TrakEM2 Software for Neural Circuit Reconstruction

By Albert Cardona, Stephan Saalfeld, Johannes E. Schindelin, Ignacio Arganda-Carreras, Stephan Preibisch, Mark Longair, Pavel Tomancak, Volker Hartenstein, Rodney J. Douglas. PLoS ONE, 2012

969 citations recorded by the source corpus.

A key challenge in neuroscience is the expeditious reconstruction of neuronal circuits. For model systems such as Drosophila and C. elegans, the limiting step is no longer the acquisition of imagery but the extraction of the circuit from images. For this purpose, we designed a software application, TrakEM2, that addresses the systematic reconstruction of neuronal circuits from large electron microscopical and optical image volumes. We address the challenges of image volume composition from individual, deformed images; of the reconstruction of neuronal arbors and annotation of synapses with fast manual and semi-automatic methods; and the management of large collections of both images and annotations. The output is a neural circuit of 3d arbors and synapses, encoded in NeuroML and other formats, ready for analysis.

Subfields: Biophysics, Structural Biology

View Paper: https://www.semanticscholar.org/paper/327b33d038e3422d89e6ee64e841a6bc70308ca9

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Citations in the corpus

Semantic Image Segmentation with Contextual Hierarchical Models (2015, methodology): “In addition, we apply CHM on two electron microscopy (EM) datasets for cell membrane detection (Drosophila VNC [21], [22] and mouse neuropil [9]).”
Quantitative neuroanatomy for connectomics in Drosophila (2015, methodology): “A neuron is reconstructed with a skeleton, a directed tree graph with one or more nodes in every cross-section of neurite in an EM volume (Helmstaedter et al., 2011; Cardona et al., 2012).”
The complete connectome of a learning and memory centre in an insect brain (2017, methodology): “After sectioning the volume with a Leica UC6 ultramicrotome, sections were imaged semi-automatically with Leginon 51 driving an FEI T20 TEM (Hillsboro), and then assembled with TrakEM2 52 us-ing the elastic method 53 .”
Single-Cell Analysis of Crohn’s Disease Lesions Identifies a Pathogenic Cellular Module Associated with Resistance to Anti-TNF Therapy (2019, methodology): “Images are aligned by using the Trakem2 plugin (Cardona et al., 2012).”
Crowdsourcing the creation of image segmentation algorithms for connectomics (2015, methodology): “AC and IA manually delineated the neurite boundaries using the open-source software TrakEM2 (Cardona et al., 2012), while DB used the freely-available software VAST 3 .”
Fiji - an Open Source platform for biological image analysis (2012, methodology): “Arbitrarily large ssTEM data sets consisting of tens of thousands images are best assembled using the integrative TrakEM2 plugin 24 for registration, segmentation and analysis of electron-microscopy data.”
The ImageJ ecosystem: an open platform for biomedical image analysis (2015, background): “One of the largest ImageJ plugin libraries, TrakEM2 (Cardona et al., 2012), was designed to work on very large mosaics of electron-microscopy images that would be assembled automatically and displayed in a manner similar toGooglemaps: pre-calculated zoom levels allow fluid navigation of datasets com”
Dense connectomic reconstruction in layer 4 of the somatosensory cortex (2018, methodology): “…reconstruction techniques for large-scale 3D-EM-based connectomics, the calibration of methodological progress using various definitions of error rates per cable length were initially important (Boergens et al., 2017; Cardona et al., 2012; Helmstaedter et al., 2011; Januszewski et al., 2018).”
The ImageJ ecosystem: Open‐source software for image visualization, processing, and analysis (2020, background): “71 Fijiyama (https://imagej.net/ Fijiyama) is a 3D registration tool useful for multimodal time-lapse imaging.”