Filter
Associated Lab
- 43418 (1) Apply 43418 filter
- Aso Lab (1) Apply Aso Lab filter
- Betzig Lab (2) Apply Betzig Lab filter
- Bock Lab (1) Apply Bock Lab filter
- Cardona Lab (3) Apply Cardona Lab filter
- Clapham Lab (1) Apply Clapham Lab filter
- Fetter Lab (3) Apply Fetter Lab filter
- Funke Lab (8) Apply Funke Lab filter
- Harris Lab (3) Apply Harris Lab filter
- Hess Lab (5) Apply Hess Lab filter
- Jayaraman Lab (1) Apply Jayaraman Lab filter
- Lippincott-Schwartz Lab (2) Apply Lippincott-Schwartz Lab filter
- Rubin Lab (3) Apply Rubin Lab filter
- Remove Saalfeld Lab filter Saalfeld Lab
- Scheffer Lab (1) Apply Scheffer Lab filter
- Singer Lab (1) Apply Singer Lab filter
- Sternson Lab (2) Apply Sternson Lab filter
- Svoboda Lab (2) Apply Svoboda Lab filter
- Tillberg Lab (3) Apply Tillberg Lab filter
- Turaga Lab (3) Apply Turaga Lab filter
Associated Project Team
Associated Support Team
- Electron Microscopy (2) Apply Electron Microscopy filter
- Janelia Experimental Technology (1) Apply Janelia Experimental Technology filter
- Light Microscopy (1) Apply Light Microscopy filter
- Project Technical Resources (1) Apply Project Technical Resources filter
- Scientific Computing Software (6) Apply Scientific Computing Software filter
- Scientific Computing Systems (1) Apply Scientific Computing Systems filter
Publication Date
- 2023 (2) Apply 2023 filter
- 2022 (6) Apply 2022 filter
- 2021 (4) Apply 2021 filter
- 2020 (2) Apply 2020 filter
- 2019 (3) Apply 2019 filter
- 2018 (5) Apply 2018 filter
- 2017 (4) Apply 2017 filter
- 2016 (3) Apply 2016 filter
- 2015 (3) Apply 2015 filter
- 2014 (1) Apply 2014 filter
- 2012 (1) Apply 2012 filter
- 2010 (1) Apply 2010 filter
35 Janelia Publications
Showing 31-35 of 35 resultsSerial 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.
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.
Anatomy of large biological specimens is often reconstructed from serially sectioned volumes imaged by high-resolution microscopy. We developed a method to reassemble a continuous volume from such large section series that explicitly minimizes artificial deformation by applying a global elastic constraint. We demonstrate our method on a series of transmission electron microscopy sections covering the entire 558-cell Caenorhabditis elegans embryo and a segment of the Drosophila melanogaster larval ventral nerve cord.
The Drosophila brain is formed by an invariant set of lineages, each of which is derived from a unique neural stem cell (neuroblast) and forms a genetic and structural unit of the brain. The task of reconstructing brain circuitry at the level of individual neurons can be made significantly easier by assigning neurons to their respective lineages. In this article we address the automation of neuron and lineage identification. We focused on the Drosophila brain lineages at the larval stage when they form easily recognizable secondary axon tracts (SATs) that were previously partially characterized. We now generated an annotated digital database containing all lineage tracts reconstructed from five registered wild-type brains, at higher resolution and including some that were previously not characterized. We developed a method for SAT structural comparisons based on a dynamic programming approach akin to nucleotide sequence alignment and a machine learning classifier trained on the annotated database of reference SATs. We quantified the stereotypy of SATs by measuring the residual variability of aligned wild-type SATs. Next, we used our method for the identification of SATs within wild-type larval brains, and found it highly accurate (93-99%). The method proved highly robust for the identification of lineages in mutant brains and in brains that differed in developmental time or labeling. We describe for the first time an algorithm that quantifies neuronal projection stereotypy in the Drosophila brain and use the algorithm for automatic neuron and lineage recognition.