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Large Image Microscope Array for the Compilation of Multimodality Whole Organ Image Databases.

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THE ANATOMICAL RECORD 290:1377–1387 (2007)
Large Image Microscope Array for the
Compilation of Multimodality Whole
Organ Image Databases
EMAN NAMATI,1,2 JESSICA DE RYK,1,3 JACQUELINE THIESSE,1,3
ZAID TOWFIC,4 ERIC HOFFMAN,5 AND GEOFFREY MCLENNAN1,3*
1
Department of Internal Medicine, University of Iowa Hospitals and Clinics,
Iowa City, Iowa
2
Department of Informatics & Engineering, Flinders University, Adelaide, Australia
3
Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa
4
Department of Electrical Engineering, University of Iowa, Iowa City, Iowa
5
Department of Radiology, University of Iowa Hospitals and Clinics, Iowa City, Iowa
ABSTRACT
Three-dimensional, structural and functional digital image databases
have many applications in education, research, and clinical medicine.
However, to date, apart from cryosectioning, there have been no reliable
means to obtain whole-organ, spatially conserving histology. Our aim was
to generate a system capable of acquiring high-resolution images, featuring microscopic detail that could still be spatially correlated to the whole
organ. To fulfill these objectives required the construction of a system
physically capable of creating very fine whole-organ sections and collecting high-magnification and resolution digital images. We therefore
designed a large image microscope array (LIMA) to serially section and
image entire unembedded organs while maintaining the structural integrity of the tissue. The LIMA consists of several integrated components: a
novel large-blade vibrating microtome, a 1.3 megapixel peltier cooled
charge-coupled device camera, a high-magnification microscope, and a
three axis gantry above the microtome. A custom control program was
developed to automate the entire sectioning and automated raster-scan
imaging sequence. The system is capable of sectioning unembedded soft
tissue down to a thickness of 40 mm at specimen dimensions of 200 3 300
mm to a total depth of 350 mm. The LIMA system has been tested on
fixed lung from sheep and mice, resulting in large high-quality image
data sets, with minimal distinguishable disturbance in the delicate alveolar structures. Anat Rec, 290:1377–1387, 2007. Ó 2007 Wiley-Liss, Inc.
Key words: multimodal registration; 3D microscopy; 3D histopathology; serial sectioning; whole organ histology;
lung imaging
The combination of both destructive and nondestructive imaging modalities when spatially registered, can
provide a useful map of anatomical structure with the
potential to provide functional insight. Undertakings
such as the Visible Human Project have created a multimodal, three-dimensional (3D) representation of a male
and a female cadaver (Hohne et al., 1995). The primary
modalities include computed tomography (CT), magnetic
resonance imaging (MRI), and cryosectioned digital
pathology images. These data sets were created as a
common reference point for the study of human gross
Ó 2007 WILEY-LISS, INC.
anatomy and have found applications in diagnosis, education, virtual reality, and industry (Spitzer et al., 1996).
The Visible Human Project cryosections feature one digi*Correspondence to: Geoffrey McLennan, Internal Medicine,
University of Iowa, 200 Hawkins Drive, C325 GH, Iowa City, IA
52242. Fax: 319-353-6406. E-mail: geoffrey-mclennan@uiowa.edu
Received 22 December 2006; Accepted 20 July 2007
DOI 10.1002/ar.20600
Published online 13 September 2007 in Wiley InterScience (www.
interscience.wiley.com).
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NAMATI ET AL.
tal image per slice of tissue, such that the resolution of
the digital pathology images are comparable to that of
the other imaging techniques used. When studying gross
anatomy, this resolution provides an acceptable degree
of detail. However, in situations where fine structural
details, more specifically histology, is needed, then
higher resolution images are required.
Visual investigation of specific organ tissue in normal
and diseased states is commonly performed using some
form of histology. Traditional histology and pathology
techniques introduce destructive and spatial artifacts.
Recent advancements in microscopy have been made
that incorporate some automation in the collection and
analysis of pathology images. The commercially available Ariol SL-50 (Applied Imaging, San Jose, CA) is one
example of an automated system incorporating microscopy, digital image acquisition, archiving, and image
processing for microscope slides. A more complete automated system developed by Fernandez-Gonzalez et al.
(2002) provides a means for 3D morphological and molecular analysis of thick tissue specimens, through sequential high-resolution acquisition of histology slides,
prepared serially through a large specimen such as a
whole gland. Their approach has been successful for histology slides, but inherently requires semiautomated
registration between slices due to the shrinkage artifacts
caused through preparation of the slides.
The most significant restrictions in histology, in both
manual and automated techniques for representing
whole-organ pathology, are twofold. First, the pathology
sections are small in relation to the spatial volume of the
organ. Second, the discrete pathology samples are 2D,
with little volumetric spatial correspondence. This finding
means that direct comparison of the microscopic structure
to 3D imaging modalities is difficult and tedious at best.
Clearly a need exists to improve current techniques
for evaluation of organ structure between large-scale
macroscopic sections to small-scale microscopic histology.
Such spatial correspondence is of paramount importance
and must be maintained for accurate correlation to be
successful across different 3D modalities.
Our aim was to generate a system capable of acquiring
high-resolution images, featuring microscopic detail that
could still be spatially correlated to the whole organ. To
fulfill these objectives required the construction of a system physically capable of creating very fine whole-organ
sections and collecting high-magnification and resolution
digital images. The system magnification had to be variable to reach the desired level of microscopic structure in
an organ while minimizing the size of the data sets to
image a large-scale organ volume. We chose the lung as
our organ of interest, which created several challenges—
the lung is not a solid organ, and its structure is delicate
and hard to maintain using standard histology fixation,
be that chemically or through cryo techniques.
METHODS
Microtome Development
Due to the lack of an appropriate sectioning instrument for large unembedded soft tissue in the commercial
market, a Leica SM2500M manual microtome was purchased and modified as shown in Figure 1. The original
microtome was designed for large-scale, heavy-duty sectioning of specimens embedded in paraffin or resin.
Movement of the stage was completely manual, controlled by means of a hand crank assembly. The stage
moves toward the fixed knife assembly and is retracted
after each section. The knife assembly was re-designed
to house a custom-built large-scale vibrating blade to
smoothly section soft tissue without the need for tissue
embedment. This path was taken, because tissue processing for embedding, in particular paraffin, causes
many of the artifacts seen in lung histology. Embedding
with resins is not an option for such a large volume, due
to heat build up. In addition, to maintain vertical alignment between sections, images would be acquired en
bloc (from the surface of the remaining tissue). Precise
control over the microtome tissue stage position was,
therefore, necessary to ensure alignment between subsequent sections.
Microtome Motorization
We motorized the tissue stage so that a uniform cutting speed could be maintained through the sectioning
process, to facilitate greater repeatability and reliability.
By replacing the manual control with electronic control,
the tissue stage was also interfaced with a computer for
automation. A 200-step per revolution Slo-Syn (Superior
Electric, Connecticut) stepper motor and 6:1 gear system
was integrated for controlling the tissue stage through
the sectioning process. Because image acquisition was
performed off the cut tissue block and the maintenance
of alignment in the z-axis was paramount, a high-precision stopping mechanism with micron accuracy was also
introduced.
Vibrating Blade Microtome Development
Extensive experimentation was conducted to develop
an optimal vibrating knife system, for sectioning nonembedded tissue. Although high-frequency motion was
effective in cutting through the majority of tissues, some
difficulty was experienced in simultaneously sectioning
the delicate lung structure and dense airway walls.
Hence, the vibrating blade was designed to vibrate at
both a high frequency and a low frequency. Figure 2 represents a schematic of the vibrating knife assembly. Leaf
springs connect the base bracket to the vibration frame,
allowing horizontal motion while restricting vibration
vertically. The vibration frame holds an air pressuredriven linear vibrator, which produces a high-frequency
variable linear oscillation by means of a piston and back
trap manifold. Mounted on the base bracket is also a
gear motor and spring assembly, which provides verylow-frequency motion to the vibration frame through a
horizontally configured rod and spring system as shown
in Figure 2. The high-frequency motion can be set by
the user with in a range of 80 to 110 Hz. When sectioning soft, spongy structures such as lung tissue the secondary much lower frequency vibration provides a sawlike oscillation to aid the blade’s procession through the
tissue. There are two separate systems for controlling
the amplitude and frequencies of the vibrating knife.
High-frequency control is achieved through the relationships between applied air pressure and the oscillation
frequency of the linear vibrator. Low-frequency vibration
is controlled by means of the voltage applied to the gear
motor. The knife itself is a 26-cm surgical trimming
LIMA FOR WHOLE-ORGAN IMAGE DATABASES
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Fig. 1. The large image microscope array system shown with; a charge-coupled device (CCD) digital
camera mounted to a stereomicroscope, which are both positioned over the specimen stage by means of
a gantry to image the tissue en bloc.
blade held by means of a custom vice mechanism in the
vibration frame. Using this system, sections are removed
and can be saved for further histology.
Photo Lock
A ‘‘photo locking’’ mechanism was designed to ensure
that the microtome stage returns to the same location
between sections. The motorized stage returns to the
‘‘photo lock’’ position by means of the computer controlled stepper motor. Here a mechanism consisting of a
tapered rod pushed by means of a solenoid into a matching tapered hole achieves the final locking of the stage.
This locking mechanism can be seen on the left-hand
side of the microtome in Figure 3a, with the locking
sequence shown in greater detail in Figure 3b. The solenoid is normally locked and energy is applied to the solenoid to unlock during sectioning. This approach ensures
that the solenoid is off during imaging and does not add
unnecessary vibration to the system.
Imaging System
The imaging system is composed of a Leica MZ16FA
(Leica Microsystems GmbH, Germany) stereomicroscope
configured with a 13 plan achromatic objective and a
JenOptik C12 (Jenoptik GmbH, Germany) cooled scientific camera. This specific microscope carrier and camera
couple was selected for its flexibility in performing
brightfield and darkfield fluorescent microscopy of
stained and nonstained specimens. To remove the
‘‘stereo’’ angle and create a perpendicular line of sight
for the acquired images, a Leica axial shift adapter was
mounted between the objective and the stereoscope. The
microscope has a magnification range of 7.113 to 1153
with a maximum resolution of 420 line pairs in this configuration. Digital images are acquired through the
JenOptik C12 cooled charge-coupled device (CCD) digital
camera. The peltier cooling reduces the dark currents,
which allows for darkfield fluorescent imaging. The CCD
contains a 1,300 3 1,030 pixel array with a pixel pitch
of 6.7 mm. The camera has a multishot feature that can
be used to increase the effective pixel count from 1.3
megapixels to 12 megapixels. Image data acquired by
the camera is transferred by means of a Firewire IEEE
1394 interface to the computer.
During image acquisition, the Vibratome stage is
locked into the ‘‘photo lock’’ location and the blade vibration is stopped. However, image acquisition particularly
in multishot mode is highly sensitive to vibration. Thus,
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Fig. 2. a: A schematic of the large-scale dual-frequency vibrating knife system from a top and side
view. b: A pictorial representation of the low-frequency motion through rotation of the motor assembly
and high-frequency motion as depicted by the waves protruding from the linear air vibrator.
the LIMA system is positioned on a Micro-g (TMC, Massachusetts) vibration isolation optical table to minimize
environmental vibrations. The microscope and CCD
camera coupling is maneuvered above the sectioned tissue surface by means of a Velmex Bislide (Velmex, New
York) parallel-coupled gantry system. The gantry consists of 94-cm parallel bislides in the y-axis, a 112-cm
single bislide in the x-axis, and a 25-cm single bislide in
the z-axis. Control of the gantry motion is automated by
means of three computer-interfaced stepper motors. A
standard PC is used for both computer automation and
image acquisition.
LIGHTING TECHNIQUES
In addition to the design and development of the
LIMA hardware components, a brightfield segmentation
tomography (BFST) system has also been created to
work in conjunction with the LIMA system (Thiesse
et al., 2004). Images acquired en bloc contain a high
depth of field. For some purposes, a large depth of field
is undesirable; hence, the BFST system uses oblique illumination patterns to acquire only the top most surface.
Images with a low depth of field are of particular interest in the measurement of lung alveolar tissue, including the calculation of the mean linear intercept, wall
thickness, and alveolar area. The BFST consists of a
thick metal ring with eight uniformly spaced white light
emitting diodes (LEDs) embedded in the base of the
ring. The attachment fits on the bottom of the microscope objective similar to a traditional ring light illumination. The height of the BFST ring is just slightly
shorter than the focal length of the microscope such that
the LED illumination is almost parallel to the tissue
surface. Each LED is activated individually, and a separate image is captured for each, resulting in eight
images per field of view. Each of these images illuminates one area of the top tissue surface. When the
images from all eight angles are summed together, an
image can be created that contains information from
only the top most surface of the tissue.
Automation Software
A high level of precision is needed in sectioning tissue
samples and positioning the microscope and camera
LIMA FOR WHOLE-ORGAN IMAGE DATABASES
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Fig. 3. a: The microtome and locking mechanism (on the left) that ensures correct positioning of the
stage for image acquisition. b: A close-up of the photo locking mechanism before an imaging sequence.
couple. Add to this the highly repetitive and time-consuming nature of multiple, sequential image acquisition
and the necessity of system automation is apparent.
Microsoft’s .NET framework, specifically Managed C11,
was chosen as the programming language to design the
controller for the automation of the LIMA hardware and
to provide an efficient graphical user interface, as seen
in Figure 4.
The automation program for the LIMA system consists of controls for the three major tasks involved: initialization, sectioning, and imaging. The initialization
stage sets up the serial port connections between the
computer and the stepper motors of the gantry and
Vibratome as well as provides output paths for image
storage and name formatting. The microtome control
provides the user with the ability to position the tissue
stage in the most convenient position for specimen
attachment. Activation of the low- and high-frequency
oscillations is also automated. A grossing option is
included to section the tissue down to a level where
image acquisition is to be initiated. The user is able to
select the top left and bottom right edges of the specimen using the gantry controls on the program and visual feedback is provided from the image seen through
the CCD camera onto the computer screen. Each corner
is selected, and the program then computes the correct
raster scan image acquisition sequence using the current
field of view and magnification settings, which are also
set by means of the computer program. Once the microscope is focused on the specimen for the first slice, the
software automatically detects the best focus position,
for all subsequent slices, by maximizing the highfrequency components of the acquired images. As scanning commences, the images are overlapped onto the
screen as they are acquired, forming the entire image.
Lung Tissue Preparation
Lung tissue that is sectioned and imaged on the LIMA
system is fixed using a modified Heitzman method
(Heitzman, 1973). This fixation technique conserves radio-density while maintaining necessary histopathology
for accurate pathologic and radiologic comparisons. In brief,
the lungs are fixed and inflated with a fixation solution
consisting of 25% polyethylene glycol 400 (FisherChemicals, New Jersey), 10% ethyl alcohol (190 Proof, 95%;
Pharmco Products, Connecticut), 10% formaldehyde solution (Fisher Chemicals, New Jersey), and 55% laboratory distilled water. The fixation fluid is applied intratracheally at 15-cm water pressure through a gravity
feed system for 12 hr for mice and 48 hr for whole sheep
lung, while immersed in the fixation mixture. Intratracheal administration allows the fixation fluid to reach
within a short space of all the tissue, because the airways essentially fill the lung in a fractal manner. Also
placing the lung in the fixative ensures that areas inaccessible by means of the airways are fixed through diffusion. The lung is then disconnected from the fixation apparatus and air-dried at 15-cm water pressure for several days in a heated drying oven set to 608C. Using this
method, there is minimal reduction in lung volume during fixation. The lung density as measured by the CT
scan before and after fixation, is preserved.
Mouse Lung Agarose Embedding
Mice lungs are externally embedded using agarose to
ensure the lung tissue is well supported during sectioning. The agarose is made from standard biological grade
agarose, mixed with distilled water, and heated in a
microwave on high for 20 sec. The agarose is allowed to
Fig. 4. The graphical user interface used to control the functions of
the large image microscope array (LIMA) system. a: The tissue setup
phase, where the user interactively selects the area of the microtome
stage that includes the tissue specimen. b: The image preparation
step, where the user interactively determines the center of the tissue
specimen, along with the magnification, field of view, and boundaries
for image acquisition. c: The left panel shows the current high-magnification image that is being captured, and the right panel shows the
completed montage of the subimages.
LIMA FOR WHOLE-ORGAN IMAGE DATABASES
cool down to 10 degrees above the gelling temperature
and then poured into a custom container that houses
the mouse lung in the correct orientation with respect to
the ex vivo micro-CT scan (Thiesse et al., 2005). The container is then placed in a refrigerator where the agarose
is allowed to gel and cool for a further 6 hr.
Solid Tissue Preparation
Solid tissue specimens can also be used on the LIMA
system, and, depending on the organ of interest, fixation
may not be necessary. One example is brain tissue,
which with the correct temperature control and nutrient
availability could be sectioned and imaged live as performed on standard Vibratome systems.
In other cases, the tissue could be fixed using vascular
perfusion techniques to ensure the fixative (not confined
to the Heitzman technique, and could be formalin) permeates through the entire organ of interest. Such organs
could then be externally embedding into a container
using the agarose mixture outlined for mouse lungs.
Standard Histology and Imunohistochemical
Staining
Tissue sectioned and imaged on the LIMA system can
then be stored in PBS solution and processed for standard hematoxylin and eosin (H&E) histology as well as
immunohistochemistry. For immunohistochemistry, the
shorter the interval between fixation, sectioning, and
slide preparation the better the results.
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tained. The microscope and the camera are moved
through an automated raster scan to capture the entire
surface area of the tissue by means of multiple high-magnification images. A registration algorithm was created to
register the overlapping portions of the subimages to create a reliably aligned composite image of the complete tissue surface (de Ryk et al., 2004). This methods differs
from traditional pathology where imaging is performed on
distinct tissue sections mounted on microscope slides.
Due to the large number of images required to complete
a whole-organ image data set, storage is important. The
average dimension of a sheep or human lung can be estimated at 20 cm by 10 cm in the transverse plane. To section at 250 mm for a depth of 1 cm and acquire images at
the highest system magnification of 1153, roughly
700,000 images would be required. This would require
approximately 1.2 terabytes of storage for the entire data
set if an uncompressed portable network graphics (png)
file format were used. A balance between the resolution,
magnification, slice thickness, and image compression is
therefore required for each project to increase the useful
information, without storing redundant information.
In our standard whole mouse lung imaging, we acquire 50 images (403 magnification) per slice for 70 slices (250-micron-thick slices), generating 3,500 images.
These are saved in a loss-less TIFF format requiring 1
megabyte per image. The resultant data set of 3–4 gigabytes is now a manageable size for storage, processing,
and analysis.
Image Acquisition
RESULTS
Fixed Sheep Lung Specimens
By imaging the surface of the tissue en bloc, the spatial
relationships between each sequential slice can be main-
Fixed inflated lung specimens from sheep using the
modified Heitzman technique as outlined in the methods
Fig. 5. A computed tomography (CT) slice from the upper lobe of a fixed sheep lung (a) and the corresponding stitched large image microscope array (LIMA) image (b).
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Fig. 6. An example application of the large image microscope
array (LIMA) system for the registration of a multimodality fixed sheep
lung data set. a–c: The color LIMA image (a), the micro–computed tomography (CT) radiological image (b), and finally the hematoxylin and
eosin (H&E) histopathology image (c), from the same location. The
images have been registered using a thin-plate spline algorithm, and
the final multimodality data set provides registered radio-density, color,
and cellular information. d,e: A small area to the left of images (a,b)
has been magnified to reveal the strong correlation between the registered H&E histology and micro-CT image with respect to the LIMA
data set.
Fig. 7. An example of a registered mouse lung data set from the in
vivo state down to the histology level. a: A 28-micron slice from the
original in vivo micro-computed tomography (CT) scan. b: A 28-micron
slice from the fixed ex vivo micro-CT scan. c: A final large image
microscope array (LIMA) image, stitched from 49 subimages at 403
magnification. d: The corresponding hematoxylin and eosin (H&E) histopathology image for the same slice. In this example, we can see the
advantage of the LIMA system in providing spatial correlation between
a nondestructive three-dimensional modality such as the micro-CT
with respect to the destructive histopathology imaging.
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LIMA FOR WHOLE-ORGAN IMAGE DATABASES
section have been examined. The upper left lobe of a
sheep lung was removed and scanned on a clinical CT
scanner. The lobe was then mounted onto the tissue
stage of the LIMA microtome using a custom container
and agarose external embedding, similar to the system
used for mouse lungs. The lobe was sectioned at 250micron slice thickness and imaged using the LIMA system. With a magnification of 203, 84 images were
required to capture the entire lobe section per slice.
Images for each LIMA slice were stitched into a single
composite image as shown in Figure 5b, and slices from
the LIMA data set were then aligned with their corresponding CT slice as shown in Figure 5a.
Small sections (20 3 20 mm) of fixed sheep lung have
also been imaged using a combination of micro-CT,
LIMA, and histopathology to complement the information provided by each technique: radio-density information in micro-CT, color/fluorescent marker information in
LIMA, and cellular content in the histopathology. Figure
6 illustrates a slice of tissue as represented after registration, in the three different modalities (de Ryk et al.,
2006).
Fixed Mouse Lung Specimens
In addition to large animal specimens, the LIMA system is used to visualize small animal organs. The entire
normal mouse lung has been sectioned and imaged using
the LIMA system. An in vivo micro-CT data set is initially acquired using our recently developed Intermittent
Iso-pressure Breath Hold technique (Namati et al., 2006).
The lung is excised and fixed as detailed in the methods
and scanned on the micro-CT scanner using a custommade container, which maintains orientation between
the micro-CT imaging and the following LIMA cross-sectioning and imaging. The excised lung in its fixed orientation container is agarose embedded and serially sectioned
and imaged on the LIMA system. Each tissue section
removed from the LIMA system is then stored in PBS solution and processed for histopathology. The in vivo
micro-CT and fixed lung micro-CT images are shown in
Figure 7a and b, respectively. Figure 7c represents the
stitched LIMA image of the airways and parenchyma
from the same slice at a magnification of 403. Finally a
high-resolution image acquired from the same LIMA slice
after H&E preparation is shown in Figure 7d. Using the
registered multimodality data set, a 3D rendering can be
created as shown in Figure 8. In this example, the 3D
reconstruction is based on the LIMA images, where the
color information from each slice has been used to
pseudo-label (red and blue) the 3D data set. Also shown
in Figure 8 is a corresponding LIMA and micro-CT slice
in their registered locations.
Morphologic Analysis Using the BFST
Lighting System
An alveolar morphology analysis example using the
LIMA and BFST system is shown in Figure 10a–c,
where Figure 10a represents the normal brightfield
image of a sectioned human lung, Figure 10b represents
the oblique illumination image, and finally Figure 10c
represents the postprocessed image of Figure 10b with
the overlaid wall/air intercepts, which are used for morphometric analysis.
DISCUSSION
Modification of the Leica SM2500 microtome into a
large-scale vibrating microtome with both low- and highfrequency oscillation was successful in achieving whole
fixed nonembedded organ sections. A custom x-y-z gantry system was successfully developed to move the
microscope and CCD camera over the entire sectioned
tissue surface for acquisition of high-magnification
images of the organ surface, while maintaining spatial
integrity with respect to the organ volume. Acquiring
images en bloc rather than the resultant thin-sliced sections (as performed in standard histology), minimized
distortion of the tissue structure and maintained alignment of subsequent sections.
Large and small animal lung specimens were selected
for testing the large image microscope array (LIMA).
Some organs such as the normal brain are reasonably
solid and have a consistent texture throughout the
organ, whereas lung tissue is nonsolid and contains
varying tissue characteristics with important fine structural features. The conductive airway walls are relatively thick and tough, whereas the walls of the terminal
alveolar sacs are extremely thin and fragile. The dual
vibration frequency of the vibrating microtome permitted the sectioning of the lung tissue with very minimal
visible disturbance to the underlying alveolar structures.
The LIMA system is being used for several investigations in pulmonary imaging, bridging the gap between
nondestructive imaging techniques such as CT and MRI
and destructive techniques such as histology. Validation
of automated segmentation algorithms for airways and
vessels based on CT data sets can be performed using
registered CT and LIMA data sets such as those shown
in Figure 5a and b. Here, correlation of the anatomic
truth with the representative CT image can be accurately performed with greater confidence. In Figure 6a–
c, the LIMA slice has been accurately registered to the
representative micro-CT and histology slice. In Figure
6d and e, a high-magnification image of the histology
overlaid onto the LIMA image, and the micro-CT image
overlaid onto the LIMA image is shown, respectively. As
seen in this example accurate registration between the
three-modalities can be performed. This finding allows
for accurate correlation between the micro-CT and histology data, where automated pattern classification systems for evaluating the tissues structural detail directly
from the micro-CT data can be developed with much
greater precision. This application can be extended to in
vivo registration, where data from an in vivo clinical CT
can be accurately registered to histology and immunohistopathology. This registration allows ‘‘ground truth’’
validation of the tissue of interest as seen in the CT
images, based on accurate knowledge of the cellular
state acquired through the registered histology images.
One application is characterization of lung cancer nodules as seen in vivo from a clinical CT image, then resected and imaged on a micro-CT followed by the LIMA
system to provide information about the nodules cellular
content. Early results indicated that ‘‘malignant’’ lung
nodules contain a heterogeneous structure, with varying
proportions of cancer tumor cells, necrotic tumor cells
fibroblastic tissue, and inflammation. Understanding
this structure in 3D is very important for tumor biology,
and for assessing tumor growth and response to therapy.
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This investigation also provides important information
about the ability to identify malignant nodules using
standard biopsy techniques, and upcoming micro-optical
biopsy procedures (de Ryk et al., 2007a,b).
A second application also under investigation is characterization of lung nodules in mice. In vivo micro-CT
images are being acquired from normal mice induced
with cancer through administration of urethane, a carcinogenic agent. Micro-CT images are acquired over a
6-month period, and finally the mouse is killed and the
lungs are fixed and imaged on the LIMA, followed by
histology. Initial results indicate varying nodule growth
rates as analyzed from the longitudinal micro-CT
images, with there cellular content as seen through
the registered LIMA and histology data showing noticeable differences between nodules in the same animal.
Only through a system such as the LIMA could we
accurately analyze individual nodule histology in 3D
and correlate back to the 3D micro-CT radio-density
information.
The LIMA system has also been used to acquire the
first 3D pathology representations of the entire mouse at
Fig. 8. The large image microscope array (LIMA) data set for the
example shown in Figure 7, has been reconstructed in three-dimensions (3D), with a representative LIMA and micro- computed tomography (CT) slice also shown. Color representation of the 3D image
derived from the LIMA data set is not representative of actual color
histology information, but was pseudo-labeled based on the intensity
information contained in the data set.
Fig. 9. A three-dimensional (3D) representation of the entire mouse
lung boundary obtained from the large image microscope array (LIMA)
system is shown; clearly the non-isotropic nature of this imaging system is evident by the jarred edges between slices.
Fig. 10. A computer-automated alveolar morphology example
based on the brightfield segmentation tomography (BFST) and the
large image microscope array (LIMA) system. a–c: The original brightfield image (a), the BFST image (b), and finally the postprocessed
image (c), based on b and the overlaid wall/air intercept points that
are used for calculating the mean chord length, in this case equal to
89mm. A blue cross represents the beginning of a wall, and a red
cross represents the end of a wall.
LIMA FOR WHOLE-ORGAN IMAGE DATABASES
250-mm slice increments (Fig. 9; Thiesse et al., 2005,
2007). The 3D reconstruction of the LIMA stacks in Figure 9 have jarred edges between slices, which is due to
the nonisotropic nature of the voxels, that is, higher resolution in the x- and y-axes as compared with the zaxis. This effect can be reduced through thinner sections
as well as the incorporation of interpolation algorithms.
Using the LIMA, we can now accurately translate the
true in vivo reference lung volume as determined using
the micro-CT down to the histology by means of the
LIMA data set. This method is performed by measuring
the change in lung volume from the in vivo micro-CT
lung volume, to the ex vivo fixed micro-CT lung volume,
to the LIMA lung volume, and finally to the histology
lung volume. This strategy allows greater repeatability
and accuracy in morphometric calculations made at the
histology level, a problem that is always of concern in
lung stereology (Ochs, 2006; Weibel et al., 2007).
Clear visualization and analysis of lung tissue structure is required for many clinical and investigative applications. Of particular importance is the accurate measurement of alveolar size and shape. The dimension and
composition of alveoli are sensitive to disease and are
thus often used as measures of disease severity. Calculations measuring alveolar size are currently performed on
small histologically processed, randomly sampled sections of lung, and the estimates for alveolar dimensions
calculated are extrapolated to be representative of the
whole lung (Robbesom et al., 2003). The LIMA system
provides the unique ability to accurately section a tissue
perpendicular to the imaging system, which allows imaging with novel lighting conditions. The BFST system uses
this unique characteristic through oblique illumination
patterns along with image processing algorithms to
extract and isolate surface characteristics. In Figure
10a–c, we present an automated alveolar morphology
analysis example based on the LIMA and BFST systems.
Using a custom computer algorithm, the beginning and
end of wall intercepts have been identified as shown in
Figure 10c. Here, the computer algorithm marks the beginning of a wall with a blue cross and the end of a wall
with a red cross. Based on these intercepts, a common
lung metric, the mean chord length, was calculated as 89
mm (Weibel, 1963, 1979). The LIMA system presents a
valuable opportunity to perform comparative calculations
on an entire organ before the introduction of standard
histology artifacts.
CONCLUSION
We have developed a flexible, large-image microscope
array system to section nonembedded whole organs and
gain high-magnification digital images of each cut surface. Custom control software has been successfully
developed to automate the entire process of sectioning
and image acquisition, creating an efficient system for
whole-organ 3D pathology acquisition. Finally, we have
described and illustrated several applications that use
the newly developed system.
1387
ACKNOWLEDGMENTS
The authors would like to thank Michael J. Wardenburg and Dan O’Connor for their invaluable help in
bringing this system to reality. This project was supported in part by NIH grant 3U01CA091085-0551.
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