Elastic volume reconstruction from series of ultra-thin microscopy sections

Abstract

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. DOI: https://doi.org/10. 1038/nmeth.2072 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-127852 Accepted Version Originally published at: Saalfeld, Stephan; Fetter, Richard; Cardona, Albert; Tomancak, Pavel (2012). Elastic volume reconstruction from series of ultra-thin microscopy sections. Nature Methods, 9(7):717-720. DOI: https://doi.org/10.1038/nmeth.2072 Elastic Volume Reconstruction from Series of Ultra-thin Microscopy SectionsStephan The downside of the method is that by physically cutting a block into sections the continuity between sections is lost and individual sections are deformed. To recover the imaged volume and extract biologically interesting information such as the reconstruction of neuronal circuits 2,3,5 , sections need to be aligned and distortion must be removed.Alignment can be achieved by maximizing the overlap of similar image content between adjacent sections. However, there are two unknowns that change image content across the section series: specimen's shape and independent section distortion introduced during preparation. Naively warping one section into another would compensate for the shape of the specimen and by that introduce artificial deformation. Our method exploits the fact that the biological specimen's shape typically changes smoothly across sections whereas the independent distortion in each section is random and uncorrelated with neighboring sections. We align all sections not only to their direct neighbors in the series but to all sections in a local neighborhood by modelling sections as two-dimensional elastic sheets that penalize non-rigid deformation ( Fig. 1a and Supplementary Fig. 1).All sections are treated as moving targets in a template-free global alignment. The elastic constraint is implemented as a spring-connected particle system where each section is represented as a triangular spring-mesh ( Fig. 1b and Supplementary Video 1 and Online methods).For each vertex of the spring-mesh, we search for the corresponding location in other sections by pairwise block-matching using normalized cross-correlation (NCC). To that end, we explore all translation vectors in an immediate neighborhood which requires sections to be in approximate alignment (Fig. 1c,d). We estimate this approximate alignment using automatically extracted landmark correspondences from invariant local image features as described previously 7,8 . Originally proposed for robust object recognition under partial occlus...

The complete connectome of a learning and memory center in an insect brain

“…Samples were dehydrated in an ethanol series, then transferred to propylene oxide and infiltrated and embedded with EPON resin. 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 using the elastic method 53 . To map the wiring diagram we used the web-based software CATMAID 54 , updated with a novel suite of neuron skeletonization and analysis tools 55 , and applied the iterative reconstruction method 55 .…”

A framework for feedback-based segmentation of 3D image stacks

“…In a first step, a combination of rigid and elastic registration (for initialization and refinement, respectively) is applied using the open-source software Fiji to align all images automatically [13]. This method worked well in practice, but any other registration approach may be used if more appropriate for a given data set.…”

Investigation of Parallax Issues for Multi-Lens Multispectral Camera Band Co-Registration

“…Various image registration methods for MSCs band coregistration had been proposed in (Jhan et al, 2016;Kelcey and Lucieer, 2012;Laliberte et al, 2011;Li et al, 2015;Saalfeld et al, 2012;Suárez et al, 2009;Yang et al, 2014;Zhang et al, 2016). It shows that the proposed methodology are varies from case to case, since different feature detection methods and adopted image transformation models can be applied.…”