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
CATMAID: collaborative annotation toolkit for massive amounts of image data
Summary: High-resolution, three-dimensional (3D) imaging of large biological specimens generates massive image datasets that are difficult to navigate, annotate and share effectively. Inspired by online mapping applications like GoogleMaps™, we developed a decentralized web interface that allows seamless navigation of arbitrarily large image stacks. Our interface provides means for online, collaborative annotation of the biological image data and seamless sharing of regions of interest by bookmarking. The CATMAID interface enables synchronized navigation through multiple registered datasets even at vastly different scales such as in comparisons between optical and electron microscopy
CATMAID: collaborative annotation toolkit for massive amounts of image data
Summary: High-resolution, three-dimensional (3D) imaging of large biological specimens generates massive image datasets that are difficult to navigate, annotate and share effectively. Inspired by online mapping applications like GoogleMaps™, we developed a decentralized web interface that allows seamless navigation of arbitrarily large image stacks. Our interface provides means for online, collaborative annotation of the biological image data and seamless sharing of regions of interest by bookmarking. The CATMAID interface enables synchronized navigation through multiple registered datasets even at vastly different scales such as in comparisons between optical and electron microscopy. Availability: http://fly.mpi-cbg.de/catmaid Contact: [email protected]
As-rigid-as-possible mosaicking and serial section registration of large ssTEM datasets
Motivation: Tiled serial section Transmission Electron Microscopy (ssTEM) is increasingly used to describe high-resolution anatomy of large biological specimens. In particular in neurobiology, TEM is indispensable for analysis of synaptic connectivity in the brain. Registration of ssTEM image mosaics has to recover the 3D continuity and geometrical properties of the specimen in presence of various distortions that are applied to the tissue during sectioning, staining and imaging. These include staining artifacts, mechanical deformation, missing sections and the fact that structures may appear dissimilar in consecutive sections. Results: We developed a fully automatic, non-rigid but as-rigid-as-possible registration method for large tiled serial section microscopy stacks. We use the Scale Invariant Feature Transform (SIFT) to identify corresponding landmarks within and across sections and globally optimize the pose of all tiles in terms of least square displacement of these landmark correspondences. We evaluate the precision of the approach using an artificially generated dataset designed to mimic the properties of TEM data. We demonstrate the performance of our method by registering an ssTEM dataset of the first instar larval brain of Drosophila melanogaster consisting of 6885 images. Availability: This method is implemented as part of the open source software TrakEM2 (http://www.ini.uzh.ch/∼acardona/trakem2.html) and distributed through the Fiji project (http://pacific.mpi-cbg.de). Contact: [email protected]
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