This document discusses using the Huygens Remote Manager (HRM) to efficiently process very large microscopy image datasets through batch deconvolution. HRM allows multi-user, web-based deconvolution of datasets too large for desktop computers to handle. Two examples are provided where HRM was used to deconvolve a 30GB spinning disk dataset in 4 hours and a 1TB SPIM dataset in 120 hours, improving image quality and enabling more accurate analysis. HRM provides a solution for high-throughput processing of increasingly large microscopy images generated by techniques like SPIM.

1. Deconvolution of Very Large Data Sets
Web-Based Batch Restoration with the Huygens Remote Manager
The Huygens Remote Manager (HRM) is an
open-source, multi-user, web-based batch pro-
cessor for large scale restoration of microscopy
images. By enabling mass deconvolution, HRM
aims at maximizing the volume of data suita-
ble for segmentation, quantification and anal-
ysis. Recent deconvolution runs of large spin-
ning disk and SPIM images show that while
these images present a true challenge for cur-
rent desktop computers, they can be easily pro-
cessed on a small HRM server automatically.
Introduction
Image deconvolution reassigns the out–of-
focus signal introduced by the microscope
optics while it improves resolution, con-
trast and the signal-to-noise ratio (SNR)
[1]. Hence, the recognition of deconvolu-
tion as a fundamental processing tech-
nique for wide-field, confocal, spinning
disk, multiphoton and STED microscopy
images. As we show here, image decon-
volution also improves the quality of im-
ages from Selective Plane Illumination
Microscopy (SPIM) despite the challenge
that their large sizes represent. Moreo-
facilities worldwide (fig. 1).To illustrate the
HRM processing rate, we present the re-
sults of the deconvolution of two biological
data sets acquired with spinning disk and
SPIM microscopes.
Deconvolution Examples
of Spinning Disk and SPIM Data
The Advanced Light Microscopy Facil-
ity (ALMF) at EMBL Heidelberg has been
successfully using HRM for several years
on a server with the relatively modest
specifications of 8 cores, 96 GB of RAM
and 2 TB of disk space. Disk space and
RAM limit the maximum size of images
that can be deconvolved.
Study of Golgi Organization
with Spinning Disk Microscopy
In the study of Golgi organization in mam-
malian cells, Golgi fragments, generated
for example during experimentally in-
duced Golgi breakdown and biogenesis
processes [7, 8], or during cell division
events, are tracked over time. An analy-
ver, deconvolution is a cost-effective tool,
as it only requires conventional computa-
tional infrastructure and no additional mi-
croscopy equipment. The steady growth
of data set sizes is raising technical con-
cerns in all segments of the standard im-
aging pipelines. Data management solu-
tions (e.g. OMERO [2], openBIS [3]), image
restoration (deconvolution, stabilization,
chromatic corrections) and analysis al-
gorithms (tracking, segmentation) are all
facing this new challenge. Recent micros-
copy modalities, such as spinning disk
confocal and SPIM [4], are pushing these
boundaries even further. In particular,
SPIM microscopy allows for acquisition of
extremely large data sets and is becoming
the tool of choice in studies of embryonic
development [5] and functional whole-
brain imaging [6].To facilitate the restora-
tion of ever increasing volumes of micros-
copy data, the development of a Remote
Manager (HRM) for the Huygens soft-
ware (www.svi.nl/Huygens) was started
in 2004 at Montpellier Rio Imaging. Now-
adays, HRM is a collaborative effort that
has proven useful in the large-scale auto-
mation and optimization of the image pro-
cessing pipelines at a growing number of
22 • G.I.T. Imaging & Microscopy 2/2015
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2. sis strategy for 2D image se-
quences has worked well for
the initial characterization of
Golgi fragment dynamics [7,
8]. However, for a more com-
prehensive investigation, 3D
imaging is required, though
limited by axial resolution
and, for long live cell experi-
ments, by SNR. To track the
changes of Golgi morphology,
a stack of 40 slices was re-
corded every 3 min during 10
h with a 63x/1.4 oil immer-
sion objective on an UltraView
VoX spinning disk microscope,
where Golgi of HeLa cells
were tagged with GFP (green)
and nuclei with mCherry
(red).The final image size was
30 GB. The results of the im-
age acquisition and deconvo-
lution are presented in figure
2, where the reassignment of
out-of-focus light and SNR in-
crease by deconvolution are
visualized.This process allows
for more accurate analysis
of the Golgi dynamics as the
higher quality of deconvolved
images permits more precise
segmentation and distinction
of smaller structures. The de-
convolution time for the entire
data set was about 4 h on the
HRM server (7.5 GB/h).
Studies of Drosophila
Embryo Development with
SPIM Microscopy
One goal in developmen-
tal research is to understand
cell fate decisions on a sin-
gle cell level. Using Drosoph-
ila melanogaster embryos
as a model, imaging technol-
ogy can monitor the develop-
ment from a single to thou-
sands of cells in larval stage
within 24 hours. While SPIM
microscopy allows for fast 3D
imaging of the entire embryo
[9], fluorescent blur, and sig-
nal scattering by yolk and tis-
sues deteriorate signal qual-
ity. Successful lineage tracing,
however, is highly dependent
on the image quality, since it
requires precise segmenta-
tion and tracking of the indi-
vidual cells that are densely
packed within the embryo.
To follow the development of
these embryos, images of live
Drosophila in syncytial blasto-
derm (2 hpf) labeled with his-
tone mCherry were acquired
with a 25x/1.1 water dipping
objective lens on a SPIM mi-
croscope. A stack of 400 slices
was taken every 30 s for 20 h
to image the change in posi-
tion of nuclei during embryo
development. The total data
set size was 1 TB. The com-
parison of the raw and de-
convolved data is presented in
figure 3, where deconvolution
shows a clear SNR improve-
ment and elimination of back-
ground blur. This allows for
more precise quantification of
the embryo development due
to the more accurate detec-
tion of the individual cells and
nuclei and more reliable, eas-
ier tracking. The HRM server
deconvolved the entire data
set in about 120 h (8.3 GB/h).
Discussion and Outlook
HRM allows for efficient batch
deconvolution of very large
data sets in a multi-user en-
vironment via a user-friendly
web application [10]. With the
server specifications at ALMF,
a processing rate in the range
of 5 to 10 GB/h can routinely
be reached. Raw data is usu-
ally made immediately avail-
able at the processing server
by mounting the HRM disk [10]
on the acquisition machines.
Alternatively, HRM offers a di-
rect bridge to the OMERO data
management system that al-
lows for two-way exchange of
raw and deconvolved images
between HRM and OMERO in-
stances in a network.To speed
up the processing of large data
volumes, HRM can work with
an array of processing ma-
chines, thus splitting the work-
load across multiple servers. A
new HRM architecture based
on the GC3Pie library (code.
google.com/p/gc3pie) will soon
enable better parallelization
Fig. 1: The HRM welcome page.
DUAL INVERTED SELECTIVE
PLANE ILLUMINATION
MICROSCOPY
FOR MORE INFORMATION...
Visit: www.asiimaging.com
Email: info@asiimaging.com
Call: (800) 706-2284 or (541) 461-8181
A Perfect Solution for Live
Specimen Imaging.
• RAPID 3D IMAGING: Generate 3D volumes with isotropic resolution
(330 nm)
• BETTER AXIAL RESOLUTION: ~2x better than confocal or
spinning disk systems
• LOW PHOTOTOXICITY, EXTREMELY CELL FRIENDLY:
Achieve a ~7-10 fold reduction in photobleaching
• HIGH ACQUISITION RATES: Up to 200 fps or 2-5 volumes per second
• USE CONVENTIONAL SAMPLE MOUNTS
G.I.T. Imaging & Microscopy 2/2015 • 23
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3. of huge deconvolution tasks over clusters,
grids and cloud-based virtual machines.
Future developments of HRM will offer the
same large scale support for additional
tools like chromatic aberration correction,
image stitching, image stabilization, cross-
talk correction, and distillation of Point
Spread Functions. As data gets larger,
careful consideration must also be given
to strategies for optimally storing, access-
ing and modifying images. The Hierarchi-
cal Data Format (HDF5, www.hdfgroup.
org/HDF5) is designed to efficiently store
and organize large amounts of numerical
data. We encourage its usage in HRM and
promote its adoption (for HDF5 support
in other software see www.hdfgroup.org/
Fig. 2: Deconvolution result for spinning disk. Comparison of raw (left) and deconvolved (right) images.
(A, B) Maximum intensity projection. (C, D) Single XZ plane. Image courtesy of Christian Schuberth.
Fig. 3: Deconvolution result for SPIM. Comparison of raw (top) and deconvolved (bottom) images. (A,
B) Maximum intensity projection. (C, D) Surface rendering (Imaris, Bitplane AG) of the anterior side of
the embryo. Image courtesy of Stefan Guenther.
More information on devonvolution
methods in microscopy:
http://bit.ly/deconvolution
Read more about quantitative analysis:
http://bit.ly/IM-QA
tools5desc.html). Finally, it is important
to remember that data sets should not be
larger than they strictly need to be to ad-
dress the biological question of interest
(www.svi.nl/NyquistCalculator).
Conclusion
Image deconvolution increases resolution
and SNR while it decreases background
and blur, thus allowing for easier, more
reliable segmentation, tracking, and
analysis.Very large images are no excep-
tion and should also be deconvolved be-
fore drawing any conclusions from them.
However, large images quickly become a
real challenge for desktop computers. To
ease local computing resources and cen-
tralize image processing, we showed that
HRM allows for running large deconvo-
lution jobs at a good rate on moderately
sized servers from a user‘s web browser.
Acknowledgments
We would like to thank all members of
ALMF, Stefan Guenther and Christian
Schuberth from EMBL; Carl Zeiss AG,
Leica, Olympus and PerkinElmer; and
the HRM developers.
References
[1] Van der Voort et al.: Journal of Microscopy 178,
165–181 (1995)
[2] Allan C. et al.: Nat. Methods 9(3), 245–53 (2012)
[3] Bauch A. et al.: BMC Bioinformatics 12(1), 468–
486 (2011)
[4] Stelzer E.H.K.: Nat. Publ. Gr. 12(1), 23–26
(2015)
[5] Huisken J. et al.: Science 305, 1007–1009
(2004)
[6] Ahrens M. B. et al.: Nat Methods 10(5), 413-420
(2013)
[7] Ronchi P. et al.: J Cell Sci 127(21), 4620–33
(2014)
[8] Schuberth CE. et al.: J Cell Sci 128(7), 1279–93
(2015)
[9] Krzic U. et al.: Nature Methods 9, 730–33
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[10] Ponti A. et al.: Imaging Microscopy 9(2):57–
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Contact
Dr. Aaron Ponti
ETH Zurich
Department of Biosystems Science and Engineering
Basel, Switzerland
aaron.ponti@bsse.ethz.ch
www.bsse.ethz.ch
MSc. Daniel Sevilla Sanchez
Scientific Volume Imaging B.V.
Hilversum,The Netherlands
www.svi.nl
Dr. Yury Belyaev
EMBL - ALMF
Heidelberg, Germany
www.embl.de/almf/
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