Main Menu (Mobile)- Block

Main Menu - Block

Selected Publications

janelia7_blocks-janelia7_secondary_menu | block
More in this Publication Landing Page
janelia7_blocks-janelia7_fake_breadcrumb | block
Saalfeld Lab / Selected Publications
node_body | node_body
janelia7_blocks-janelia7_select_pub_list_header | block

Select Publications

View All Publications
publications_landing_pages | views
05/07/18 | Synaptic cleft segmentation in non-isotropic volume electron microscopy of the complete Drosophila brain.
Heinrich L, Funke J, Pape C, Nunez-Iglesias J, Saalfeld S
arXiv. 2018 May 07:1805.02718

Neural circuit reconstruction at single synapse resolution is increasingly recognized as crucially important to decipher the function of biological nervous systems. Volume electron microscopy in serial transmission or scanning mode has been demonstrated to provide the necessary resolution to segment or trace all neurites and to annotate all synaptic connections. 
Automatic annotation of synaptic connections has been done successfully in near isotropic electron microscopy of vertebrate model organisms. Results on non-isotropic data in insect models, however, are not yet on par with human annotation. 
We designed a new 3D-U-Net architecture to optimally represent isotropic fields of view in non-isotropic data. We used regression on a signed distance transform of manually annotated synaptic clefts of the CREMI challenge dataset to train this model and observed significant improvement over the state of the art. 
We developed open source software for optimized parallel prediction on very large volumetric datasets and applied our model to predict synaptic clefts in a 50 tera-voxels dataset of the complete Drosophila brain. Our model generalizes well to areas far away from where training data was available.

View Publication Page
07/12/18 | A complete electron microscopy volume of the brain of adult Drosophila melanogaster.
Zheng Z, Lauritzen S, Perlman E, Robinson CG, Nichols M, Milkie DE, Torrens O, Price J, Fisher CB, Sharifi N, Calle-Schuler SA, Kmecova L, Ali IJ, Karsh B, Trautman ET, Bogovic JA, Hanslovsky P, Jefferis GSXE, Kazhdan M, Khairy K
Cell. 2018 Jul 12;174(3):730-43. doi: 10.1016/j.cell.2018.06.019

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..

View Publication Page
12/23/16 | Image-based correction of continuous and discontinuous non-planar axial distortion in serial section microscopy.
Hanslovsky P, Bogovic JA, Saalfeld S
Bioinformatics (Oxford, England). 2016 Dec 23:. doi: 10.1093/bioinformatics/btw794

MOTIVATION: Serial section microscopy is an established method for detailed anatomy reconstruction of biological specimen. During the last decade, high resolution electron microscopy (EM) of serial sections has become the de-facto standard for reconstruction of neural connectivity at ever increasing scales (EM connectomics). In serial section microscopy, the axial dimension of the volume is sampled by physically removing thin sections from the embedded specimen and subsequently imaging either the block-face or the section series. This process has limited precision leading to inhomogeneous non-planar sampling of the axial dimension of the volume which, in turn, results in distorted image volumes. This includes that section series may be collected and imaged in unknown order.

RESULTS: We developed methods to identify and correct these distortions through image-based signal analysis without any additional physical apparatus or measurements. We demonstrate the efficacy of our methods in proof of principle experiments and application to real world problems.

AVAILABILITY AND IMPLEMENTATION: We made our work available as libraries for the ImageJ distribution Fiji and for deployment in a high performance parallel computing environment. Our sources are open and available at, and CONTACT: : saalfelds@janelia.hhmi.orgSupplementary information: Supplementary data are available at Bioinformatics online.

View Publication Page
05/28/15 | BigDataViewer: visualization and processing for large image data sets.
Pietzsch T, Saalfeld S, Preibisch S, Tomancak P
Nature Methods. 2015 May 28;12(6):481-3. doi: 10.1038/nmeth.3392
11/15/12 | ImgLib2--generic image processing in Java.
Pietzsch T, Preibisch S, Tomancak P, Saalfeld S
Bioinformatics. 2012 Nov 15;28(22):3009-11. doi: 10.1093/bioinformatics/bts543

SUMMARY: ImgLib2 is an open-source Java library for n-dimensional data representation and manipulation with focus on image processing. It aims at minimizing code duplication by cleanly separating pixel-algebra, data access and data representation in memory. Algorithms can be implemented for classes of pixel types and generic access patterns by which they become independent of the specific dimensionality, pixel type and data representation. ImgLib2 illustrates that an elegant high-level programming interface can be achieved without sacrificing performance. It provides efficient implementations of common data types, storage layouts and algorithms. It is the data model underlying ImageJ2, the KNIME Image Processing toolbox and an increasing number of Fiji-Plugins.

AVAILABILITY: ImgLib2 is licensed under BSD. Documentation and source code are available at and in a public repository at

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Online.


View Publication Page
07/01/12 | Elastic volume reconstruction from series of ultra-thin microscopy sections.
Saalfeld S, Fetter RD, Cardona A, Tomancak P
Nature Methods. 2012 Jul;9(7):717-20. doi: 10.1038/nmeth.2072

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.

View Publication Page
06/01/10 | Software for bead-based registration of selective plane illumination microscopy data.
Preibisch S, Saalfeld S, Schindelin J, Tomancak P
Nature Methods. 2010 Jun;7(6):418-9. doi: 10.1038/nmeth0610-418
06/15/10 | As-rigid-as-possible mosaicking and serial section registration of large ssTEM datasets.
Saalfeld S, Cardona A, Hartenstein V, Tomancak P
Bioinformatics. 2010 Jun 15;26(12):i57-63. doi: 10.1093/bioinformatics/btq219

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.

View Publication Page
08/01/09 | CATMAID: collaborative annotation toolkit for massive amounts of image data.
Saalfeld S, Cardona A, Hartenstein V, Tomancak P
Bioinformatics. 2009 Aug 1;25(15):1984-6. doi: 10.1093/bioinformatics/btp266

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:

View Publication Page