Evidence map›Paper›PMID 20529937›Full record

ArticleBioinformatics (Oxford, England)2010

As-rigid-as-possible mosaicking and serial section registration of large ssTEM datasets.

Stephan Saalfeld, Albert Cardona, Volker Hartenstein, Pavel Tomančák

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
65citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

65 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  10. Lineages to circuits: the developmental and evolutionary architecture of information channels into the central complex.Journal of comparative physiology. A, Neuroethology, sensory, neural, and behavioral physiology · 2023
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  12. Frontiers in neuroscience · 2023
    Article
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  15. Volume electron microscopy.Nature reviews. Methods primers · 2022
    Article
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5 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Stephan SaalfeldMax Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.
Albert Cardona
Volker Hartenstein
Pavel Tomančák

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

motivationTiled 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.

resultsWe 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).

Indexed as

Databases, FactualAnimalsDrosophilaImaging, Three-DimensionalMicroscopy, Electron, Transmission

Identifiers

PMID20529937
PMCPMC2881403

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.