ANALYTICAL AND QUANTITATIVE CYTOPATHOLOGY AND HISTOPATHOLOGY

1. 159
OBJECTIVE: Peripheral nerve injury in the extremities
usually has poor prognosis and a high disability rate
even after repair because the anatomy of the peripheral
nerves remains unknown. Thus, three-dimensional (3D)
reconstruction of the inside of the peripheral nerve
bundles is considered as a potential solution. The aim
of this study was to obtain the 3D reconstruction of
the tibial nerve from calcium chloride–enhanced micro–
computed tomography (micro-CT) images.
STUDY DESIGN: In this study the tibial nerve sam-
ples were dyed with a saturated solution of calcium
chloride and scanned by micro-CT to acquire two-
dimensional (2D) images. The seed images were anno­
tated artificially, and the 2D edge contours of the nerve
bundles were extracted automatically from a series of
images. On this basis, the 3D model of the tibial nerve
was constructed.
RESULTS: The 3D model of the tibial nerve was suc-
cessfully constructed. Calcium chloride enhanced the
visualization in all micro-CT, and visualization was
substantially improved by the image processing algo­
rithm. The 3D renderings provided detailed visualiza-
tion of the tibial nerve. Furthermore, compared to the
physical slice preparation method, similarity, repeatabi­
lity, and precision were improved considerably using the
micro-CT scanning method.
CONCLUSION: 3D texture features model recon­
struction based on calcium chloride–enhanced micro-
CT is a major breakthrough in the field of 3D recon­
struction of the bundles inside the peripheral nerves
Analytical and Quantitative Cytopathology and Histopathology®
0884-6812/19/4105-0159/$18.00/0 © Science Printers and Publishers, Inc.
Analytical and Quantitative Cytopathology and Histopathology®
3D Reconstruction of Peripheral Nerves Based
on Calcium Chloride Enhanced Micro-CT
Images
Yingchun Zhong, Ph.D., Peng Luo, Ph.D., Junli Gao, Ph.D., Jian Qi, Ph.D.,
Fang Li, M.S., Liwei Yan, Ph.D., and Liping Wang, Ph.D.
From the School of Automation, Guangdong University of Technology, Guangzhou, Guangdong; the Department of Bone and Joint
Surgery, Shenzhen Sixth People’s Hospital, Shenzhen, Guangdong; the Department of Orthopedics Trauma and Microsurgery, the First
Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong; the School of Information, Guangdong University of Finance and
Economics, Guangzhou, Guangdong; and the Department of Hand Surgery, Ningbo No. 6 Hospital, Ningbo, Zhejiang, China.
Yingchun Zhong and Peng Luo are co–first authors.
Yingchun Zhong is Associate Professor, School of Automation, Guangdong University of Technology.
Peng Luo is Attending Surgeon, Department of Bone and Joint Surgery, Shenzhen Sixth People’s Hospital.
Junli Gao is Associate Professor, School of Automation, Guangdong University of Technology.
Jian Qi is Associate Professor, Department of Orthopedics Trauma and Microsurgery, the First Affiliated Hospital of Sun Yat-sen
University.
Junli Gao is Associate Professor, School of Automation, Guangdong University of Technology.
Fang Li is Associate Professor, School of Information, Guangdong University of Finance and Economics.
Liwei Yan is Attending Surgeon, Department of Orthopedics Trauma and Microsurgery, the First Affiliated Hospital of Sun Yat-sen
University.
Liping Wang is Associate Professor, Department of Hand Surgery, Ningbo No. 6 Hospital.
Address correspondence to: Junli Gao, Ph.D., University Mage Center of Guangzhou, School of Automation, Guangdong University
of Technology, 100 Waihuan West Road, Guangzhou, Guangdong 510006, China (jl_gao@aliyun.com) or to Jian Qi, Ph.D., Department
of Orthopedics Trauma and Microsurgery, First Affiliated Hospital of Sun Yat-sen University, 58 Zhongshang Two Road, Guangzhou,
Guangdong 510080, China (9696096@qq.com).
Financial Disclosure: The authors have no connection to any companies or products mentioned in this article.

2. and lays the basis for further research into peripheral
nerve bundles. (Anal Quant Cytopathol Histpathol
2019;41:159–168)
Keywords: anatomy; diagnostic imaging; image
processing, computer-assisted; magnetic resonance;
micro-computed tomography; nerve bundles; nerve
regeneration; peripheral nerve injury; peripheral
nervous system; three-dimensional reconstruction;
tissue engineering/methods; tissue scaffolds; X-Ray
microtomography/methods.
Peripheral nerves in the 4 limbs are important
nerves connecting limbs to the trunk and trans­
mitting sensory impulses and motor commands.
Repair surgery for peripheral nerve injury has
been practiced for many years, but the anatomy
of the peripheral nerves such as the orientation,
bifurcation, and merging of nerve bundles as they
extend remains unknown.1,2 A 3D model of the
anatomy of peripheral nerves in the limbs is the
foundation of research in peripheral nerves. The
application of 3D reconstruction includes making
connection to the same type of nerve bundles,
restoring the motor and sensory functions of the
4 limbs, assisting the repair surgery or teaching,
surgical navigation, and 3D printing of the nerve
bundles.3,4
Magnetic resonance imaging (MRI) and diffu-
sion tensor imaging (DTI) are useful approaches
towards 3D reconstruction of the nerve bundle.
For instance, Boriani et al applied MRI scan to
the 3D modeling of median nerve injury in the
upper limbs.5 Sheikh adopted DTI in the artificial
3D reconstruction of the nerves enervating the
ankle joint.6 Kim et al took pictures with a scan­
ning electron microscope and reconstructed the
3D morphology of mossy fiber rosettes in mice.7
However, the insufficient resolution of MRI or
DTI may have an adverse impact on the clarity
of the nerve bundles and their relative positions
shown on the images. High-resolution micro-CT
scan can capture clear images of the nerves and
the relevant soft tissues. Watling et al achieved a
clear visualization and 3D reconstruction of the
nerve bundles inside the sciatic nerve by using
micro-CT scan.8 Hopkins et al performed micro-
CT scan of rat sciatic nerve and observed the
regeneration of the nerve bundles.9 However,
many problems still remain unresolved in this
field, such as how to acquire clear images of the
bundles inside the tender nerves and how to iso-
late the targets from the series of micro-CT images.
In order to perform 3D reconstruction of the
bundles inside the peripheral nerve, we explored
a rapid and accurate method of automatic periph­
eral nerve bundle contour extraction to increase
the efficiency of 3D reconstruction of peripheral
nerve bundles and to realize the 3D reconstruc-
tion of long segment of nerve bundle.
Materials and Methods
Human Tibial Nerve Sample
Tibial nerve was collected from an adult male at
5–10 cm from the popliteal fossa (Figure 1). The
adipose and connective tissues were removed
under the microscope, and the tibial nerve was
cut into 24 segments with a length of 2–3 mm.
One specimen was randomly chosen and soaked
in a saturated solution of calcium chloride for
2–4 hours and freeze dried for 24 hours. Then
the specimen was scanned using MicroCT 50
160 Analytical and Quantitative Cytopathology and Histopathology®
Zhong et al
Figure 1
Preparation of the specimens
of tibial nerve from the lower
limb.

3. (SCANCO_V1.2a, SCANCO Medical AG, Brütis­
tellen, Switzerland) with a scan interval of 10 μm.
The image size was 2,056×2,056 pixels, and the
image was stored in Digital Imaging and Com­
munications in Medicine (DICOM) format. All
methods were carried out in accordance with rel-
evant guidelines and regulations, and all experi­
mental protocols were approved by the Ethics
committee of the First Affiliated Hospital of Sun
Yat-sen University. Informed consent was obtained
from human participants.
Artificially Annotating the First Images
First, the image stored in DICOM format was
converted into jpg format. The nerve bundle
contours were annotated using software Paint
(Windows 7, Microsoft, USA). The nerve bundle
contours in the first image were delineated using
red lines with a width of 1 pixel, and the nonnerve
bundle regions were randomly delineated using
blue lines, the width being any pixels.
Extracting the Features Bundle Contour of Images
The quality of contours extracted from the first
image has a decisive impact on subsequent con­
tour extractions. Here, statistical texture features
were used for extracting the annotated region
from the seed images. Annotated region features
in the first image were described with statistical
texture features, and 6 statistical features were
extracted from this region. Statistical features
were computed in the neighborhood of each pixel
in the annotated region, and the feature matrix
was generated for the seed image. For the first
image, the nerve bundle contours and the non-
nerve bundle contours were described using the
statistical features of textures, and the feature
matrix was obtained. Six pixels were randomly
selected from the image (Figure 2A). Pixels num-
bered as 1 and 2 were located along the nerve
bundle contours, pixels numbered 3 and 4 were
within the nerve bundle, and pixels numbered 5
and 6 were outside the nerve bundle. Then the
feature matrix of texture features was generated
(Figure 2B). Comparison was performed between
pixels numbered 1 and 2 and the remaining 4 pix­
els. The mean, variance, smoothness, and entropy
values of the first 2 pixels were larger as com-
pared with the remaining 4 pixels. The 6 statistical
texture features are
L
Mean: Fea1 = ∑zi · p(zi) (1),
i=0
where z is the grayscale of each pixel in the region;
p(z) is the probability of this grayscale appearing in
this region.
Volume 41, Number 5/October 2019 161
3D Reconstruction of Peripheral Nerves
Figure 2
Texture features of pixels in
different regions. (A) Pixel of
interest (POI) was selected.
POIs 1 and 2 are located along
nerve bundle contours, POIs
3 and 4 are located within
the nerve bundle, and POIs
5 and 6 are outside the nerve
bundle. (B) Texture features
of 25 pixels around the POIs
were analyzed as shown
in (C).

4. Variance: Fea2 = [∑(z − Fea1)* p(z)]2 (2)
1
Smoothness: Fea3 = 1 − _________ (3)
1 − Fea2
2
2
Third-order moment: Fea4 = ∑(zi − Fea1)2 · p(zi) (4)
i=0
Consistency: Fea5 = ∑p2 (z) (5)
Entropy: Fea6 = −∑p(z) * log2 p(z) (6)
Statistical texture features were calculated in the
neighborhood of each pixel in the annotated re-
gion, and the feature matrix was generated. Then
we extracted the nerve bundle contour automati­
cally by the following steps:
1. The first image was the baseline and the sec­
ond image was the image to be processed. The
nerve bundle contours in the first image were
annotated artificially (Figure 3).
2. Nerve bundle contours were extracted from the
first image (Figure 4).
3. The nerve bundle contours were binarized (Fig­
ure 5).
162 Analytical and Quantitative Cytopathology and Histopathology®
Zhong et al
Figure 3
Artificial annotation of the
CT image of the tibial nerve
bundle. (A) Red lines indicate
nerve bundle contours and
blue lines indicate non-nerve
bundle contours. (B) Enlarged
area taken from the same
image.
Figure 4
Procedures for automatic nerve
bundle contour extraction.

5. 4. The contours were superimposed onto the sec­
ond image (Figure 4).
5. The pixels covered by the contours were iso-
lated to obtain Figure 4.
6. Feature matrix was generated based on statisti­
cal texture features describing statistical texture
features of each pixel along the contours.
7. Wheth­
er pixel belonged to real nerve bundle
contours was examined (Figure 2).
8. In-contour pixels were superimposed onto the
second original image (Figure 4).
9. The processed second image was used as base­
line, and the third image was processed as in
steps 1 to 8.
Three-Dimensional Reconstruction
After contour extraction, 3D reconstruction was
carried out as follows:
1. After importing 2D contour imaging, the soft­
ware automatically generated sagittal and coro­
nal images based on transverse images
2. Removing noise signals by setting thresholds.
3. Based on 3D interpolation, 2D images were
transformed to 3D model. Parameters such as
intensity, color, and so on were adjusted for
better illustration. Animations were made for
translation, zooming in/out, intersection at a
different horizontal position, and rotation for
better illustrating 3D morphologies of nerve
bundles.
Results
Results of Micro-CT Scan
A total of 227 clear images were acquired using
a MicroCT 80 scanner, and one of them is shown
in Figure 6. The nerve dyed with calcium chloride
revealed the presence of a large amount of inter­
ferences outside. The pixels’ grayscales within the
nerve bundle region were very close to those
of the interferences outside the nerve, and the
pixels’ grayscales of the epineurium were mixed
disorderly (Figure 6A). In contrast, there were ba-
sically no interferences in the cross section of the
nerve bundle without any dyeing, and the pixels’
grayscales within the nerve bundle were uniform
and regular. Outside the nerve bundle, the pixels’
grayscales showed high consistency (Figure 6B).
Automatic Contour Extraction
The automatic nerve bundle contour extraction
not only reduced the computational load in identi­
fication, but also provided the variation scope of
nerve bundle contours between the 2 images. Since
these 2 images were collected only at a distance
of 5 μm, the variations of the nerve bundle con-
tours between the 2 images were very slight. The
nerve bundle contours were made more accurate-
ly by further identification and treatment. The ex-
traction of the nerve bundle contours was always
accurate as the nerve bundles extend, even in the
case of nerve bundle merged (Figure 7).
3D Reconstruction
As shown in Figure 8, the nerve bundle could be
observed from different angles by adjusting pa-
rameters in software. Divergence and convergence
of nerve bundles were evident in the images. Since
the sample was 2 mm, divergence and conver­
Volume 41, Number 5/October 2019 163
3D Reconstruction of Peripheral Nerves
Figure 5 Procedures for 3D printing of the tibial nerve bundle
in the lower limbs.

6. gence of surrounding nerves could be observed.
Moreover, key mathematical morphological terms
for nerve bundle features such as area, roundness,
long/short axis ratio, and so on could be clearly
detected.
Comparison of Physical Slice Preparation and
Micro-CT Scanning Method
As shown in Table I, the procedures using physi­
cal section preparation included sectioning, dye-
ing, photographing, splicing, recognition of posi­
tioning line, registration, contour extraction, and
3D reconstruction. Micro-CT scan consisted of
only 4 steps: dyeing, scanning, nerve bundle ex-
traction, and 3D reconstruction. The similarity for
3D reconstruction and the actual morphology of
the nerve bundles was higher than physical sec­
tions. Furthermore, because of the intrinsic fea­
tures of micro-CT scan, the repeatability of 3D
reconstruction (80–90%) also far exceeded that of
physical sections (95–98%). Time consumption for
physical sections (280–300 h) was obviously high-
er than that of 3D reconstruction (18–20 h). Preci­
sion of physical sections reached 89.2%, while it
was 91.4% in 3D reconstruction.
Discussion
Peripheral nerve injury usually has poor progno­
sis and a high disability rate, which urgently re-
quires an efficient solution. However, very few
techniques are available to ensure the right con­
nections to the same type of nerve bundles. Micro-
CT is widely used for the study of mineralized
tissues, but it is limitedly used for soft tissues
due to its low X-ray attenuation. This limitation
can be overcome by the recent development of
different staining techniques.10 In the present
study, 3D reconstruction of peripheral nerves was
done based on calcium chloride–enhanced micro-
CT images.
Besides functional tests, histological imaging
using conventional bright-field microscopy of
stained sections is an important tool for evalua­
tion of the structure and regenerating nerve. For
instance, using a combination of osmium tetrox­
ide staining, micro-CT imaging, and an image
processing algorithm, an accurate and detailed
visualization of the 3-dimensional microanatomy
of the human inner ear was provided.11 Watling
et al dyed rat sciatic nerve with osmium com­
pounds, which was then immobilized to the po-
lymer scaffold for low X-ray irradiation on the
micro-CT scanner. They clearly observed the bun-
dles inside the sciatic nerve as well as the blood
supply to the nerve and regeneration of the nerve
bundles and achieved 3D reconstruction of the
sciatic nerve.8 Hopkins et al dyed the rat sciatic
nerve with iodine and performed micro-CT scan.
The regeneration of the nerve bundles was facili­
tated by removing the excess iodine with sodium
thiosulfate, guided with a titanium alloy tube.9
Calcium chloride is reported to enable the gener­
ation of 3D bioprinting and allows for reducing
the number of histologic specimens that have to
164 Analytical and Quantitative Cytopathology and Histopathology®
Zhong et al
Figure 6
Micro-CT scan of the tibial
nerve bundle in the lower
limbs without (A) and with (B)
calcium chloride treatment.
The contrast between
non-bundle and bundle
regions were increased after
treatment (B).

7. be produced and provides a precise selection of
the most suitable region at the same time.12 In this
study, calcium chloride was used to enhance 3D
reconstruction.
Different imaging techniques, specimen size,
and dyeing methods have great impact on 3D
reconstruction of the nerve bundles. Preparing
physical sections of the nerve bundles is one way
towards 3D reconstruction. Cagimni et al and
Katagiri et al prepared sections of the nerve bun­
dles and blood vessels for 3D reconstruction.13,14
In this study we prepared sections of the median
nerves from the cubital fossa and wrist, which
were then dyed and photographed; the images
were spliced and the cross section of the position­
ing line was identified from the images based
on quasicircular contours. Image calibration and
registration were performed using the bilinear
method. The nerve bundle contours were extracted
automatically using the gradient vector flow (GVF)
snake method. The functions of the nerve bundles
were determined by multi-oriented gradients and
second-order gradient, and the 3D reconstruction
of a short segment of peripheral nerve bundle
was achieved.15 However, 3D reconstruction based
on physical sections of the peripheral nerve has
low efficiency, registration precision, and splicing
precision. Preparing conventional sections is usu­
Volume 41, Number 5/October 2019 165
3D Reconstruction of Peripheral Nerves
Figure 7
Examples for extracting nerve
bundle contour from a series
of images obtained by a
series CT scan from the same
sample. Numbers indicate the
order of the image in the series
CT scan. Bundles a and b in
the 50th image were found to
converge into a large bundle c
in the 100th image.
Figure 8
3D reconstruction of the tibial
nerve bundle in the lower
limbs after contour extraction.

8. ally the foundation of 3D reconstruction of periph-
eral nerve bundles.15,16 In this study we acquired
micro-CT images of peripheral nerve bundles, from
which the nerve bundle contours were automati­
cally extracted and the 3D morphology of nerve
bundles was reconstructed.
Methods such as MR and DTI micro-CT have
been used extensively in research to generate
high-resolution 3D images of tissues nondestruc­
tively. Gu et al and Bradfield et al applied CT
scan to the imaging of hard tissues (e.g., teeth
and bones), and the scan result provided accurate
and reliable clues for the diagnosis of hard tissue
diseases.17,18 Boriani et al mapped the median
nerve of the upper limbs using MR diffusion
tensor imaging, tracking the recovery process of
the median nerve.5 Zhu et al and Lehmann et al
captured the images of the inside of the nerves
using MR and DTI, respectively.19,20 Cagimni et al
captured the cross-sectional images of the blood
vessels and nerves under the light microscope
from the physical sections. The nerve contours
were extracted after enhancement, registration, in-
terpolation, and segmentation of the images, and
the 3D morphology of the nerves and blood ves-
sels was reconstructed.13 The above research stud-
ies applied MR or DTI to acquire the images
from which the peripheral nerve bundles were
reconstructed in a 3D manner. However, the
quality of the images collected is not always
satisfactory due to the limitation of resolution,
and the following problems are common: blurred
margins of nerve bundles and small difference
in contrast between the nerve bundle region and
other tissues.21,22 Griffin et al performed segmen­
tation of micro-CT images of meteorite using local
histograms of pixel intensity. Large components
were clearly differentiated from the matrix, and
the segmentation precision was as high as over
86%.21 Taylor et al applied micro-CT scan to
teeth, and the regions of enamel and dentine
were segmented based on sharp increases and
decreases of grayscale.22 Sheppard et al proposed
the use of spiral cone strategy to micro-CT scan
of rocks. Then the watershed segmentation algo­
rithm was combined with Gaussian mixture mod­
el for the segmentation of the scan images, and
good segmentation effect was obtained.23 Carrera
et al applied micro-CT scan to tooth filling to
quantitatively evaluate the leakage of composite
resin material during the process of tooth filling.
Image segmentation was performed using the
thresholding approach.24 Gangsei and Kongsro
scanned the bones of living pigs using micro-CT
scan.25 Yan et al scanned the peripheral nerves
by micro-CT.26 We also observed the peripheral
nerves by calcium chloride–enhanced micro-CT
scan.
In addition, we described the nerve bundle
contours from the micro-CT images using texture
features. Description based on statistical texture
features is a supervised feature extraction tech­
nique.27,28 Unlike unsupervised feature extraction
techniques,29,30 the advantages of using super­
vised feature extraction of the nerve bundle re-
gion include the following: (1) It can be seen from
the comparison of nerve bundle region and non-
nerve bundle region in that the two regions are
intermingled sometimes. The nerve bundle region
cannot be fully differentiated based on grayscale
thresholding alone. (2) Region description,31 tex-
ture description, or invariant moment descrip-
tion32 are adapted to the description of nerve
bundles on the images. As the changes in pixels’
grayscales in the nerve bundle region are usually
regular, which is defined as texture, we described
the nerve bundle region with texture features.
(3) Regarding the description of texture features,
the classical methods include statistical features
and spectral features of textures and gray-level
co-occurrence matrix.33,34 The computational effi­
ciency is higher, and an overall description of
texture features is enabled with statistical features
of textures. Therefore, we chose statistical texture
features for the supervised feature extraction.
166 Analytical and Quantitative Cytopathology and Histopathology®
Zhong et al
Table I Comparison of the 2 Methods of 3-D Reconstruction
Micro-CT
Physical slice scanning
preparation method
Procedure Slice, dye, take photos, Dye, scan,
splice, recognize the nerve bundle
positioning line of the extraction, and
pictures, registration of 3D reconstruc-
sliced images, extracting tion
of nerve bundle images,
and 3D reconstruction
Time consumed 280–300 h 18–20 h
Similarity 80–95% 90–98%
Repeatability 80–90% 95–98%
Precision 89.2% 91.4%

9. Volume 41, Number 5/October 2019 167
3D Reconstruction of Peripheral Nerves
struction of peripheral nerves from optical projection to-
mography images: A method for studying fascicular inter-
connections and intraneural plexuses. Clin Anat 2018;31(3):
424-431
5. Boriani F, Fazio N, Fotia C, Savarino L, Nicoli Aldini N,
Martini L, Zini N, Bernardini M, Baldini N: A novel tech­
nique for decellularization of allogenic nerves and in vivo
study of their use for peripheral nerve reconstruction. J Bio­
med Mater Res A 2017;105(8):2228-2240
6. Sheikh KA: Non-invasive imaging of nerve regeneration.
Exp Neurol 2010;223:72-76
7. Kim HW, Kim N, Kim KW, Rhyu IJ: Three-dimensional
imaging of cerebellar mossy fiber rosettes by ion-abrasion
scanning electron microscopy. Microsc Microanal 2013;19(5):
172-177
8. Watling CP, Lago N, Benmerah S, FitzGerald JJ, Tarte E,
McMahon S, Lacour SP, Cameron RE: Novel use of X-ray
micro computed tomography to image rat sciatic nerve
and integration into scaffold. J Neurosci Methods 2010;188:
39-44
9. Hopkins TM, Heilman AM, Liggett JA, LaSance K, Little KJ,
Hom DB, Minteer DM, Marra KG, Pixley SK: Combining
micro-computed tomography with histology to analyze
biomedical implants for peripheral nerve repair. J Neurosci
Methods 2015;255:122-130
10. Heimel P, Swiadek NV, Slezak P, Kerbl M, Schneider C,
Nürnberger S, Redl H, Teuschl AH, Hercher D: Iodine-
enhanced micro-CT imaging of soft tissue on the example of
peripheral nerve regeneration. Contrast Media Mol Imaging
2019;2019:7483745
11. van den Boogert T, van Hoof M, Handschuh S, Glueckert
R, Guinand N, Guyot JP, Kingma H, Perez-Fornos A,
Seppen B, Johnson Chacko L, Schrott-Fischer A, van de Berg
R: Optimization of 3D-visualization of micro-anatomical
struc­
tures of the human inner ear in osmium tetroxide
contrast enhanced micro-CT scans. Front Neuroanat 2018;
12:41
12. Raddatz L, Lavrentieva A, Pepelanova I, Bahnemann J,
Geier D, Becker T, Scheper T, Beutel S: Development and
application of an additively manufactured calcium chloride
nebulizer for alginate 3D-bioprinting purposes. J Funct Bio­
mater 2018;9(4). pii: E63
13. Cagimni P, Govsa F, Ozer MA, Kazak Z: Computerized
analysis of the greater palatine foramen to gain the palatine
neurovascular bundle during palatal surgery. Surg Radiol
Anat 2017;39:177-184
14. Katagiri N, Katagiri Y, Wada M, Okano D, Shigematsu Y,
Yoshioka T: Three-dimensional reconstruction of the axon
extending from the dermal photoreceptor cell in the extra­
ocular photoreception system of a marine gastropod, on-
chidium. Zoolog Sci 2014;31(12):810-819
15. Zhong Y, Wang L, Dong J, Zhang Y, Luo P, Qi J, Liu X, Xian
CJ: Three-dimensional reconstruction of peripheral nerve
internal fascicular groups. Sci Rep 2015;5:17168
16. Wang CW, Budiman Gosno E, Li YS: Fully automatic and
robust 3D registration of serial-section microscopic images.
Sci Rep 2015;5:15051
17. Gu Y, Tang Y, Zhu Q, Feng X: Measurement of root surface
There are still several limitations in this study.
First, limited by micro-CT devices, it is not yet
possible to scan for long-segment nerves over 5
mm in length. The long-segment nerve was cut
into a small segment of around 3 mm, and the
scan interval was 10 μm. Because there were
errors and losses during cutting, the total num-
ber of scanned images obtained by the specimen
in the paper was only 227. Second, the method
in this paper was the basis for the 3-dimensional
reconstruction of long-segment peripheral nerves.
In the follow-up study the 3-dimensional recon­
struction of long-segment nerve will be studied,
including the study of 3-dimensional splicing of
nerve beam.
In summary, the nerve bundles were more clear­
ly visualized by micro-CT scan if the specimens
were first dyed with saturated solution of calci­
um chloride. The precision, time efficiency, and
repeatability were higher using micro-CT than
those using physical sections. It is indicated that
3D texture features model reconstruction based on
calcium chloride–enhanced micro-CT can improve
our understanding of neurobiological principles
and guide accurate repair of nerves in-clinic.
Acknowledgments
This study was supported by the Natural Science
Foundation Project of Guangdong Province, China
(No. 2018A0303130137) and the Opening Project
of Key Lab of High Performance Computation of
Guangdong Province, China (No. TH1528), the
Technology Planning Project of Guangdong Prov­
ince, China (No. 2015B010114004), the Science
and Technology Project of Guangzhou City, China
(No. 201704030041), and the Technology Planning
Project of Shenzhen city, China (No. 2014028).
References
1. Donzelli R, Capone C, Sgulò FG, Mariniello G, Donzelli O,
Maiuri F: Microsurgical repair by autografting in traumatic
injuries of peripheral nerves: A series of 50 cases. J Neurosurg
Sci 2019;Feb 4. doi: 10.23736/S0390-5616.19.04572-7. [Epub
ahead of print]
2. Möller I, Miguel M, Bong DA, Zaottini F, Martinoli C: The
peripheral nerves: Update on ultrasound and magnetic
resonance imaging. Clin Exp Rheumatol 2018;36(Suppl 114)
(5):145-158
3. Chen L, Lenz F, Alt CD, Sohn C, De Lancey JO, Brocker KA:
MRI visible Fe3O4 polypropylene mesh: 3D reconstruction
of spatial relation to bony pelvis and neurovascular struc­
tures. Int Urogynecol J 2017;28(8):1131-1138
4. Prats-Galino A, Čapek M, Reina MA, Cvetko E, Radochova
B, Tubbs RS, Damjanovska M, Stopar Pintarič T: 3D recon­

10. 168 Analytical and Quantitative Cytopathology and Histopathology®
Zhong et al
and Electronics in Agriculture 2016;121:191-194
26. Yan L, Guo Y, Qi J, Zhu Q, Gu L, Zheng C, Lin T, Lu Y, Zeng
Z, Yu S, Zhu S, Zhou X, Zhang X, Du Y, Yao Z, Lu Y, Liu X:
Iodine and freeze-drying enhanced high-resolution microCT
imaging for reconstructing 3D intraneural topography of
human peripheral nerve fascicles. J Neurosci Methods 2017;
287:58-67
27. Lake BM, Salakhutdinov R, Tenenbaum JB: Human-level
concept learning through probabilistic program induction.
Science 2015;350:1332-1338
28. Yadav AR, Anand RS, Dewal ML, Gupta S: Gaussian
image pyramid based texture features for classification of
microscopic images of hardwood species. Optik 2015;126:
5570-5578
29. Rastghalam R, Pourghassem H: Breast cancer detection using
MRF-based probable texture feature and decision-level
fusion-based classification using HMM on thermography
images. Pattern Recognition 2016;51:176-186
30. Li SZ, Yu B, Wu W, Su SZ, Ji RR: Feature learning based on
SAE–PCA network for human gesture recognition in RGBD
images. Neurocomputing 2015;151:565-573
31. Su SZ, Liu ZH, Xu SP, Li SZ, Ji R: Sparse auto-encoder based
feature learning for human body detection in depth image.
Signal Processing 2015;112:43-52
32. Zhang X, Li X, Feng Y: A medical image segmentation al-
gorithm based on bi-directional region growing. Optik 2015;
126:2398-2404
33. Karakasis EG, Amanatiadis A, Gasteratos A, Chatzichristofis
SA: Image moment invariants as local features for content
based image retrieval using the Bag-of-Visual-Words
model. Pattern Recognition Letters 2015;55:22-27
34. Zhang Y: MRI texture analysis in multiple sclerosis. Int J
Biomed Imaging 2012;2012:762804
area of permanent teeth with root variations in a Chinese
population: A micro-CT analysis. Arch Oral Biol 2016;63:
75-81
18. Bradfield J, Hoffman J, De Beer F: Verifying the potential of
micro-focus X-ray computed tomography in the study of
ancient bone tool function. J Archaeological Sci Rep 2016;
5:80-84
19. Zhu S, Zhu Q, Liu X, Yang W, Jian Y, Zhou X, He B, Gu L,
Yan L, Lin T, Xiang J, Qi J: Three-dimensional reconstruction
of the microstructure of human acellular nerve allograft. Sci
Rep 2016;6:30694
20. Lehmann HC, Zhang J, Mori S, Sheikh KA: Diffusion tensor
imaging to assess axonal regeneration in peripheral nerves.
Exp Neurol 2010;223:238-244
21. Griffin LD, Elangovan P, Mundell A, Hezel DC: Improved
segmentation of meteorite micro-CT images using local his­
tograms. Computers & Geosciences 2012;39:129-134
22. Taylor AM, Satterthwaite JD, Ellwood RP, Pretty IA: An
automated assessment algorithm for micro-CT images of
occlusal caries. Surgeon 2010;8:334-340
23. Sheppard A, Latham S, Middleton J, Kingston A, Myers G,
Varslot T, Fogden A, Sawkins T, Cruikshank R, Saadatfar
M, Francois N, Arns C, Senden T: Techniques in helical
scanning, dynamic imaging and image segmentation for
improved quantitative analysis with X-ray micro-CT. Nucl
Instrum Methods Phys Res B 2014;324:49-56
24. Carrera CA, Lan C, Escobar-Sanabria D, Li Y, Rudney J,
Aparicio C, Fok A: The use of micro-CT with image seg­
mentation to quantify leakage in dental restorations. Dent
Mater 2015;31:382-390
25. Gangsei LE, Kongsro J: Automatic segmentation of com­
puted tomography (CT) images of domestic pig skeleton
using a 3D expansion of Dijkstra’s algorithm. Computers