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Imaging ATUM ultrathin section libraries with WaferMapper: a
multi-scale approach to EM reconstruction of neural circuits
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Hayworth, Kenneth J., Josh L. Morgan, Richard Schalek, Daniel
R. Berger, David G. C. Hildebrand, and Jeff W. Lichtman. 2014.
“Imaging ATUM ultrathin section libraries with WaferMapper: a
multi-scale approach to EM reconstruction of neural circuits.”
Frontiers in Neural Circuits 8 (1): 68.
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December 16, 2015 3:22:51 PM EST
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published: 27 June 2014
doi: 10.3389/fncir.2014.00068
Imaging ATUM ultrathin section libraries with
WaferMapper: a multi-scale approach to
EM reconstruction of neural circuits
Kenneth J. Hayworth 1*, Josh L. Morgan 2*, Richard Schalek 2 , Daniel R. Berger 2 ,
David G. C. Hildebrand 2 and Jeff W. Lichtman 2
Howard Hughes Medical Institute, Ashburn, VA, USA
Department of Molecular and Cell Biology, Harvard University, Cambridge, MA, USA
Edited by:
Benjamin R. Arenkiel, Baylor College
of Medicine, USA
Reviewed by:
Julian Budd, University of Sussex,
Richard J. Weinberg, University of
North Carolina, USA
Kenneth J. Hayworth, Howard
Hughes Medical Institute, Janelia
Farm Research Campus, 19700
Helix Dr., Ashburn, VA 20147, USA
e-mail: hayworthk@janelia.hhmi.org;
Josh L. Morgan, Department of
Molecular and Cell Biology, Harvard
University, 52 Oxford St.,
Cambridge, MA 02138, USA
e-mail: joshmorgan@fas.harvard.edu
The automated tape-collecting ultramicrotome (ATUM) makes it possible to collect large
numbers of ultrathin sections quickly—the equivalent of a petabyte of high resolution
images each day. However, even high throughput image acquisition strategies generate
images far more slowly (at present ∼1 terabyte per day). We therefore developed
WaferMapper, a software package that takes a multi-resolution approach to mapping
and imaging select regions within a library of ultrathin sections. This automated method
selects and directs imaging of corresponding regions within each section of an ultrathin
section library (UTSL) that may contain many thousands of sections. Using WaferMapper,
it is possible to map thousands of tissue sections at low resolution and target multiple
points of interest for high resolution imaging based on anatomical landmarks. The program
can also be used to expand previously imaged regions, acquire data under different
imaging conditions, or re-image after additional tissue treatments.
Keywords: connectomics, ATUM, volume EM, scanning electron microscopy, ultramicrotome, imaging software,
tape collection, serial-section electron microscopy
The three dimensional (3D) structure of biological tissues can be
ascertained at high resolution by cutting plastic-embedded tissue into a series of ultrathin sections, imaging those sections with
an electron microscope, and reconstructing the objects contained
therein (volume EM). Obtaining such volumetric reconstructions
is especially useful for analysis of nervous system samples because
nerve cells distribute their processes over extended volumes and
only with the resolution of electron microscopy (EM) is it possible to identify the network of synaptic connections between
all the neurons. This dense synaptic connectivity data is critical to understanding how nervous systems process information
(Morgan and Lichtman, 2013).
Until recently, volume EM required manually collecting a
series of ultrathin sections onto the nanometers thin plastic film of a transmission electron microscope (TEM) grid.
Because of the thin substrate, the process of making serial
sections can be painstaking and is subject to tissue loss that
poses a serious challenge for very large volume reconstructions (Gay and Anderson, 1954; Harris et al., 2006). With
the introduction of high-performance field emission scanning electron microscopy (SEM) (Joy, 1991; Bogner et al.,
2007), high quality images can be acquired from the surface of ultrathin sections thereby removing the need to mount
sections on an electron-transmissive substrate. Several new
imaging strategies have emerged to take advantage of EM
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surface imaging to facilitate the production of large EM image
One strategy that is based on SEM surface imaging is to image
the surface of a plastic-embedded block of brain tissue that is
mounted directly inside the SEM. Tens of nanometers of the
block’s top surface are then removed by either a microtome in
the serial blockface EM (SBEM) approach or a focused ion beam
(FIB) in the FIB-SEM approach to expose a new surface for imaging. This procedure can be repeated many thousands of times to
produce a volume EM image set (Denk and Horstmann, 2004;
Knott et al., 2008).
A second strategy, which we adopt in this paper, takes advantage of the surface imaging capabilities of SEM by mounting
ultrathin sections on a much more stable substrate than can
be used in TEM. In the recently invented Automatic Tapecollecting UltraMicrotome SEM (ATUM-SEM) process (Schalek
et al., 2011), the ultrathin sections cut by a commercial ultramicrotome are immediately and automatically collected from the
knife’s water boat onto the surface of a partially submerged conveyor belt made of sturdy plastic tape (Figure 1). SEM imaging of
the series of sections collected on the tape produces a dataset of
micrographs which renders individual planes through a 3D tissue volume. This tape collection allows thousands of ultrathin
sections to be collected in an automatic way. Because of the uninterrupted flow of tissue onto the relatively wide conveyor belt, the
sections obtained can be thinner, larger in area (several square
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Imaging ATUM ultrathin section libraries
FIGURE 1 | ATUM-SEM process. (A) Picture of ATUM tape collection
device installed on a commercial ultramicrotome housed in an
environmental control chamber. (B) Side view of ATUM. (C,D) Sequential
video images of section collection. (E) CAD rendering of ATUM showing
path collected sections take from the knife boat to the final take-up reel.
(F) CAD zoom in on knife boat showing collection process. (G) Picture of
unraveled tissue-tape on take-up reel containing a series of ultrathin
millimeters each), and of higher quality (without tears, etc.) than
are obtained by manual collection for TEM.
A major challenge of the ATUM-SEM approach is setting up
many thousands of targeted image acquisitions from tissue sections spread across meters of ATUM collection tape. To convert
these sections into an image volume, the ATUM’s tape must be
mounted in the SEM and a region of interest on a section must
be positioned beneath the electron beam for imaging. The corresponding region of interest must be found again and again on
all subsequent sections. Each section’s target region must be positioned, rotated, and focused beneath the electron beam to obtain
a high resolution image series. Here we describe a semi-automatic
microscope control software package named WaferMapper that
can orchestrate all of these steps to produce volume EM image
sets from an ATUM tape.
With the proper software solution for handling the additional
imaging complexity, the ATUM-SEM process has several potential advantages over alternative techniques. The most obvious
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sections. Each dark rectangle is a section. (H) Picture of a 100 mm
diameter silicon wafer with 10 tissue-tape strips adhered to it. There are
a total of 162 ultrathin sections on this one wafer. (I) Picture of 20
wafers all filled with tape strips from a single ATUM run consisting of
over 15 m of tissue tape. This collection of 20 wafers is a single
UltraThin Section Library (UTSL) containing over 3000 ultrathin sections,
representing a total tissue volume of over 0.2 mm3 .
advantage over block-face techniques is that the ATUM-SEM
technique does not destroy the tissue as it is being imaged. Low
resolution images of the entire tissue volume can, therefore,
be taken relatively quickly. This overview image volume can be
used to plan efficient, targeted high resolution imaging volumes
encompassing only those regions crucial to the biological study
at hand. Additional high resolution imaging forays on the same
sectioned material can be conducted at multiple later times if
The importance of this ability becomes apparent when one
considers the time and storage requirements involved in imaging 3D volumes at nanometer resolutions. For example, consider
a 0.5 × 2.5 × 3 mm block of mouse brain trimmed to encompass regions of the lateral geniculate nucleus (LGN) and primary visual cortex (V1) along with intact axonal projections
connecting the two (MacLean et al., 2006). On the ATUM,
such a “visual thalamocortical slice” block could potentially be
reduced to a tape of 17, 000 × 30 nm thick sections collected
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with just a few days of sectioning. If imaged in total, with a
typical 5 nm in-plane resolution, this volume would require storage of 5 petabytes of image data (17,000 sections each montage
imaged with 500, 000 × 600, 000 pixels). Worse still, if imaged
at a standard rate of about 10 megapixels per second, a single
microscope would require over 15 years to image the volume,
seemingly putting such a study out of reach today. However, the
actual connected regions of the LGN and V1 represent only a
tiny fraction of the full slice volume which they span. If one
could direct high-resolution imaging mainly to those regions
which are actually connected then the total imaging time could
potentially be reduced by a factor of 10× or more. One way to
efficiently direct such high-resolution imaging would be to utilize an iterative process of first making a low resolution image
set of the entire volume and then use several passes of directed,
medium and high-resolution imaging to narrow in on and eventually high-resolution image only those parts containing an intact
thalamocortical circuit. An additional advantage afforded by nondestructive ultrathin section collection is that imaging time can be
further reduced by dividing up the ATUM tape so that it can be
simultaneously imaged in parallel across multiple SEMs.
This type of parallel, multi-scale, directed-access volume EM
imaging—which is not possible in blockface approaches and
extremely difficult when handling individual TEM grids—is possible given the large tissue volumes and the robustness to reimaging and tissue handling of the ATUM-SEM technique. In
this paper we use the term “UltraThin Section Library” (UTSL)
to describe a collection of many thousand ATUM-collected ultrathin sections which have been securely mounted on wafers for
SEM imaging and which have undergone all of the coordinate
mapping steps and low resolution overview imaging necessary to
allow quick and easy random-access imaging of any point in the
volume. These mapping steps are performed through our custom
SEM-automation software–WaferMapper.
Our vision is that a researcher using such an UTSL and
WaferMapper should be able to quickly browse through the entire
tissue volume at low resolution, identify salient anatomical features, and then graphically specify a subvolume for automated
imaging. The WaferMapper software then instructs which wafers
to load into the SEM leaving the software to automate all subsequent imaging operations.
In order to take full advantage of a large tissue library, the
mapping and imaging software must meet the following criteria:
- Automated imaging—The first goal of an automated image
acquisition software package is to allow a user to image the
corresponding region of tissue on all of the sections in a tissue library without having to manually direct the microscope
to each section. Ideally, the user should be able to pick a target region within the software, load a wafer, and leave the
microscope while the imaging takes place automatically.
- High throughput—To reconstruct large regions of tissue at
high resolution, images must be acquired quickly. Scan speeds
within a single image currently range from about 0.5 to 20
million pixels per second (MPS) using the commercially available SEMs and detectors described in this paper. This large
range of imaging speeds reflects the wide range of staining
Frontiers in Neural Circuits
techniques used in connectomics studies as well as differences
in the efficiency and bandwidth of different types of detectors
used under various imaging conditions. Ideally the acquisition overhead (time spent between image scanning) should
be less than the actual image acquisition time. Eliminating
human involvement in the image acquisition procedure is an
important part of reducing overhead. In addition, however,
automated steps such as stage movements, focusing, and image
retakes also need to be accomplished quickly so as not to slow
down the throughput. (We provide a table breaking down
actual data acquisition times for key WaferMapper steps in the
Example data section below).
- Robustness—The software must allow for variations in tissue
properties, as well as staining, cutting, and imaging conditions.
In particular, the software must be able to find and image
the corresponding region in serial sections that may appear
different due to staining artifacts, damage during cutting, or
biological changes in the tissue. If changes in the tissue or
the microscope result in failures to target the correct tissue
region or acquire high quality images, these failures should
be detected and corrected without the requirement of human
WaferMapper has been designed to meet these three goals. Its
central strategy is to first map the dataset using low resolution
imaging so that the time consuming process of high resolution
imaging can be intelligently targeted and automatically executed.
In addition, WaferMapper checks its own work -making sure that
it has successfully navigated to the correct position in the tissue
and that the images are of acceptable quality. Using this software,
we have been able to collect image volumes from a wide range of
ATUM-collected tissue libraries (including a mouse cortex UTSL,
a mouse cerebellum UTSL, a mouse thalamus UTSL, and a larval
zebrafish UTSL). These 3D image volumes ranged in size from
about 1 to 100 terabytes of image data and required the imaging
of many thousands of ultrathin sections.
All experiments were performed according to the guidelines of the
Harvard Animal Care and Use Committee. The tissue samples for
ATUM are standard EM blocks preserved using aldehydes, stained
with osmium tetroxide, and embedded in a hard resin (Hayat,
2000). For large volumes to be imaged quickly, good contrast is
essential. We often use a combination of the (R)OTO technique
for enhancing osmium staining en bloc and lead citrate post section staining (Tapia et al., 2012). It is important to note, that by
thickening and darkening membranes, this technique can make
synapses more difficult to identify in single sections. A great deal
of this ambiguity is resolved when a synapse is reconstructed in its
3D context. There are also a number of staining techniques that
can be used to enhance synaptic labeling. Regrettably, most EM
protocols were not developed to penetrate volumes that extend
for 100 s of microns in depth. The lack of uniformity in staining,
in general, can be a significant problem. More uniform staining
can be achieved by decreasing stain concentration and increasing incubation time. However, with any particular sample there
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is a significant risk that the stain will not be acceptable all the
way through the volume. One of the benefits of generating the
low resolution image series with WaferMapper is that the thousands of sections can be evaluated for tissue quality before the
more time-consuming high resolution imaging is begun. Once
high resolution imaging is completed, WaferMapper can also be
used to reimage ambiguous structures under different imaging
conditions or after further post section staining.
ATUM uses a reel-to-reel conveyor belt to collect sections from
the water boat of standard ultramicrotome diamond knives. Once
sections are cut and float into the water boat, they come in contact
with the inclined surface of the moving collection tape that juts
out of the water (Figure 1). Depending on the size of the tissue
block, 1000–10,000 sections can be collected over a 24 h period
with no human interaction. Once the ATUM sectioning and collection process is started, the operator typically leaves the room
and can check in on cutting remotely via video. Knife water level
is maintained automatically by a video feedback mechanism controlling a digital syringe pump. After sectioning about 100 µm of
tissue, the microtome reaches the end of its useful range and has
to be manually reset. At this time, the sample can also be moved to
a fresh position on the diamond knife so that knife sharpness does
not become a problem. In this way, large volumes can be sectioned
with only a single interruption every several thousand sections.
For some samples, it is possible to collect thousands of sections
without tissue loss. However, many factors such as heterogenetity
in the tissue, and knife dullness can result in sections breaking or
folding as they are being cut. For most connectomics applications
we are able to tolerate one or two damaged sections per hundred
as long as damage is not occurring in sequential sections.
The tape containing the sections is next cut into strips and
mounted on 100 mm diameter silicon wafers which are flat, conductive (doped) and vacuum safe. To adhere the tape to the wafer,
the surface of the wafer is covered with double sided conductive tape. Each section needs a path to ground or it will become
electrically charged during SEM imaging. This grounding can be
accomplished by thin-film depositing a carbon coating over the
entire surface of the wafer with tape strips attached (typically
works well with backscattered electron detection). If the tissue
will be imaged using voltages that cannot penetrate a carbon coating (see section: Imaging Hardware), use of a collection tape that
is pre-coated with a conductive layer is required. The top surface of the conductive tape can then be connected to ground
by using conductive tape or paint along its edges. Because this
approach also works with backscattered electron detection, we
are able to first use backscattered imaging to acquire large field
of view overview images with minimal field distortion (because
of high electron voltages) and then switch to secondary electron
imaging if it is optimal for the smaller field of view high resolution
imaging step.
Once the tape segments are mounted onto silicon wafers,
we affix fiducial markers (Copper Reference Finder TEM grids
style H6 from Ted Pella work well) to the double sided carbon
tape at the corners of the wafers. This is critical for the wafer
mapping process, described in detail below. A standard wafer
Frontiers in Neural Circuits
box (Figure 1) can hold 25 silicon wafers containing hundreds
of sections each, resulting in a 10,000-section UTSL that can be
stored in a desk drawer.
There are several SEM imaging systems commercially available
that could be used for imaging ultrathin section libraries generated by ATUM sectioning. The software presented here was
developed to drive off-the-shelf Sigma and Merlin SEMs (Carl
Zeiss Microscopy LLC, Oberkochen, Germany) fitted with Fibics
scan generators (Fibics Inc., Ottawa Ca.). All results presented
here were imaged with one of these microscopes. The Fibics scan
generator allowed for images to be acquired with pixel dimensions
up to 32 by 32 k. Both of these microscopes have multiple detectors that include an outside-the-lens secondary electron detector,
a below the lens backscatter detector, and an in-lens secondary
electron detector. We use the in-lens secondary electron detector
for high speed imaging because this detector’s response speed was
sufficient to keep up with high scan rates (10 MPS in the case of
the Sigma and 20 MPS in the case of the Merlin). When imaged
with this detector, our samples yielded the best signal using 1.7–
3.5 keV and therefore required collection on an ATUM tape having conductive coating rather than thin-film carbon coating the
sections after collection. We also found that the “depth of field”
mode with extended depth of focus available with the Merlin is
helpful for acquiring large fields of view of potentially uneven
surfaces. Both microscope types were also fitted with an Evactron
plasma generator (XEI Scientific, Redwood City, CA) to clean and
etch surface material before imaging tape affixed to wafers.
A consequence of automated sectioning is that far more tissue can
be cut than can currently be imaged at the highest EM resolution. Because this tissue is mounted on silicon wafers and imaged
with an SEM, it is relatively easy to image, store, and reimage
the tissue. It is possible, therefore, to treat ATUM cut samples
as an UTSL to be sampled on demand. For some experiments,
large target areas might be imaged once at high resolution. Other
experiments might involve various small high resolution volumes
being fit onto a low resolution map of the total tissue volume. The
experimental flexibility gained by the UTSL depends critically on
developing the means to allow easy navigation within the digital
volume. This section (Imaging strategy and workflow) contains
an outline of the intent of the low and high resolution imaging steps. The following section (Implementation: WaferMapper)
contains a detailed description of the WaferMapper implementation. Graphical depictions of some of the key concepts (UTSL,
wafer, fiducial, section overview image, aligned stack of overview
images, target point) can be found in Figure 2A.
The first step in generating a UTSL is mapping the positions of
each of the sections on all the silicon wafers. The low resolution
mapping step can be accomplished using either an optical image
or an EM image montage of the entire silicon wafer. Sections are
identified automatically and then any instances of unlabeled sections or debris labeled as sections are corrected manually. This
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FIGURE 2 | Overview of WaferMapper terminology, SEM system
integration, and Graphical User Interface (GUI). (A) Graphical depiction of
some of the key terms described in the text. (B) Block diagram showing how
one copy of the WaferMapper program is used to automate SEM imaging
while a separate copy is used to handle offline tasks such a browsing the
aligned section overviews stack and graphically planning montage imaging
volumes. A central UTSL Directory structure organizes all metadata and
images related to the library. (C) The WaferMapper GUI is organized around a
wafer-level overview display (left) and a section overview display (right). Each
procedure marks the position of each section. For the microscope to consistently find the mapped section positions, reference
points (fiducials) are imaged on each wafer (Figure 2A). Any time
a mapped wafer is loaded into the SEM, the fiducial points are
re-imaged and compared to the original fiducial images to determine the correction factor required to translate the wafer map
coordinate system onto the new wafer position.
The second step in creating a UTSL requires obtaining a more
detailed low resolution image of each section (but not the whole
wafer). For this imaging phase, the microscope automatically uses
the map of section positions obtained for each wafer (see above)
to drive the microscope stage to each section on a wafer and
obtain a “section overview” image (see Figure 2A). The overview
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tissue section on the wafer-level overview display is marked by a red cross
along with its section number. In the section overview display, a red cross
designates the target point for high-resolution imaging. In this case the red
cross is overlaid by a smaller blue cross designating where autofocuses
should occur (which, in general, can be offset from the target point). The
yellow box in the section overview display denotes the field-of-view used for
local target point alignment (see section: Target point setup). The red boxes
in the section overview display are the high resolution montage tile positions
defined in the montage parameters (see section: Montage parameters).
image generally includes all of the tissue in the section (typically several square millimeters) while using a pixel size just
small enough to identify features relevant to the future targeting of image volumes within the section (commonly 1 µm pixel
size). After acquiring the overview images they are digitally registered to each other to remove effects of inter-section rotation and
translation. The resultant “aligned stack of overview images” (see
Figure 2A) constitutes a 3D coordinate map of tissue locations
used for all subsequent navigation of the UTSL.
With the low resolution UTSL overview map it becomes possible to select one or more target regions for higher resolution
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imaging throughout the volume. A center position “target point”
(see Figure 2A) for a targeted region is recorded and in some cases
a second higher resolution local registration (setting translation
parameters only) is performed now with the target position at
its focus. At high resolution, a target region may require imaging
a mosaic of multiple overlapping images arranged in rows and
columns. Imaging the target region therefore requires defining
“montage parameters” (i.e., size of the imaging mosaic, resolution, and other imaging parameters). Once these parameters are
set, each wafer can be loaded onto the microscope stage and
imaged automatically.
High resolution data collection begins with loading a wafer
and imaging the fiducials to adjust the wafer’s section overview
map to register it to the new position of the wafer on the microscope stage. These adjustments are typically in the range of 100 s
of microns. With the montage parameters loaded, the microscope
now has sufficient information to acquire a series at high resolution for each loaded wafer. Once the high resolution imaging
step is initiated, the microscope automatically moves the stage to
the first section and then moves to the target position. The scan
rotation based on the stored parameters is initiated so that all the
sections are acquired in the same orientation. The microscope
then automatically adjusts focus and stigmation at the target
Correct focus and stigmation are critical for SEM imaging
of tissue sections spread across ATUM tape strips and adhered
to silicon wafers. The depth-of-field in typical SEM imaging is
small relative to typical wafer and tape mounting variability. The
beam focus must therefore be adjusted to match the z-position
of the tissue as the stage moves across millimeters of tissue and
centimeters of silicon wafer. Generating a high resolution beam
spot also depends on stigmators compensating for any aberrations in the focusing of the electron beam. Our experience is
that the optimal stigmation changes during an imaging period as
the focus depth and microscope conditions change. Acquiring a
dataset with consistent image quality therefore requires periodic
automated focusing and stigmation.
We find that, when we apply the focus algorithms currently
available on the Zeiss Sigma and Merlin, our focus quality is
unacceptable about 5% of the time. Therefore, high resolution
images undergo an automatic quality check which can trigger a
corrective focus and stigmation followed by re-imaging. Because
focus and stigmation can take as long as imaging time and failure rates can vary with tissue and imaging conditions, a variety of
focusing strategies are made available (described below). When
the high resolution imaging of a section is completed, the digital
data is automatically moved from a local data buffer to long-term
network storage.
If high resolution imaging is interrupted at any point, no data
is lost. The wafer can be removed from the microscope for storage and then reloaded when convenient. Because this procedure
exposes the wafer to air and can affect the microscope chamber and column conditions, there may be a delay of an hour
or so before imaging conditions are ideal. We have been able to
return to a wafer after years of bench-top storage for reimaging.
However, it is likely that storing a UTSL for many years without
degradation will require placing tissue sections in a desiccation
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or vacuum chamber. Tissue contrast can be altered by the initial imaging process (particularly at focus points), however this
change is usually not destructive as long as surface contamination
of the wafer is minimal.
The software that oversees the steps outlined above is a
MATLAB®-based program called WaferMapper. The MATLAB®
script allows researchers with limited programing background to
readily customize the code according to their particular needs.
The WaferMapper source code is freely accessible through a
Google code SVN server (https://wafermapper.googlecode.com,
See user guide for Matlab toolbox dependencies.) and we encourage any interested parties to participate in the further development of WaferMapper. A detailed, step-by-step user’s manual is
also available at this site.
In addition to the MATLAB® code, we provide two C wrappers for interacting with the Zeiss SmartSEM API (to control
the microscope) and the Fibics scan generator API (for acquiring
high pixel density images). Although WaferMapper was written to drive the Zeiss/Fibics SEM system, it can be adapted to
other imaging systems with the addition of appropriate command
wrappers. For those who wish to build their own imaging software, the following description of our implementation should still
be helpful as a practical guide to managing and imaging a UTSL.
Figure 2B is a block diagram showing how WaferMapper
interfaces with SEM hardware. WaferMapper can be run as a
standalone application for steps which do not require SEM control. A UTSL directory structure, stored on a network file system, organizes all metadata and images related to a particular
library. Figure 2C shows the WaferMapper graphical user interface (GUI). The GUI is organized around a wafer-level overview
display and a section overview display. The red crosses designate
the position of mapped sections. When WaferMapper is connected to the SEM and the currently loaded wafer is displayed, the
user can quickly move the SEM stage position to any point on the
wafer by clicking on the wafer image or to any point in a section
by clicking on and zooming in on the section overview display.
When WaferMapper is run on a computer not connected to the
SEM, the user can browse through all wafer images and through
the entire stack of aligned section overviews to graphically define
a target region for high resolution montage imaging.
Figure 3 is a flowchart showing all key steps in the creation and imaging of a UTSL using the WaferMapper software.
Conceptually, the process is broken into three key phases. The first
is an “SEM Wafer Mapping Phase” in which the software is used to
map out the locations of all sections across all wafers in the library,
and in which low resolution overview images of all sections are
acquired by automation of SEM stage movements and imaging.
The second is a “Target and Montage Definition Phase” in which
WaferMapper (usually being run on a non-acquisition computer)
is used to align all section overviews and is used to graphically
define a subregion for high resolution montage imaging. The final
phase is the “High Resolution Montage Imaging Phase” in which
WaferMapper automates the SEM operations necessary to acquire
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FIGURE 3 | Flow chart showing all key steps in the creation and
imaging of a UTSL using WaferMapper. The process is broken into
three phases: an “SEM Wafer Mapping Phase,” a “Target and Montage
the defined high resolution volume data. Each of these phases is
described in detail below.
Definition Phase,” and a “High-Resolution Montage Imaging Phase.”
IBSC, Imaged Based Stage Correction (see section: Starting high
resolution image acquisition).
may consist of tens or hundreds of wafers and many thousands
of sections, we have tried to automate the majority of the steps in
this process.
The goal of the “SEM Wafer Mapping Phase” is to produce a
set of images and metadata covering all wafers and all sections
in a UTSL. This collection of images and metadata (e.g., stage
coordinates of all section overview images and fiducial images,
pixel-to-stage calibration scaling factor, pixel size, dwell time, etc.)
allows reloading of wafers and automatic movements of the stage
to preselected target points within each section. Because a UTSL
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Acquiring a full wafer image
An image of the entire surface of the wafer is acquired before
mapping of sections begins. This image can be generated optically or using an electron microscope. The benefit of generating
the image in the electron microscope is that contrast will be based
on electron scattering, i.e., metalized tissue will stand out from
the background. However, full wafer imaging is time-consuming
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(∼20 min on the Merlin and 60 min on the Sigma) given that
the limited field of view of an electron microscope necessitates
acquiring many individual montage tiles. We have often found,
however, that even images of the wafer rapidly taken with an optical camera while the wafer is being lit indirectly via a diffuse white
background are of sufficient quality to serve as a full wafer image
so long as all of the ultrathin sections and fiducials are visible
in the image (Figure 4A). The regularity and resolution of the
full wafer image will determine how accurately the next stage of
mapping, overview image acquisition, can be targeted. We typically acquire overview images with a field of view approximately
a millimeter larger than the tissue section which corresponds
to about a half millimeter of fault tolerance in the full wafer
If WaferMapper already has access to an optical image of a
loaded wafer, the user can navigate to “Map Wafer Operations”
> “Acquire Full Wafer Montage” and select the option to load
that camera-acquired full wafer image. The full wafer image must
then be mapped into the microscope’s stage space by selecting
three or four fiducial points on the wafer’s image and manually
driving the stage to the corresponding locations. To create a new
wafer image within the SEM, the user can first select “Map Wafer
Operations” > “Wafer Parameters” to define the image resolution
and dwell time of the full wafer image. The user then navigates
to “Map Wafer Operations” > “Acquire Full Wafer Montage,”
defines the edges of the wafer image and begins montaged
Once a full wafer image has been mapped onto stage space or
acquired within the SEM, the wafer image can be used for navigation. The user can select “Map Wafer Operations” > “Free View”
and then click on any point in the wafer image to drive the stage
to the corresponding location.
Acquire images of wafer fiducial marks
At the core of a mapped UTSL is a set of related coordinate systems which allows every point in the aligned stack of section
overview images to be mapped back to a particular position of
the SEM stage. Since there is typically a significant offset introduced when reloading a wafer into the SEM, we also acquire a
set of images of fiducial marks that are permanently placed on
the corners of the wafer. The stage positions of these fiducial
points are used as a reference frame for all other stage positions. The first step of acquiring overview images is, therefore,
to image the fiducial points on the wafer first at low resolution (3 µm per pixel), then at high resolution (0.25 µm per
pixel). By selecting “Map Wafer Operations” > “Acquire Low
Res. Fiducials,” the user can use the full wafer image to navigate to fiducial points on the wafer and acquire images of these
points. These images can then be used any time the wafer is
reloaded to map section overview space back onto microscope
stage space.
Automap all sections
Once the fiducials have been imaged, the next step is to automatically determine the positions of all sections on the wafer. The
user selects “Map Wafer Operations” > “Acquire Example Section
Image” and either is directed to cut out an example image of a
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section from the full wafer image (if a full wafer optical image is
being used) or is able to acquire a new example image of a section
by SEM (if a full wafer SEM montage is being used). This example
section image is then used as a template which is scanned across
the full wafer image at a range of image rotations to pick out the
positions of the other sections with sufficient precision to drive
section overview imaging. This process is displayed in Figure 4
for an example wafer whose whole wafer image was obtained
by a full wafer SEM montage (left), and for an example wafer
whose whole wafer image was obtained by an optical camera
while the wafer was lit indirectly via a diffuse white background
The “Map Wafer Operations” > “Threshold image” command
calls up a new GUI (see Figure 4B) which is used to set upper
and lower gray scale thresholds to convert both the full wafer
image and the example section image into binary masks. Next the
user selects “Map Wafer Operations” > “Auto Map All Sections”
to open up the automap GUI (see Figure 4C). Within this GUI,
the example section binary mask is used as a convolution kernel
and convolved at multiple rotations across the full wafer binary
mask. The result is a heat map image whose “brightest” points
correspond to high correlations between the wafer image and
the example section image. Bright points in the heat map image
correspond to locations on the wafer image that resemble the
example section. The user selects a threshold for heat map image
and the centroids of image patches that pass threshold constitute the section locations (see Figure 4D). With mouse clicks and
zoom operations, the user can add or remove section positions to
correct any mistakes made by the automatic section finding. On
most wafers there will be a few additions and subtractions to the
section list. Once all of the sections are marked, the sections are
automatically assigned number labels according to their position
on the collection tape. Typically this automap process will find
and correctly label >95% of sections on a wafer. Sections that are
not identified automatically, usually because they lay close to a
high contrast edge or because two sections are too close together,
can be identified manually and added by the user with a few
mouse clicks.
Pixel-to-stage calibration
To use overview images of sections to direct stage positioning, the
relationship between pixel size and stage travel must be precisely
defined. Slight inaccuracies, arising from imperfect calibration of
the microscope, can result in noticeable errors in WaferMapper’s
ability to target the correct region of tissue. To compensate
for potential discrepancies between pixel size and stage travel,
we perform a pixel-to-stage calibration immediately before the
acquisition of the section overview images. This process produces
a pixel-to-stage conversion factor which is specific to the particular set of imaging conditions used for the overview images. The
pixel to stage conversion factor is determined by selecting “Map
Wafer Operations” > “Perform Pixel to Stage Calibration.” The
user is then prompted to select an image target. The microscope
takes an image of the target region using the same settings that will
be used to acquire the section overview images, moves the stage
a defined distance and then takes a new image. By comparing the
displacement in the images, a pixel-to-stage conversion factor is
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FIGURE 4 | WaferMapper process steps for automatically finding the
positions of all sections on a wafer. (A) Acquisition and registration of a full
wafer image to the SEM stage using either a full wafer SEM montage (left) or a
full wafer unprocessed optical image (right). (B) Thresholding of full wafer
image and example section image creating binary masks. (C) Convolution of
obtained and recorded as part of the metadata associated with this
Acquire section overviews
With fiducials mapped, sections identified, and the stage calibrated, WaferMapper has all the information it requires to begin
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the example section mask over the full wafer image creates a heat map whose
hottest points correspond to likely section locations. (D) After the user selects a
suitable threshold for the heat map, the program marks the locations of all
centroids as individual sections and numbers the sections in strips according to
the order the tape strips were collected on the ATUM machine.
acquiring an overview image of every section on the wafer. When
the user selects “Map Wafer Operations” > “Acquire Section
Overview Images,” WaferMapper drives the stage to the first section and begins acquiring images using the user-defined settings
for this UTSL. The default setting is to acquire 3 mm-wide images
with pixel sizes slightly smaller than 1 µm. These settings can be
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changed according to the size of the target sample, the accuracy of
the section targeting and the desired precision of the 3D map of
the UTSL. Depending on the number of sections and the imaging
parameters, the process of acquiring section overviews typically
takes 30 min–3 h per wafer depending on the number of sections
and the desired overview image quality.
Target point setup
Repeat for all wafers
This mapping procedure is repeated for every wafer in the UTSL.
Figure 5 shows an example of a fully mapped UTSL consisting
of 2637 sections of mouse cerebellum tissue spanning 16 wafers.
The mapping procedure for this UTSL was based on optical camera images of each wafer. Figure 6 shows an example of a fully
mapped UTSL consisting of 1025 sections of mouse cortex tissue
spanning 11 wafers. The mapping procedure for this UTSL was
based on full wafer SEM montage images of each wafer.
With the mapping data acquired, the section overview images can
now be assembled into a 3D map of the tissue volume in which
high resolution imaging targets can be selected.
Align section overviews
Each section overview image must be aligned to its neighbors
across all wafers in order for the stack of images to be treated as
an image volume. Here we describe the cross-correlation based
alignment strategy that we have used to acquire all data sets to
date. For this method, we find the y translation, x translation
and rotation that produces the highest cross-correlation value
between two images.
Typically, the section overviews for a particular wafer are
aligned on a non-acquisition computer with access to the UTSL
directory while other wafers are being imaged. To align images,
the user first selects a template image either from the current
wafer (if this is the first wafer mapped in the UTSL) or from
the previous wafer which has already undergone section overview
alignment. By aligning each wafer to a section in the previous
wafer, a single aligned stack is created spanning all wafers in the
At this stage in alignment, each section on the wafer is aligned
to the selected template image. The section-to-section registration produced by this alignment is not as good as an alignment
procedure that compares neighboring sections, but the template
matching has the advantage of not accumulating drift and being
robust to single “problem” sections in the stack. The automated
alignment of each wafer usually takes 10–30 min using 3.2 GHz
processors on a standard desktop computer.
Any mistakes in the alignment can be corrected using “Section
Overview Processing” > “Check and Correct Alignment GUI,”
which calls up an easy to use GUI in which alignments can be
quickly reviewed and corrected by simple mouse drags. It is not
necessary, at this point, that the resulting alignment is perfect. The
alignment only needs to be good enough that, when an imaging target point is chosen on one section overview, a second
stage of target point alignment (described below) can access the
appropriate region of each section overview.
By following the above procedure, a small UTSL can be
mapped in several hours. A larger dataset, consisting of ∼10,000
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sections might take closer to 1 weeks (working 8 h per day). The
end product of the mapping process is a set of full wafer images
that can be used to navigate around each wafer and a low resolution 3D image volume of the tissue sections that can be used to
direct high resolution image capture.
The first step of defining a target point for high resolution imaging is to choose an XY position from within a section overview
image. This XY position, the “target point,” will be used as the reference point for targeting high resolution imaging and will serve
as the center of a second stage of more precise local alignment of
the section overviews. The user selects a target point by loading
a UTSL and wafer into WaferMapper and selecting “Target Point
Setup” > “Choose Target Point in Aligned Section Overview.” The
user is then prompted to click on a point within the displayed section overview image and can save the target point for later use.
Unlike the previous steps in the wafer mapping process, in which
it is expected that a single map is generated for a given UTSL,
the selection of target points is a branch point where many target
points can be defined, one for each high resolution subvolume to
be imaged.
Once a target point is selected, a new targeted alignment is executed by selecting “Target Point Setup” > “Generate and Save List
of Aligned Target Points.” For this alignment, a relatively small
window is extracted from each section overview image. Each subregion of the section overview images is aligned to a running
average of previously aligned subregions. This process takes about
10 min per wafer. The goal of this second stage of alignment is
to produce a better local section to section registration than can
be generated from an alignment of the entire section overview
images themselves. Once this alignment is completed, any mistakes can be corrected using the “Target Point Setup” > “Check
and Correct Target Point Alignment” GUI.
The results of each target point alignment are stored in a
new Aligned Target List subdirectory in the UTSL containing all of the aligned subregion images for use in image-based
stage correction (IBSC), described below, and a new datafile
called “AlignedTargetList.mat.” “AlignedTargetList.mat” contains
the pixel offsets needed to align each of these cropped subregions.
These pixel offsets (when combined with the pixel-to-stage calibration factor determined during the mapping phase) will be used
in the high resolution imaging phase to quickly position the SEM
stage for imaging each section.
Montage parameters
The position, dimensions and imaging conditions of each high
resolution dataset are defined within “Montage Setup” > “Set
Montage Parameters.” The central position of each image montage is set relative to the aligned target point by defining an X
offset, Y offset and North Angle. The dimensions of the montage are set by defining the field of view of each tile (“Tile FOV”)
and the number of rows and columns of tiles in each montage.
Additionally, the overlap between tiles is defined here. For our
systems, four micrometers of overlap was sufficient to consistently acquire images without gaps between tiles. WaferMapper
provides a graphical overlay of the montage tile positions on top
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FIGURE 5 | Example of a 2637 section UTSL of mouse cerebellum tissue
spanning 16 wafers. An optical image of each wafer was taken and used as the
basis of automapping all sections. (A) Optical image of wafer 8 (right), filtered
of its section overview display (Figure 7), allowing the user to
scroll through the entire stack of section overviews and graphically check placement of the montage tiles across all sections prior
to the start of a long imaging run.
The pixel size (“Tile FOV”/“Tile width”) and pixel dwell time
must be set to achieve the necessary balance between image scanning time and image quality. Using high contrast staining and the
in-lens secondary electron detector we were able to obtain images
with acceptable noise levels using the maximum scan speed of our
microscopes (50–100 ns), however these results depend heavily on
the tissue preparation and imaging configuration. The Fibics scan
generator allows image sizes up to 32 × 32 k pixels, which means
100 µm-wide images can be acquired at 4 nm per pixel resolution.
Being able to scan large field-of-view images at high resolution
reduces the impact of tile to tile overhead on image acquisition time and generally increases the efficiency of managing large
WaferMapper includes the option to take an overview image
of the targeted imaging region before the high resolution
imaging begins. Being able to take advantage of this option
requires that the microscope setup that is used to acquire
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optical image with automapped positions of all sections labeled (left). (B)
Graphical depiction representing the stack of 2637 overview section images
acquired during the mapping phase. (C) All 16 automapped wafers in this UTSL.
high resolution images is also amenable to large field of view
Once the section overviews have been acquired, the wafer can
be removed from the microscope and stored. When the wafer is
placed in a new SEM or returned to the same one, the position
of the wafer on the stage will not be exactly the same as when
the wafer was mapped. To bring the wafer map into register with
the new position on the stage, the user can follow the steps listed
under the “Reload Wafer Operations” menu. The first step is to
manually set the coarse offset of the stage. The microscope drives
to the first fiducial point on the wafer and the user manually
rotates and translates the stage to correct for any gross change in
position. “Free view with Offset” can then be used to confirm that
the new wafer position is roughly correct. Once the stage rotation
and translation have been adjusted within approximately a millimeter of the mapped position, the user can then run “Do all
steps for Stage Correction.” The microscope will then automatically drive to the fiducial positions, take new images and compare
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FIGURE 6 | Example of a 1025 section UTSL of mouse cortex
tissue spanning 11 wafers. A full wafer SEM montage image of
each wafer was taken and used as the basis of automapping all
sections. (A) Full wafer SEM montage of wafer 6 (right), same
these images with the original images of the fiducials. This comparison is used to find a coordinate transformation that will be
used to translate between stage space and wafer map space for the
remainder of the imaging session. This ability to automatically
register a reloaded wafer is also crucial when it is necessary to
retake images, when imaging a new region, and when sharing
a UTSL between different laboratories. Once reloaded, a wafer’s
metadata, in principle, contains all the information necessary for
another lab with the same SEM setup to replicate an imaging run.
Quality check
The ability to acquire a high quality large-scale SEM dataset
depends heavily on imaging with the correct focus and stigmation settings. The depth of field of the SEM is typically around
∼0.5–10 µm, depending on the imaging modality, so that multiple focus points might be required to image a large montage.
Depending on the sample, imaging conditions and stability of
the microscope, periodic refocusing and restigmating might be
required regardless of the sample flatness. To acquire images for
days without human intervention, WaferMapper uses a variety of
strategies to minimize blurry images.
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image with automapped positions of all sections labeled (left). (B)
Graphical depiction representing the stack of 1025 overview section
images acquired during the mapping phase. (C) All 11 automapped
wafers in this UTSL.
WaferMapper includes an algorithm that evaluates the quality
of SEM images. We designed this algorithm to be able to quickly
judge the quality of very large images and then to base its evaluation not on the average quality of the image, but on the quality
of the best regions of the image. By only paying attention to areas
with the most high frequency contrast, quality values are less sensitive to changes in tissue statistics that might come from blood
vessels or section edges. For the quality check to read and analyze images without adding to the tile-to-tile overhead time, the
quality check samples only a small fraction of the available pixels.
Quality check reads in a grid, usually 200 × 200, of 3 × 3 pixel
image kernels from a newly acquired image (Figure 8A). These
samples are then fit into a 200 × 200 by 9 image volume.
To find a quality value for each grid point, we find the relative contrast of different patterns within the 3 × 3 pixel kernel.
One set of patterns finds the difference in the average intensities
of adjacent lines of pixels (two horizontal comparisons and two
vertical comparisons). The other set of patterns compares the difference in the average intensities between groups of interleaved
pixels (Figure 8B). A well-focused SEM image of cell membranes
will tend to produce relatively more contrast in the adjacent lines
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FIGURE 7 | Examples of graphically defining an imaging target and
montage. (A) Image shows the WaferMapper GUI being used to
graphically define a 3 × 3-tile montage (red boxes) covering just the brain
region in a larval zebrafish UTSL. (B) Image shows WaferMapper used to
graphically define a 3 × 7-tile montage (red boxes) covering a single
worm in a C. elegans UTSL. Note the ability to selectively designate a
subset of montage tiles for imaging. Of crucial importance is the
pixel patterns than in the interleaved pixel patterns. Once a quality value is obtained for each grid point, a quality value for the
entire image is obtained by averaging a small percentage of the
best grid points. In this way, changes in tissue statistics have a relatively small effect on the quality rating. This method can also
be used to identify poor quality regions of the tissue or of the
imaging field.
Within WaferMapper’s “Montage Parameters” setup menu,
the user can choose to have the quality check performed at several points in the image acquisition process. First, a quality check
can be performed after each autofocus to determine if the correct working distance has been obtained. After each autofocus,
WaferMapper can take a quick image and perform a quality
check. If the quality value exceeds the user-defined threshold, high
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software’s ability allowing the user to scroll through the entire stack of
section overviews to graphically check placement of the imaging
montage across all sections prior to the start of any long imaging run.
The yellow box in the section overview display denotes the field-of-view
used for local target point alignment (see section: Target point setup).
The small blue cross denotes the autofocus position to be used, which
in this case is deliberately offset from the center of the montage.
resolution imaging begins. Otherwise, the image is refocused.
Second, the user can choose to have a quality check performed
after each tile is acquired. If the image quality fails to pass a user
defined threshold, the image is refocused and retaken. Finally, the
quality values of each tile are displayed on the down-sampled
stage-stitched image of the montage (Figure 8C). Stage stitched
images, with the quality values displayed on top, provide an easy
way for the user to monitor the imaging process and to review the
performance of the microscope.
Focusing strategies
The best possible image quality will usually be achieved by
autofocusing, then autostigmating and then autofocusing again
(focus-stig-focus) before each tile is acquired. However, this
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FIGURE 8 | Structure of quality check procedure. (A) A 200 × 200 array
of 3 × 3 pixel kernels is extracted from an SEM image. (B) Structured
(top) and unstructured (bottom) contrast patterns are compared to obtain
procedure can take significantly longer than the acquisition of
a single tile. Therefore, a variety of strategies are offered in
WaferMapper for increasing the efficiency of high throughput
data collection. In many cases a single autofocus before each
montage will be sufficient to produce acceptable image quality.
For large montages a three-by-three grid of focus points can be
acquired and then fit to a plane that predicts the optimal focus
point for each tile of the large montage. Alternatively, tiles can be
pooled and a central focus point acquired before a two-by-two
box of tiles is acquired.
In a typical example we might choose to focus-stig-focus once
per section as long as image quality stays above threshold. When
the microscope starts a new section it will first drive to a predefined central focus point within the montage. The microscope
then takes a quick image approximately the size of an image tile
and drives to the region within the image with the highest contrast. In this way, WaferMapper avoids autofocusing on regions,
such as the interior of blood vessels, which provide no useful
information to the focus algorithm. The microscope then performs a focus-stig-focus and begins imaging the first section. The
order in which the tiles are imaged is determined by the proximity
of the focus point so that, if refocusing is required, best advantage is taken of each focus point. Once an image is acquired, the
quality is evaluated. If the tile passes, the microscope moves onto
the next tile. If the tile fails, the microscope refocuses and takes
the image again. At any point in the imaging process a user can
review either the images or the quality values being produced by
the microscope and assign sections to be retaken. This strategy
of only focusing once if the quality values stay above threshold
works well when image acquisition time is small relative to the
time it takes to autofocus and when the majority of tiles can be
imaged using a single focus point.
Starting high resolution image acquisition
With the aligned target points loaded and the montage parameters set, WaferMapper is ready to acquire images. The user
selects “Montage Setup” > “Acquire Montage Stack Main” and
is prompted to select a target directory. This target directory can
be anywhere; however, image writing and quality check work best
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a quality value for each 3 × 3 pixel kernel. (C) Stage stitched montage of
images displayed with quality values in green (passing) and red (below
if the data is saved on a local solid state drive. This data can then
be managed and transferred through a network connection to a
large data server. In addition to writing the images in this directory, a log file is written that records all stage movements, image
qualities and image conditions.
When starting image acquisition, the user will also be asked
whether or not WaferMapper should use IBSC. The accuracy of
targeting we were able to achieve using wafer fiducials only was
usually limited to about ±15 µm. We found that we could significantly improve this accuracy using IBSC. When this option is
selected, WaferMaper acquires a quick image every time it drives
to a new section. This image is processed using a difference of
Gaussians filter to enhance features on the scale of cell bodies.
This section’s aligned subregion target image (cutout of the section overview image after local alignment during the Target Point
Setup step, see section: Target point setup) is likewise filtered and
compared with the newly acquired image using cross correlation.
If the stage movement was completely accurate, then the two
images should match exactly. If they do not, then WaferMapper
uses the offset obtained from the cross-correlation to adjust the
stage position and then checks its work with a second image.
The accuracy with which this second image matches the section
overview target is also recorded within the log file.
Data sets have been acquired using WaferMapper that range in
size from 1 to 100 terabytes. Figures 9, 10 show some example images from WaferMapper runs of a mouse cerebellum
UTSL and a mouse cortex UTSL. These figures illustrate the
range of scales which must be spanned by the mapping software in order to precisely target automated imaging of a small
montage volume within the much larger volume of the full
UTSL. Below we describe the mapping, imaging and data set
acquired from one test of WaferMapper on the mouse cerebellum UTSL shown in Figure 5. Table 1 provides actual average
data acquisition times and rates for the various steps in the mapping and imaging process. These times were recorded during a
separate, multi-month imaging project using the WaferMapper
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FIGURE 9 | Cerebellum UTSL example data. (A) Image of WaferMapper
GUI showing a graphically-defined 3 × 5-tile montage targeted near the
surface of a cerebellar folium. Zoom overlays show high resolution images of
that region. (B) Montage images of three successive ultrathin sections in this
The cerebellum of an adult mouse was cut into several 300 µmthick vibratome sections and stained with osmium tetroxide and
uranyl acetate. The tissue was embedded in Embed 812 (Electron
Microscopy Sciences). A block containing one of these vibratome
sections was trimmed to include a 1 × 3 mm wide region of
cerebellum. The blockface was trimmed so that the leading and
trailing edges of each section come to a point. This shape helps
minimize cutting disruptions caused by the contact of the block
face with the diamond knife and the removal of the section from
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cerebellum UTSL acquired automatically by WaferMapper (scale bar =
100 µm). Red arrow shows location of corresponding high resolution images
shown in (C). (C) Cut outs of high resolution data imaged at 10 MPS and
aligned in FIJI (scale bar = 1 µm). Asterisks mark position of two synapses.
the edge of the diamond knife. Eight thousand ultrathin sections
were cut at a thickness of 30 nm and collected on carbon coated
Kapton tape. A subset of this ATUM run’s tape containing 2637
sections was cut into strips and mounted on 16 wafers as shown
in Figure 5.
The WaferMapper software was used to map these 16 wafers and
acquire overview images of each section to generate a low resolution 3D map of the tissue. Viewing the aligned overview stack in
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FIGURE 10 | Progressively higher resolution images through a mouse
cortex UTSL mapped and imaged by the WaferMapper software. (A)
Single section of the 1025 section mouse cortex UTSL whose wafers are
displayed in full in Figure 6. This image is from a screen capture of the
WaferMapper program showing the location of a 3 × 4-tile montage
overlaying a target region. (scale bar = 1 mm). (B) Zooming in on the section
overview display in WaferMapper allows the graphical display of the montage
to be finely positioned relative to blood vessel and cell body landmarks visible
WaferMapper, an imaging target point was selected from a region
of the molecular layer of the cerebellum where the arbors of the
Purkinje cells were parallel to the plane of microtome sectioning.
By selecting “Target Point Setup” > “Generate and Save List of
Aligned Target Points,” the software was used to generate a second, local alignment, suitable for directing the high resolution
Using WaferMapper’s GUI display of the aligned overview stack,
we graphically defined a high resolution imaging montage that
encompassed the arbors of several Purkinje cells (Figure 9A). The
montage consisted of three rows and five columns of tiles. Each
title consisted of 12, 800 × 12, 800 pixels resulting in a total 4 nm
resolution montage that covered about 250 × 150 µm of cerebellum. High resolution images were collected from 498 sections
spanning three wafers and ∼15 µm of cerebellum (Figures 9B,C).
The acquisition of the high resolution data required ∼100 h of
microscope time on a Zeiss Sigma. Approximately one third of
this acquisition time was consumed by scanning voxels at 10 MPS.
The remaining acquisition time was spent primarily on autofocusing. We found that this imaging time could be significantly
reduced on a Zeiss Merlin due to its faster scan speed and larger
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at the resolution of the section overview images. (scale bar = 100 µm). (C)
Zooming in again, this time to a small region of one image from a larger stack
of images automatically acquired by WaferMapper of this same UTSL. The
outlines of two neuronal processes sharing a synapse are shown highlighted
in color. (scale bar = 1 µ). (D) Graphic displaying every 10th image from this
same aligned dataset acquired by WaferMapper. ATUM sections were cut at
30 nm thickness, thus these images are displayed here at 300 nm intervals
through the tissue.
We wrote WaferMapper in MATLAB® so that researchers who are
not primarily programmers could readily add to the code according to the needs of their experiments. To date, each large dataset
acquired with WaferMapper has involved modifications of the
code. While the core version of WaferMapper we have released
should be able to acquire most types of data, we see collaborative
development of the code as critical to WaferMapper’s usefulness
as new technologies and new uses for ultrathin section libraries
WaferMapper meets our initial simple goal of producing a
data acquisition pipeline in which more time is spent acquiring image montages than is spent finding the right place to
image. However, even during the production of this software,
there have been significant improvements in SEM scan speed and
more improvements are on the way. The future development of
WaferMapper will hopefully produce streamlined versions of the
code as well as a version with fewer Matlab toolbox dependencies. At the time of the submission of this publication, the most
pressing areas for further code development are finding faster and
more reliable methods of both focusing the microscope, judging
image quality, and aligning section overviews and target points
(Table 1). A branch of the code being developed, primarily by
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Table 1 | Breakdown of the time required for each step of data
acquisition using ATUM-SEM and WaferMapper.
Sample preparation
Time (varies with tissue size and
staining method)
Tissue processing
1–2 weeks
8–30 s/section
Constructing wafers
∼30 min/wafer (100–500 sections per
Wafer mapping
Time per wafer
Full wafer image (optical)
3 min/wafer
Wafer loading into chamber
5 or ∼45 min/wafer (with or without load
Full wafer image (EM
alternative to optical)
20, 60 min/wafer (Merlin, Sigma)
Forrest Coleman, aligns SURF points over a large number of sections as an alternative to cross correlation to improve the targeting
of high resolution imaging within UTSLs. We find that this solution is often more robust than the current implementation of
cross correlation. This branch of the code and others will be made
available through the Google Code SVN server. We encourage
other users of the WaferMapper software to test code and to add
to the repository as new solutions are developed.
Section mapping
∼10 min/wafer
Section overview image
30–120 min/wafer (depending on desired
image quality)
Offline wafer mapping*
Time per wafer (using 2 Intel Xeon
x5672 3.2 GHz 4 core processors)
Section overview alignment
10–30 min/wafer
Manual correction of
overview alignment
0–20 min/wafer
Target point alignment*
10–30 min/wafer
Manual correction of target
point alignment
0–20 min/wafer
High resolution imaging
Time per wafer
Wafer load into microscope
5 or ∼45 min/wafer (with or without load
WaferMapper reload
∼10 min/wafer
Montage setup
∼5 min/wafer
Montage time
Movement to section
∼10 s
Image based stage correction
∼1 min
∼2 min
Pixel dwell time
50–3000 ns (depending on tissue signal
and detector)
Time between tiles (with
quality check)
Estimated project
High speed imaging of 10,000
sections using 100,000 × 100,000
pixel montages
Raw pixel scan time
57 days
Montage acquisition time
(with overhead)
104 days
Total data acquisition time
(start to finish)
130 days
Asterisks indicate steps that could significantly benefit from further software
Frontiers in Neural Circuits
Small EM volumes (<1 terabyte) can be aligned on a powerful desktop computer using publicly available alignment software
such as the registration plugins for Fiji (Schindelin et al., 2012).
However, the stitching and alignment of high resolution images
becomes increasingly difficult as data sets become larger. The
computational power required to manipulate and process terabytes of images requires hardware that is not standard in most
labs and, while most steps in alignment are amenable to parallelization, running these steps in parallel often requires changes in
code and expertise in managing clusters. Because of these problems, aligning multi-terabyte datasets is currently being done by
only a few groups. However, the recent production of many multiterabyte EM volumes has spurred efforts to scale up alignment
tools to make it easier for the broader research community to turn
hundreds of terabytes of EM images into usable 3D tissue maps.
A major goal of the integration of automated tape collection
and automated imaging of sections is to make volume EM easy
enough to be a standard technique that many labs can use to study
biological samples that are tens or hundreds of micrometers wide.
However, we also wish to stress the potential of UTSLs to allow a
new type of collaborative neuroscience. As discussed above, the
ATUM in a few days of cutting can potentially produce so many
ultrathin sections that it might take decades to image them in total
at high resolution. A typical research publication using ATUMSEM and WaferMapper might end up acquiring high resolution
images from only 1% of the total volume of a collected UTSL.
For example, in the visual thalamocortical slice case outlined in
the introduction one researcher may end up imaging and sparsely
tracing only a finely targeted 300 × 300 × 300 µm volume in
cortical layer IV -mapping out the local connectivity of thalamic afferents and interneurons in that area. Once this research is
published, other labs may wish to build upon this connectomics
data by performing additional imaging and tracing in neighboring regions of the same brain, literally starting their tracing work
from the very same neurons in this already published study. In
this way, a collaboration of multiple research labs could muster
the time and resources necessary to elaborate the connectomes of
larger inter-regional circuits than any one lab could by working
In this paradigm, some research labs might specialize in producing UTSLs with the highest quality ultrastructure preservation
and staining, encompassing brain regions that are of interest to
many labs (for example, a visual thalamocortical slice UTSL, a
barrel cortex UTSL, a hippocampal slice UTSL, etc.). Some of
these UTSLs may even be designed to include prior functional
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Hayworth et al.
Imaging ATUM ultrathin section libraries
imaging to augment expected connectomics studies. This design
of UTSLs tailored for wider research interest would be similar
to the way some labs today specialize in the creation of transgenic animals designed specifically for wider research use. Other
labs would then specialize in curating and EM imaging these
UTSLs, providing (perhaps for a fee) the highest quality 3D volume data on request of research groups. This strategy would
be similar to the way some groups in the astronomical community specialize in the design and construction of the highest
quality telescopes whose specifications far outstrip the funds and
resources of any single astronomical research group. We would
like to argue that, for truly large-scale cellular connectomics, the
neuroscience community has reached a similar need for pooling
of resources, and a similar need to create dedicated “Connectome
Observatories” whose high-quality, large volume EM imaging
abilities are designed to be shared by the entire neuroscience
There are many challenges to imaging ultrathin sections that are
absent from technologies that image intact tissue (such as confocal imaging, SBEM, or FIB-SEM). However, these challenges
can be overcome and even turned into advantages if software is
available to map a tissue library prior to high resolution imaging. With WaferMapper, we were able to target the acquisition
of large volumes of high resolution images from tissue libraries
consisting of many thousands of ultrathin sections. We are hopeful that this software will continue to develop within an open
source community as it is adapted to new experiments and imaging systems. More generally, we believe a multi-scale mapping and
imaging approach is key to taking advantage of the large number
of ultrathin sections that can now be generated using ATUM.
We would like to thank Juan Carlos Tapia and Dan Bumbarger
for providing some of the tissue used in the example
images. This work was supported by NIH/NINDS (High
Resolution Connectomics of Mammalian Neural Circuits,
TR01 1R01NS076467-01) and NIH (Imprinting a connectome: developmental circuit approach to mental illness, Conte
1P50MH094271-01, NIH 5T32MH20017, NIH 5T32HL007901).
The content is solely the responsibility of the authors and does not
necessarily represent the official views of the National Institutes of
Frontiers in Neural Circuits
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Conflict of Interest Statement: Harvard University has applied for patents covering
some aspects of the ATUM-SEM process. The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Received: 10 April 2014; accepted: 05 June 2014; published online: 27 June 2014.
Citation: Hayworth KJ, Morgan JL, Schalek R, Berger DR, Hildebrand DGC and
Lichtman JW (2014) Imaging ATUM ultrathin section libraries with WaferMapper:
a multi-scale approach to EM reconstruction of neural circuits. Front. Neural Circuits
8:68. doi: 10.3389/fncir.2014.00068
This article was submitted to the journal Frontiers in Neural Circuits.
Copyright © 2014 Hayworth, Morgan, Schalek, Berger, Hildebrand and Lichtman.
This is an open-access article distributed under the terms of the Creative Commons
Attribution License (CC BY). The use, distribution or reproduction in other forums is
permitted, provided the original author(s) or licensor are credited and that the original
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use, distribution or reproduction is permitted which does not comply with these terms.
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