Restoring drifted electron microscope volumes using synaptic vesicles at sub-pixel accuracy

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Standard

Restoring drifted electron microscope volumes using synaptic vesicles at sub-pixel accuracy. / Stephensen, Hans Jacob Teglbjærg; Darkner, Sune; Sporring, Jon.

In: Communications Biology, Vol. 3, No. 1, ´81, 2020.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Stephensen, HJT, Darkner, S & Sporring, J 2020, 'Restoring drifted electron microscope volumes using synaptic vesicles at sub-pixel accuracy', Communications Biology, vol. 3, no. 1, ´81. https://doi.org/10.1038/s42003-020-0809-4

APA

Stephensen, H. J. T., Darkner, S., & Sporring, J. (2020). Restoring drifted electron microscope volumes using synaptic vesicles at sub-pixel accuracy. Communications Biology, 3(1), [´81]. https://doi.org/10.1038/s42003-020-0809-4

Vancouver

Stephensen HJT, Darkner S, Sporring J. Restoring drifted electron microscope volumes using synaptic vesicles at sub-pixel accuracy. Communications Biology. 2020;3(1). ´81. https://doi.org/10.1038/s42003-020-0809-4

Author

Stephensen, Hans Jacob Teglbjærg ; Darkner, Sune ; Sporring, Jon. / Restoring drifted electron microscope volumes using synaptic vesicles at sub-pixel accuracy. In: Communications Biology. 2020 ; Vol. 3, No. 1.

Bibtex

@article{de414de24fce4f67aa59553b91cc3794,
title = "Restoring drifted electron microscope volumes using synaptic vesicles at sub-pixel accuracy",
abstract = "Imaging ultrastructures in cells using Focused Ion Beam Scanning Electron Microscope (FIB-SEM) yields section-by-section images at nano-resolution. Unfortunately, we observe that FIB-SEM often introduces sub-pixel drifts between sections, in the order of 2.5 nm. The accumulation of these drifts significantly skews distance measures and geometric structures, which standard image registration techniques fail to correct. We demonstrate that registration techniques based on mutual information and sum-of-squared-distances significantly underestimate the drift since they are agnostic to image content. For neuronal data at nano-resolution, we discovered that vesicles serve as a statistically simple geometric structure, making them well-suited for estimating the drift with sub-pixel accuracy. Here, we develop a statistical model of vesicle shapes for drift correction, demonstrate its superiority, and provide a self-contained freely available application for estimating and correcting drifted datasets with vesicles.",
author = "Stephensen, {Hans Jacob Teglbj{\ae}rg} and Sune Darkner and Jon Sporring",
year = "2020",
doi = "10.1038/s42003-020-0809-4",
language = "English",
volume = "3",
journal = "Communications Biology",
issn = "2399-3642",
publisher = "nature publishing group",
number = "1",

}

RIS

TY - JOUR

T1 - Restoring drifted electron microscope volumes using synaptic vesicles at sub-pixel accuracy

AU - Stephensen, Hans Jacob Teglbjærg

AU - Darkner, Sune

AU - Sporring, Jon

PY - 2020

Y1 - 2020

N2 - Imaging ultrastructures in cells using Focused Ion Beam Scanning Electron Microscope (FIB-SEM) yields section-by-section images at nano-resolution. Unfortunately, we observe that FIB-SEM often introduces sub-pixel drifts between sections, in the order of 2.5 nm. The accumulation of these drifts significantly skews distance measures and geometric structures, which standard image registration techniques fail to correct. We demonstrate that registration techniques based on mutual information and sum-of-squared-distances significantly underestimate the drift since they are agnostic to image content. For neuronal data at nano-resolution, we discovered that vesicles serve as a statistically simple geometric structure, making them well-suited for estimating the drift with sub-pixel accuracy. Here, we develop a statistical model of vesicle shapes for drift correction, demonstrate its superiority, and provide a self-contained freely available application for estimating and correcting drifted datasets with vesicles.

AB - Imaging ultrastructures in cells using Focused Ion Beam Scanning Electron Microscope (FIB-SEM) yields section-by-section images at nano-resolution. Unfortunately, we observe that FIB-SEM often introduces sub-pixel drifts between sections, in the order of 2.5 nm. The accumulation of these drifts significantly skews distance measures and geometric structures, which standard image registration techniques fail to correct. We demonstrate that registration techniques based on mutual information and sum-of-squared-distances significantly underestimate the drift since they are agnostic to image content. For neuronal data at nano-resolution, we discovered that vesicles serve as a statistically simple geometric structure, making them well-suited for estimating the drift with sub-pixel accuracy. Here, we develop a statistical model of vesicle shapes for drift correction, demonstrate its superiority, and provide a self-contained freely available application for estimating and correcting drifted datasets with vesicles.

U2 - 10.1038/s42003-020-0809-4

DO - 10.1038/s42003-020-0809-4

M3 - Journal article

C2 - 32081999

VL - 3

JO - Communications Biology

JF - Communications Biology

SN - 2399-3642

IS - 1

M1 - ´81

ER -

ID: 236989955