Cervical Spine Range of Motion Measurement Utilizing Image Analysis

Kana Matsuo

1 a

, Koji Fujita

2 b

, Takafumi Koyama

3 c

, Shingo Morishita

3 d

and Yuta Sugiura

1 e

Department of Information and Computer Science, Keio University, Kanagawa, Japan

Department of Functional Joint Anatomy, Tokyo Medical and Dental University, Tokyo, Japan

Department of Orthopedic and Spinal Surgery, Tokyo Medical and Dental University, Tokyo, Japan

Keywords:

Measurement System, Deep Learning, Cervical Spine, Range of Motion.

Abstract:

Diseases of the cervical spine often cause more serious impediments to daily activities than diseases of other

parts of the body, and thus require prompt and accurate diagnosis. One of the indicators used for diagnosing

cervical spine diseases is measurements of the range of motion (RoM) angle. However, the main measure-

ment method is manual, which creates a burden on physicians. In this work, we investigate the possibility of

measuring the RoM angle of the cervical spine from cervical X-ray images by using Mask R-CNN and image

processing. The results of measuring the RoM angle with the proposed cervical spine motion angle measure-

ment system showed that the mean error from the true value was 3.5 degrees and the standard deviation was

2.8 degrees. Moreover, the standard deviation of the specialist measurements used for comparison was 2.9

degrees, while that of the proposed system was just 0 degrees, indicating that there was no variation in the

measurements of the proposed system.

1 INTRODUCTION

In the medical ﬁeld, many diagnostic imaging tests

(e.g., those on X-ray images) are performed, but it is

inconvenient for physicians to examine a large num-

ber of images and it takes away from the time they

could be spending on medical treatment. In addi-

tion, at night, when only a small number of physicians

are on duty, they may be asked to perform diagnostic

imaging in areas in which they do not specialize. For

this reason, there has been extensive research in recent

years on automatic image diagnosis using artiﬁcial in-

telligence (AI).

The cervical spine is one of the areas on which

many imaging studies have been focused, especially

in orthopedics. When the degeneration of the cervical

spine progresses due to aging, it causes cervical pain

as well as neurological diseases such as myelopa-

thy and radiculopathy. When myelopathy worsens, it

causes dyskinesia of the ﬁngers and gait, resulting in

serious obstacle in daily life, so a prompt and accurate

diagnosis is required.

https://orcid.org/0000-0001-9753-2983

https://orcid.org/0000-0003-3733-0188

https://orcid.org/0000-0002-1676-7578

https://orcid.org/0000-0002-5993-2297

https://orcid.org/0000-0003-3735-4809

Figure 1: Example of annotation for measurement.

When the neck is moved, movement occurs in

each of the C1–C7 vertebra (Figure 1). In speciﬁc

regard to ﬂexion and extension, cervical instability

may be assessed by analyzing the mobility between

vertebrae from cervical X-ray images. For this eval-

uation, it is necessary to measure each range of mo-

tion (RoM) angle between C1 and C7. In the Bakke

method, which is one of the measurements performed

by the physician, X-ray images are taken when the

cervical region is ﬂexed and extended, and lines are

drawn at the upper and lower edges between each

vertebra. The next step is to ﬁnd the angle between

Matsuo, K., Fujita, K., Koyama, T., Morishita, S. and Sugiura, Y.

Cervical Spine Range of Motion Measurement Utilizing Image Analysis.

DOI: 10.5220/0010819400003124

In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP, pages

861-867

ISBN: 978-989-758-555-5; ISSN: 2184-4321

861

these lines and take the difference between the angle

for ﬂexion and the angle for extension. Due to the

special shape of C1 and C2, a line is drawn on the

posterior margin of the anterior arch for C1 and on

the posterior margin of the vertebral body for C2, and

the angle between these lines is determined (Figure

1). In this method, a total of 24 lines are drawn on the

cervical spine images of ﬂexion and extension, which

creates a signiﬁcant burden for the physician who has

to check them. Moreover, the C1 and C2 vertebra are

particularly difﬁcult to measure accurately because of

their special shape. In this study, we aim to support

physicians in measuring the RoM angles by imple-

menting a system that automates the measurement of

RoM angles from cervical spine X-ray images.

2 RELATED WORK

2.1 Medical Image Segmentation

Segmentation using image processing has been stud-

ied. Huaifei et al. proposed a method using image

processing to extract a rough cervical spine region by

contrast histogram and then estimate the region by us-

ing curve ﬁtting (Huaifei et al., 2011). In addition,

Lecron et al. proposed a method to detect the corners

of each cervical spine section by using edge detec-

tion and Hough transform for segmentation (Lecron

et al., 2010). Segmentation using such image process-

ing techniques requires a large number of parameters

to be set and threshold values to be determined, which

makes the implementation more complex. Moreover,

in order to avoid the inﬂuence of the head when fo-

cusing on the histogram, it is necessary to crop only

the cervical region from the original X-ray image.

Recently, segmentation methods based on deep

learning have been proposed. Architectures used

for such segmentation include Fully Convolutional

Networks (FCN) (Long et al., 2015), U-Net (Ron-

neberger et al., 2015), and Mask R-CNN (He et al.,

2017). Arif et al. proposed an FCN-based Convolu-

tional Neural Network (CNN) called SPNet for cervi-

cal spine segmentation (Arif et al., 2018) that, com-

pared with existing methods such as U-Net, can ro-

bustly detect the cervical region even in images con-

taminated with foreign matter.Masuzawa et al. pro-

posed a method for automatic segmentation, localiza-

tion, and identiﬁcation of vertebrae in arbirary 3D CT

images. They developed a network for instance seg-

mentation of cervical, thoracic, and lumbar vertebrae

from 3D CT images(Masuzawa et al., 2020). Uozumi

et al. used Mask R-CNN to extract lung regions from

chest X-ray images with high accuracy (Uozumi et al.,

2020) and found that, while U-Net is more accurate in

terms of extraction accuracy, Mask R-CNN is more

robust to the diversity caused by changes in the shad-

ing of X-ray images and changes in the position of the

lungs due to disease. Mask R-CNN has the advantage

of being able to process each cervical spine section

separately since, unlike other networks specialized for

segmentation, it performs instance segmentation. In

the present work, we extract the cervical spine region

from the cervical spine X-ray image by using Mask

R-CNN. The contribution of our research is that we

did a segmentation of the cervical spine and then mea-

sured the RoM of the cervical spine and evaluated the

accuracy of the measurement with the physician.

2.2 Computer Aided Diagnosis for

Cervical Spine

Various research is being conducted to analyze and

diagnose medical images using segmentation tech-

niques, image processing, and deep learning to as-

sist physicians in diagnosis. Choi et al. developed a

system to estimate the spinal column alignment from

moir

e images by training a set of moir

e images and

lumbar spine X-ray images using CNN and then mea-

suring the Cobb angle, which represents the degree of

the curve of the spine (Choi et al., 2017). The aver-

age error from the true value in their system was 3.8

degrees, which is equivalent to the accuracy of mea-

surement by a physician. Alomari et al. developed a

system to segment lumbar discs by using image inten-

sity and gradient vector ﬂux and to detect disc abnor-

malities by measuring the disc height (Alomari et al.,

2011). Young et al. used deep learning and image

processing to measure the thickness of the preverte-

bral soft tissue in front of the vertebral body from cer-

vical spine X-ray images, and proposed a method to

diagnose swelling by referring to the ﬂow of swelling

diagnosis by physicians (Young et al., 2018).

In the present study, we measure the RoM an-

gle between sections of the cervical spine by using a

system implemented with reference to the diagnostic

ﬂow of a physician. By measuring this RoM, the fea-

tures of the cervical vertebrae are detected and used to

help the physician make a diagnosis. The computer-

based measurement is expected to have the advantage

of eliminating the variation in measurement among

physicians.

VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications

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Figure 2: System ﬂow.

3 METHOD

3.1 Overview of System

The ﬂow of the system proposed in this study is

shown in Figure 2. First, the cervical spine region is

estimated from the cervical spine X-ray image using

Mask R-CNN. The estimated region is approximated

by a simple polygon with three or four vertices. The

edges of the polygons of cervical spine n and cervi-

cal spine n+1, which are close to the center of each

other, are the edges that lie between the cervical spine

vertebrae. The RoM angle is calculated by measuring

the angle between the cervical spine in the ﬂexion im-

age and the extension image, and then calculating the

difference.

3.2 Estimation of Cervical Spine Region

The training data are X-ray images of the cervical

spine when it is ﬂexed and extended. Based on the

guidance of a radiologist specializing in the cervical

spine, cervical spine mask information is created, and

the pair with the original image is used as training

data. The cervical spine regions to be masked are dif-

ferent for C3 to C7 and for C1 and C2 because of the

special shape of the latter. Speciﬁcally, the C1 region

is the anterior arch, and the C2 region includes the en-

tire vertebrae from the vertebral body to the odontoid

process. The regions are labeled as c1, c2, and bone

(Figure 3).

3.3 RoM Angle Measurement from

Estimated Cervical Region

The bounding box coordinates and area information

of the regions estimated by Mask R-CNN are stored

Figure 3: Annotation area.

in an array, but they are not arranged in the order

of C1 to C7. Therefore, it is necessary to shift the

cervical spine positions of the estimated regions in

both images so that they correspond. To prepare for

the measurement, the y-coordinates of the bounding

box should be in ascending order, and the estimated

regions should be rearranged in the order of C1 to

C7. However, it is sometimes not possible to esti-

mate some of the cervical regions in the image. In

such cases, we take the difference between the y-

coordinates of the upper left and lower right of the

rectangle of cervical spine n and cervical spine n+1,

and consider it continuous if it is less than half of the

height of the bounding box of cervical spine n. If this

condition is not met, it is judged that there is a mis-

estimated region or a cervical region that cannot be

estimated. The ﬂow of the automatic annotation is

shown in Figure 4. First, the contour coordinates of

the region are obtained from the estimated cervical re-

gion (Figure 4(a)). The coordinates of the convex hull

are selected from the contour coordinates and then the

convex hull region is approximated as a polygon with

three or four vertices (Figure 4(b), (c)). After this, the

midpoint of each edge of the approximated polygon is

calculated. The edge where the calculated midpoints

are close to each other (between cervical spine n and

cervical spine n+1) is regarded as the edge of the cer-

vical spine used for measurement (Figure 4(d)). How-

ever, since approximate polygons below C3 are ap-

proximated to be inscribed in the convex hull region,

their edges may be far from the contour coordinates

of the estimated region. In this case, we extract the

contour coordinates near the selected edge in the ap-

proximate polygon (Figure 4(f)). Let x1 and y1 be

the coordinates of the left end of the selected edge in

Cervical Spine Range of Motion Measurement Utilizing Image Analysis

863

Figure 4: Flow of automatic annotation.

Figure 5: Extraction range of coordinates through which the

line passes.

the approximate polygon, x2 and y2 be the right ends,

and x M and y M be the midpoints (Figure 5). If we

denote the x-coordinate group of the contour coordi-

nates as verts x and the y-coordinate group as verts y,

the upper edge of the vertebra of interest is the contour

coordinate that satisﬁes the conditions of Equation 1,

and is the contour coordinate in the blue box in Figure

5. The lower edge is the contour coordinate that satis-

ﬁes the conditions of Equation 2, which is the contour

coordinate in the red box in Figure 5. A line is drawn

on these coordinates using the least-squares method

to ﬁnd the angle between them (Figure 4(g)). The an-

gle between the two lines is calculated from Tangent’s

additive theorem by using Equation 3, where a and b

are the slopes of the two lines.

(x1 ≤ verts x ≤ x2) ∩ (y2 ≤ verts y ≤ y1) (1)

(

(x1 ≤ verts x ≤ x M) ∩(y M ≤ verts y ≤ y1)

(x M ≤ verts x ≤ x2) ∩ (y2 ≤ verts y)

(2)

tanΘ =

a − b

1 + ab

(3)

4 EVALUATION

4.1 Conditions for Learning Model of

Cervical Spine Region

The dataset used in this study consisted of 968 X-ray

images of cervical spine ﬂexion and extension in 484

individuals aged between 20 and 100 years who were

patients at the Department of Radiology, Tokyo Med-

ical and Dental University Medical Hospital. Of these

968 images, 922 (461 patients) were used as training

data, and the remaining 46 (23 patients) were used as

test data.

We used the weights learned by Microsoft COCO

as the initial weights and updated them by re-training

the entire network with the created training data. The

training parameters are listed in Table 1. The 922

training data were divided so that the ratio of train-

ing data to validation data was 8 to 2. The learning

parameters in this experiment were as follows.

4.2 Results and Discussion of Learning

Model for Cervical Region

We performed estimation on 46 images of the test

data. Out of a total of 322 cervical vertebrae in these

46 images, 313 were successfully estimated. This

means that 97% of the total test data was able to be

detected. Intersection over Union (IoU), which is the

similarity between two sets, was used to evaluate the

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Table 1: Hyperparameters.

Training data 756 images

Validation data 166 images

Classiﬁcation Three-class classiﬁcation

Number of epochs 100

Image size 512 × 512 px

Batch size 1

Learning coefﬁcient 0.001

Optimization method Stochastic gradient descent

Table 2: IoU of each cervical spine.

Position IoU

C1 0.74

C2 0.83

C3 0.88

C4 0.88

C5 0.87

C6 0.86

C7 0.86

Average 0.85

estimated cervical region. The IoU value is obtained

by dividing the common part of the correct and esti-

mated regions by the union of the two regions, where

the maximum value is represented by 1. The higher

the value, the higher the accuracy of the object detec-

tion. The IoU values for each cervical spine vertebra

are shown in Table 2. The mean IoU value for all 46

test data was 0.85.

4.3 Accuracy Comparison between

Proposed System Measurements

and Resident Measurements

The measurement accuracy of the automatic measure-

ment method was evaluated by comparing the average

error between the true value and the automatic mea-

surement value, and between the true value and the

resident’s measurement value, using the specialist’s

measurement as the true value. A specialist in this

context refers to a physician who specializes in the

diagnosis of the cervical spine, while a resident refers

to a physician who does not specialize in the diagno-

sis of the cervical spine.

The data to be measured were the same 46 im-

ages of the test data (23 patients) used to validate the

learning model in the cervical region. The true value

was measured 69 times by two specialists, 23 persons

(test data) × 3 times (number of measurements). The

frequency of measurement was limited to once a day

and was not continuous. The true value is the average

of the three measurements taken by two medical spe-

cialists. Two residents who were given guidance on

the cervical RoM angle measurement by a specialist

were asked to measure the test data under the same

conditions as the specialist. In the measurement of

the RoM angle in the test data, the number of places

where automatic measurement was possible was 133

out of 138 places for 23 persons × 6 (places where

the RoM angle was measured). The remaining ﬁve

places were not measured because the cervical region

could not be estimated: the cervical regions were not

segmented at all.

The error between the true value and the automatic

measurement value was calculated by averaging the

difference between the two for each vertebra as an ab-

solute value. The error between the true value and the

value measured by a resident was calculated by av-

eraging the difference between the true value and the

resident’s measurement between each cervical spine

for each of the three times the resident performed the

measurement. The mean error between the true value

of the RoM angle between each vertebra in the auto-

matic and resident measurements is shown in Table

3. We also calculated the standard deviations for each

measurement by the specialist and the residents, with

the results shown in Table 4. In the automatic mea-

surement, the standard deviation was 0 because the

same value was obtained even after three measure-

ments.

We compared the errors of the true value and the

automatic measurement with those of the true value

and the residents measurement. Figure 6 shows the

comparison of the average error between the resident

and automatic measurements. There was no differ-

ence in the overall mean error between the automatic

and residents measurement. However, the variance in

error was smaller for the automatic measurement. We

performed a two-sided t-test at the 5% level of sig-

niﬁcance to see if these was statistically signiﬁcant.

Variable 1 is the error value of the resident, and the

number of samples was 798: resident (2 persons) ×

number of measurements (3 times) × 133/138 cervical

intervals. Variable 2 is the error value of the automatic

measurement, and the number of samples was 798:

133/138 cervical spine × the number of measurements

of the system (6 times). There was no signiﬁcant dif-

ference in the average error between the resident and

automatic measurements. We examined the signiﬁ-

cant differences in the errors for each of C1/2–C6/7.

There is a signiﬁcant difference in C2/3 and C5/6 and

the resident has a smaller error. C3/4 and C4/5 have

also signiﬁcant but the automatic measurement has a

smaller error.

Cervical Spine Range of Motion Measurement Utilizing Image Analysis

865

Table 3: Average error compared to the specialist measure-

ment.

Automatic

measurement

(deg)

Resident

measurement

(deg)

vertebrae

Average

error

Standard

deviation

Average

error

Standard

deviation

C1/2 5.7 4.4 5.9 8.9

C2/3 4.0 3.6 2.9 2.3

C3/4 2.5 2.4 3.1 2.7

C4/5 2.6 2.1 3.3 2.7

C5/6 3.6 2.3 2.8 2.2

C6/7 2.5 2.2 2.6 1.9

Average 3.5 2.8 3.5 3.4

Table 4: Standard deviation of specialist and resident mea-

surements.

Specialist value

(deg)

Resident value

(deg)

Vertebra Standard deviation Standard deviation

C1/2 4.4 5.2

C2/3 2.4 2.6

C3/4 2.9 2.9

C4/5 2.5 3.0

C5/6 2.7 2.7

C6/7 2.4 2.7

Average 2.9 3.2

Figure 6: Comparison of mean error between resident mea-

surements and automatic measurements.

4.4 Discussion

Figure 7 shows an example of the correct estimation

of C1 to C7, where the green color represents the cor-

rect region and the red color represents the estimated

region. The average IoU in this case was over 0.85.

Examples of incorrect estimation are shown in Figure

8. Cases (a), (c), and (d) were presumably caused by

a lack of training data, while case (b) seems to have

stemmed from a problem with the X-ray image. In the

future, we discuss whether it is possible to measure

the RoM angle of the part that could not be measured

Figure 7: Example of correct estimation.

Figure 8: Examples of incorrect estimation: (a) Large miss-

ing estimation area. (b) C6 and C7 are hidden in the pa-

tient’s chest and cannot be estimated. (c) C1 is not es-

timable. (d) Non-cervical region estimated to be cervical

spine.

by our method by learning the past RoM angle values

measured by physicians and images as a set and then

performing regression analysis.

According to the results in 4.2, the IoU values

of C1 were smaller than those of other cervical re-

gions. The IoU values of C2 also tended to be smaller

than those of C3 to C7. This indicates that the es-

timation accuracy of the cervical region of C1 and

C2 is low. Moreover, the accuracy of the proposed

system was particularly poor for C1/2 between C1/2

and C6/7. One of the factors that reduced the mea-

surement accuracy was the low estimation accuracy

of the cervical region of C1 and C2. The reason for

this low estimation accuracy is that C1 and C2 are la-

beled differently, and there is only one region in one

image, but C3 through C7 are labeled the same, so

there are four regions in one image. We therefore con-

clude that the IoU values of C1 and C2 were lower be-

VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications

866

cause these two vertebrae had less training data than

C3–C7. However, although the IoU value tends to in-

crease as the number of training images is increased,

the IoU values of C2–C7 at the current number of im-

ages tends to reach a peak. As for C1, the trend of

increasing IoU value is seen. From the above, the cur-

rent number of data sets is sufﬁcient for C2–C7. On

the other hand, it is expected that even if the number

of training images is increased, the IoU only increases

for C1. If we can increase the amount of training data,

we should be able to improve the accuracy of the es-

timation of C1.

5 CONCLUSION

In this paper, we proposed a cervical spine RoM angle

measurement assistance system to measure the cervi-

cal RoM angle by using image analysis. Our ﬁndings

showed that Mask R-CNN estimation of the cervical

region was able to estimate 97% of the total test data,

resulting in an overall IoU of 0.85. The standard de-

viation of the measurements was 2.9 degrees among

the specialists and 3.2 degrees among the residents,

while that of the proposed system was just 0, as the

measurements did not change no matter how many

times they were taken. The reproducibility, which

is an advantage of computer vision technology, al-

lowed the physician’s measurements to overcome the

problem of inconsistent values. The mean measure-

ment error of the proposed system and residents were

same value: 3.5 degrees. In the errors for each of

C1/2–C6/7, there is a signiﬁcant difference in C2/3,

C3/4, C4/5, and C5/6. However, there was no signiﬁ-

cant difference in the overall mean error between the

automatic measurement and the resident’s measure-

ment.

In the analysis of cervical X-ray images, attention

should be paid not only to the RoM angle but also to

the normal alignment of the cervical spine. In future

work, we will increase the amount of training data

to improve the accuracy of cervical spine region esti-

mation and see if we can determine the cervical mis-

alignment to estimate the defective cervical spine re-

gion. We also plan to try other segmentation methods

to compare the accuracy of the cervical region estima-

tion and the accuracy of the RoM measurement.

ACKNOWLEDGEMENTS

This work was supported by JST AIP-PRISM, grant

number JPMJCR18Y2; and JSPS KAKENHI, grant

number JP21H03485. We appreciate Dr. Kaburaki

of Tokyo Medical and Dental University for his coop-

eration in the measurements and Assistant Professor

Ienaga for his advice on this study.

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