AJR 2000; 175:1329-1334
© American Roentgen Ray Society
Automatic Detection and Quantification of Ground-Glass Opacities on High-Resolution CT Using Multiple Neural Networks
Comparison with a Density Mask
Hans-Ulrich Kauczor1,
Kjell Heitmann1,2,
Claus Peter Heussel1,
Dirk Marwede1,3,
Thomas Uthmann2 and
Manfred Thelen1
1
Department of Radiology, Johannes Gutenberg-University Mainz, Langenbeckstr.
1, 55131 Mainz, Germany.
2
Institute of Computer Science, Johannes Gutenberg-University Mainz, Staudinger
Weg 9, 55128 Mainz, Germany.
3
Institut für Radiologie,
Universitätsklinikum
Lübeck, Ratzeburger Allee 160, 23538
Lübeck, Germany.
Received November 1, 1999;
accepted after revision April 4, 2000.
Supported by the German Research Council (DFG) under grant numbers Th 315/6
and 316/10.
This article contains parts of the doctoral thesis of Dirk Marwede,
Johannes Gutenber-University Mainz, 2000.
Address correspondence to H.-U. Kauczor.
Abstract
OBJECTIVE. We compared multiple neural networks with a density mask
for the automatic detection and quantification of ground-glass opacities on
high-resolution CT under clinical conditions.
SUBJECTS AND METHODS. Eighty-four patients (54 men and 30 women; age
range, 18-82 years; mean age, 49 years) with a total of 99 consecutive
high-resolution CT scans were enrolled in the study. The neural network was
designed to detect ground-glass opacities with high sensitivity and to omit
air—tissue interfaces to increase specificity. The results of the neural
network were compared with those of a density mask (thresholds, -750/-300 H),
with a radiologist serving as the gold standard.
RESULTS. The neural network classified 6% of the total lung area as
ground-glass opacities. The density mask failed to detect 1.3%, and this
percentage represented the increase in sensitivity that was achieved by the
neural network. The density mask identified another 17.3% of the total lung
area to be ground-glass opacities that were not detected by the neural
network. This area represented the increase in specificity achieved by the
neural network. Related to the extent of the ground-glass opacities as
classified by the radiologist, the neural network (density mask) reached a
sensitivity of 99% (89%), specificity of 83% (55%), positive predictive value
of 78% (18%), negative predictive value of 99% (98%), and accuracy of 89%
(58%).
CONCLUSION. Automatic segmentation and quantification of
ground-glass opacities on high-resolution CT by a neural network are
sufficiently accurate to be implemented for the preinterpretation of images in
a clinical environment; it is superior to a double-threshold density mask.
Introduction
Several attempts have been made to use texture analysis techniques for the
automatic segmentation of CT scans
[1,2,3,4].
Classic statistical methods may include the parameters of first-, second-, and
third-order statistics and other composite or custom-made texture parameters.
Their disadvantages include the extensive training required before automated
or semiautomated segmentation, evaluation of potential usefulness of a
particular parameter only after implementation, and cumbersome and
time-consuming adaptation to new segmentation tasks. Conversely, neural
networks are promising for a modular approach to the segmentation of CT scans.
We applied self-organizing neural networks, so-called Kohonen-feature maps
[5], that use the pixel
information of CT scans, such as density, or first- and second-order
statistics as input [6]. For
the first clinical application, we chose the quantitative assessment of
ground-glass opacities on high-resolution CT scans. The neural network should
"preinterpret" the scans to assist the radiologist in making a
diagnosis and in determining the extent of ground-glass opacities. For this
screening purpose, high sensitivity and high negative predictive value are
required, and quantification is essential for follow-up examinations. In this
prospective study, we investigated the usefulness of the hierarchic network of
multiple neural networks for the automatic detection and quantification of
ground-glass opacities under clinical conditions. A currently available and
easy-to-implement approach to detect ground-glass opacities is an appropriate
density mask that can be used as a reference technique to evaluate the
improvement obtained by the neural network. The assessment of a radiologist
served as the gold standard.
Subjects and Methods
Eighty-four patients (54 men and 30 women; age range, 18-82 years; mean
age, 49 years) with a total of 99 consecutive CT scans were prospectively
enrolled in the study, which was approved by the appropriate institutional
review board. Informed consent was obtained for further processing of clinical
and imaging data. The patients were referred for CT for the following clinical
reasons: suspected pneumonia in patients at risk (n = 73), fibrosis
or alveolitis (n = 23), acute respiratory distress syndrome
(n = 1), pulmonary congestion (n = 1), and postradiation
pneumonitis (n = 1). Follow-up studies were examined for nine
patients (4 had 2, 4 had 3, and 1 had 4 examinations performed). The
prevalence of disease-related ground-glass opacity was estimated to be
approximately 50%. High-resolution CT was performed on a Somatom plus S
scanner (Siemens Medical Systems, Erlangen, Germany) with 137 kV, 210 mAs, a
1-mm section thickness, a 10-mm increment, an acquisition in maximum
inspiration (1 sec), a reconstruction with a high-frequency algorithm
(ultrahigh), and a mean field of view of 360 mm2. From all studies,
three scans were selected for segmentation. Intentionally, one scan was
obtained from the upper (apex to the carina), middle (carina to the lower lung
veins), and lower lung fields (lower lung veins to the diaphragm). Scans with
severe pulsation or respiration artifacts were excluded. The data were
transferred onto a PC-based workstation (Pentium [200 MHz, 32MB of RAM];
Intel, Feldkirchen, Germany) for postprocessing. The technical details for
image segmentation, development, implementation, and testing of the neural
network have been published elsewhere
[6]. In summary, a hierarchy of
a CT scan, two receptive fields, three neural networks, two contrast
parameters, and one expert rule was built. The receptive fields consisted of 5
x 5 pixels for networks 1 and 2 and of 9 x 9 pixels for network 3.
Network 1 had 15 x 15 neurons and divided the image into ground-glass
pixels and non-ground-glass pixels with high sensitivity by identifying the
density range from -750 to -300 H without explicit textural parameters.
However, network 1 could not discriminate homogeneous areas of ground-glass
opacity from other contrast-rich opaque areas close to high-attenuation
structures, such as bronchovascular bundles
[6]. Networks 2 and 3, both
with 10 x 10 neurons, were used to detect these contrast-rich opaque
areas using contrast as an explicit textural parameter; the networks
identified density differences between pixels as a measure of the amount of
contrast in the receptive field, and large differences corresponded to
increased contrast. The different sizes of the receptive fields of networks 2
and 3 were used to account for contrast in small and large environments.
Therefore, contrast richness was detected as a more local and a more mid-range
feature to segment (e.g., smaller and larger vessels). An equation (expert
rule) combined the three networks: Ground-glass pixels = network 1 positive
pixels - network 2 positive pixels - network 3 positive pixels. For the first
evaluation, the neural network computed the total number of pixels classified
as ground-glass opacity. The lungs were segmented semiautomatically
(thresholds, -1024/ -200 H), and the percentage of lung area affected was
calculated. For comparison, a density mask purely based on the predefined
ground-glass opacity thresholds (-750/-300 H) was applied, and the percentage
of the affected lung area was calculated. Values for agreement and
disagreement were computed. On the basis of former experience, we considered
the neural network superior to the density mask, and differences between the
two indicated gains of the neural network in sensitivity (areas detected only
by the network) and specificity (areas detected only by the density mask). For
the second evaluation, an experienced chest radiologist served as the gold
standard. Initially blinded to the results of the neural network and the
density mask, the chest radiologist visually evaluated the presence and extent
of ground-glass opacity on a workstation using different window settings. The
color-coded segmentation of the neural network was then displayed on the
monitor. The same radiologist visually estimated the size of the area with a
false-negative or a false-positive classification of ground-glass opacity.
This measurement was given as a percentage (to the nearest 5%) of the area
truly showing ground-glass opacity (gold standard). Afterward, the same
procedure was repeated for the density mask, and the percentages for
true-positive and true-negative findings were computed. Then sensitivity,
specificity, positive predictive value, negative predictive value, and
accuracy were calculated on a per-scan and per-patient basis. Differences
between the upper, middle, and lower lung fields were noted. The reasons for
false-positive findings, such as partial volume effects, respiratory motion,
or cardiac motion were recorded. The percentage of true-positive findings
corresponding to dependent opacities was also noted. The Wilcoxon's rank sum
test was used for statistical evaluation. A probability of error for the
entire set of less than 5% was regarded as significant (p < 0.05;
Bonferroni adjustment for multiple comparisons: cutoff, 0.004).
Results
Semiautomated segmentation of lung required approximately 2 min per scan,
and automated segmentation of ground-glass opacity required approximately 5
min per scan for the neural network and approximately 10 sec for the density
mask.
In the first evaluation, the neural network classified 6% of the total lung
area as ground-glass opacity (Table
1). Ground-glass opacity was least frequent in the upper lung
(5.1%), more frequent in the middle lung (5.1%), more frequent in the middle
lung (6%), and most frequent in the lower lung (6.9%). An average of 4.7% was
also detected by the density mask. An average of 1.3% was detected solely by
the neural network (Fig.
1A,1B,1C)
on the basis of textural properties. This percentage represents the increase
in sensitivity by the neural network. The density mask classified 22% of total
lung area as ground-glass opacity, and 17.3% was detected solely by the
density mask without textural features characteristic of ground-glass opacity
(Table 1). We considered the
neural network superior to the density mask, and false-positive areas detected
by the density mask were omitted; they represented the increase in specificity
by the neural network (Figs.
2A,2B,2C
and
3A,3B,3C)
Other abnormalities with a higher increase in density, such as masses or
consolidations, were not categorized as ground-glass opacity by the network
(Fig.
1A,1B,1C).
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TABLE 1 Lung Area Classified as Ground-Glass Opacity After Evaluation of CT
Scans with Neural Network and Density Mask
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Fig. 1B. —49-year-old female patient with acute respiratory distress
syndrome. Image with segmentation by neural network shows extensive
ground-glass opacities in green with concentration in core areas. Pulmonary
vessels, pleura, and consolidations are clearly delineated and not categorized
as ground-glass opacities.
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Fig. 1C. —49-year-old female patient with acute respiratory distress
syndrome. Image with segmentation by density mask shows extensive ground-glass
opacities in green. False-positive segmentations are found at borders toward
pleura and bronchial walls.
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Fig. 2A. —43-year-old female patient with pneumonia. Source image shows
ground-glass opacities in left upper and left lower lobes, indicating
pneumonia. Note dependent opacities in right lower lobe and partial volume
effect in right upper lobe.
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Fig. 2B. —43-year-old female patient with pneumonia. Image with
segmentation by neural network shows ground-glass opacities in green,
indicating pneumonia, dependent opacities, and partial volume. Note
false-positive segmentation of fissure on right side.
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Fig. 2C. —43-year-old female patient with pneumonia. Image with
segmentation by density mask shows ground-glass opacities and all peripheral
air—tissue interfaces (false-positive findings) in green.
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Fig. 3B. —27-year-old male patient with suspected pneumonia. Image with
segmentation by neural network shows small partial volume effects at pleura
and vascular borders (false-positive findings) in green.
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Fig. 3C. —27-year-old male patient with suspected pneumonia.
Segmentation by density mask shows air—tissue interfaces, peripheral
pulmonary structures, and bronchial walls (false-positive findings) in
green.
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In the second evaluation, the neural network had a sensitivity of 99%,
specificity of 83%, positive predictive value of 78%, negative predictive
value of 99%, and accuracy of 89% for the area involved with ground-glass
opacity when compared with the gold standard (Tables
2 and
3). Sensitivity and negative
predictive value did not vary between the upper, middle, and lower lung
fields, whereas we noted a decrease in specificity, positive predictive value,
and accuracy from the upper to the lower lung fields. This decrease was caused
by a significant increase in false-positive findings (overall 21.4%) from the
upper lung fields (11.2%) to the middle (21.9%) and lower lung fields (27%)
(Table 2). The false-positive
findings were caused by partial volume effects of bronchovascular bundles,
lung veins, pericardium, or pleura and pulsation artifacts. Of the
true-positive findings, 9.6% corresponded to dependent opacities. All scans
with ground-glass opacities were identified by the neural network. With regard
to the lung area, false-negative findings averaged 1.1% with a maximum of
approximately 5% on images with extensive ground-glass opacity.
The density mask was inferior to the neural network with a sensitivity of
89%, specificity of 55%, positive predictive value of 18%, negative predictive
value of 98%, and accuracy of 58% (Table
3). The high number of false-positive findings was caused by the
fact that the density mask outlined almost every air—tissue interface
(bronchovascular bundles, interlobular septa, and pleura).
The neural network was also used for follow-up examinations. Resolution of
ground-glass opacities was found in three patients in whom fever had
disappeared. Despite persistent fevers, five other patients no longer had
large areas of ground-glass opacity (>1% of total lung area) on any CT
scans. In these patients, nonspecific ground-glass opacity was no longer
thought to be related to the cause of the fever. A patient with alveolitis
showed stable disease without a change in ground-glass opacities; this finding
correlated with clinical findings.
Discussion
A self-organizing neural network was implemented for the automated
segmentation and quantification of ground-glass opacities on high-resolution
CT scans of the lung. Because of high sensitivity and high negative predictive
value, it can be used in the clinical environment for the preinterpretation of
high-resolution CT scans. When compared with a density mask, the neural
network showed a marked improvement in specificity, positive predictive value,
and accuracy.
Until now, applications of artificial neural networks in chest imaging
concentrated on the detection and evaluation of pulmonary nodules and
interstitial disease based on conventional chest radiography
[7,8,9,10,11]
and on the diagnosis of acute pulmonary embolism based on
ventilation-perfusion scintigraphy
[12,
13]. In most older studies,
neural networks were trained with expert knowledge. For example, the
radiomorphology of pulmonary nodules and the corresponding probabilities of
malignancy were used as input
[7]. The neural network gives
the accurate or the most likely diagnosis
[14],
15]. Only recent studies used
digital data of chest radiography
[8,9,10]
or of CT [4] as input for the
automatic detection of pulmonary nodules. To our knowledge, digital CT data
have not been used as direct input for self-organizing neural networks to
detect interstitial lung disease. The main reasons for this approach are
flexibility and self-organization. These properties are advantageous in
comparison with classic texture analysis (Bayesian analysis, classification of
particular feature vectors, or cluster analysis)
[16,17,18],
which has been applied for the segmentation of CT scans with various success
rates [1,
16,
19]. Because these systems are
dependent on particular trainers, users, and CT scanners, their use is not
widespread. Conversely, self-organizing neural networks do not require an
adequate and complete set of training patterns
[20], which would be extremely
difficult to obtain for an extensive and amorphous texture like ground-glass
opacity. These difficulties have already been stated as motivation for the
application of neural networks
[8]. Because of
self-organization, minor adaptive changes are necessary only for the
evaluation of scans acquired by different scanners.
The density range of ground-glass opacity has not been defined. From our
experience and from reports in the literature, we selected values from -750 to
-300 H. Additionally, ground-glass opacity has an extensive, contiguous, and
amorphous appearance [21]. Our
neural network started from the predefined density range but also correctly
included areas on the basis of texture (an additional 20% of pixels). These
pixels were not detected by the density mask, which was restricted to the
predefined density range. The automatic detection of ground-glass opacities
required a hierarchic system of multiple networks, operative fields, and
filters that have already been successfully applied to other complex problems
[13,
22]. Advantages of this system
include the consideration of original density information and textural
features, self-organization, and hierarchical structure.
On a per-patient and per-scan basis, our system had a sensitivity and
negative predictive value of 100% for the detection of ground-glass opacity;
for the area involved, the sensitivity and negative predictive value were 99%.
These results fulfill the requirements (not to overlook any areas of
ground-glass opacity) for a screening preinterpretation tool. For this
purpose, the neural network was slightly superior to the density mask with
identical thresholds (sensitivity, 89%; negative predictive value, 98%). At
the same time, specificity and positive predictive value of the neural network
were clearly superior to the density mask, 83% versus 55% and 78% versus 18%,
respectively. This improvement is sufficient to make the neural network
acceptable for routine application.
Automatic detection of ground-glass opacity has advantages over subjective
visual assessment. Neural networks work independently from viewing conditions
such as window settings [21],
laser printer settings, ambient light, and underlying diseases that may cause
heterogeneous lung density. In such cases, it may be difficult to decide
whether the hypo- or hyperatenuating areas represent normal parenchyma
[23]. Under these conditions,
the output of the network can be used as a second opinion to increase
specificity and diagnostic accuracy as has been shown for the detection of
interstitial opacities [11]
and for the differentiation of interstitial lung disease using chest
radiography [15]. The visual
assessment of ground-glass opacity comprises extent and severity and is highly
interpreter-dependent. Conversely, the neural network provides an objective
measure for ground-glass opacity. One could argue that the severity of
ground-glass opacity is not adequately considered by the neural network.
However, the hierarchic structure of multiple networks facilitates and favors
the detection of homogeneous areas with high edge contrasts, which correspond
to a higher subjective severity rating. Therefore, the severity is represented
in the segmented area.
Our study has some limitations. Segmented areas of extensive ground-glass
opacity were up to 5% smaller than those identified by the radiologist,
findings that represent systemic bias. This bias is caused by the hierarchic
structure of the network, which favors a concentration on the core areas of
ground-glass opacity together with a marked delineation between ground-glass
opacity and denser structures such as vessels or pleura (Fig.
1A,1B,1C).
This property will not affect the comparability of results among different
patients or during follow-up examinations. The prevalence of ground-glass
opacity in the study population (approximately 50%) reflects the diseases that
are investigated by high-resolution CT. Because positive predictive value and
negative predictive value are dependent on pretest probability, positive
predictive value might be underestimated and negative predictive value might
be overestimated.
The neural network was not able to overcome typical pitfalls in the
detection of ground-glass opacities such as cardiac and respiratory motion
artifacts. Partial volumes of bronchovascular bundles, chest wall, diaphragm,
and pleura as well as air—tissue interfaces, which have the same density
quality as ground-glass opacities, were the most frequent pitfalls. This
pitfall has not been described in major publications because it does not pose
a real problem to radiologists. Approximately 10% of the ground-glass
opacities were attributed to simple dependent opacities. If ground-glass
opacities segmented by the neural network had always been regarded as
abnormal, subsequent clinical treatment would have been different from that
following the evaluation of the radiologist. Although neural networks are
sufficiently accurate to be used for the preinterpretation of ground-glass
opacities, the final diagnosis of pneumonic infiltrates, alveolitis, fibrosis,
dependent opacities, or artifacts is the task of the radiologist.
Acknowledgments
We thank Patty Meinhardt for her editorial assistance in preparing this
manuscript.
References
-
Delorme S, Keller-Reichenbecher MA, Zuna I, Schlegel W, van Kaick
G. Usual interstitial pneumonia: quantitative assessment of high-resolution
computed tomography findings by computer-aided texture-based image analysis.
Invest Radiol
1997;32:566
-574[Medline]
-
Brown MS, McNitt-Gray MF, Mankovich NJ, et al. Method for
segmenting chest CT image data using an anatomical model: preliminary results.
IEEE Trans Med Imaging
1997;16:828
-839[Medline]
-
Heitmann K, Rueckert S, Heussel CP, Uthmann T, Thelen M, Kauczor
H-U. Fuzzy-neuronale Netze zur automatischen Milzvolumen-Berechnung aus
Spiral-CT-Daten. Vergleich mit einer Standard Dichtemaske [abstract in
English; text in German]. Fortschr Roentgenstr
2000;172:139
-146
-
Henschke C, Yankelevitz D, Mateescu I, Brettle D, Rainey T,
Weingard F. Neural networks for the analysis of small pulmonary nodules.
Clin Imaging
1997;21:390
-399[Medline]
-
Kohonen T. Analysis of a simple self-organizing process.
Biol Cybern
1982;44:135
-140
-
Heitmann KR, Kauczor H-U, Mildenberger P, Uthmann T, Perl J, Thelen
M. Automatic detection of ground glass opacities on lung HRCT using multiple
neural networks. Eur Radiol
1997;7:1463
-1472[Medline]
-
Gurney JW, Swensen SJ. Solitary pulmonary nodules: determining the
likelihood of malignancy with neural network analysis.
Radiology
1995;196:823
-829[Abstract/Free Full Text]
-
Lin JS, Hasegawa A, Freedman MT, Mun SK. Differentiation between
nodules and end-on vessels using a convolution neural network architecture.
J Digit Imaging
1995;8:132
-141[Medline]
-
Wu YC, Doi K, Giger ML, Metz CE, Zhang W. Reduction of false
positives in computerized detection of lung nodules in chest radiographs using
artificial neural networks, discriminant analysis, and a rule-based scheme.
J Digit Imaging
1994;7:196
-207[Medline]
-
Wu YC, Doi K, Giger ML. Detection of lung nodules in digital chest
radiographs using artificial neural networks: a pilot study. J
Digit Imaging 1995;8:88
-94[Medline]
-
Monnier-Cholley L, MacMahon H, Katsuragawa S, Morishita J, Ishida
T, Doi K. Computer-aided diagnosis for detection of interstitial opacities on
chest radiographs. AJR
1998;171:1651
-1656[Abstract/Free Full Text]
-
Scott J, Palmer E. Neural network analysis of ventilation-perfusion
lung scans. Radiology
1993;186:661
-664[Abstract/Free Full Text]
-
Tourassi GD, Floyd CE, Sostman HD, Coleman RE. Artificial neural
network for diagnosis of acute pulmonary embolism: effect of case and observer
selection. Radiology
1995;194:889
-893[Abstract/Free Full Text]
-
Boone JM. Neural networks at the crossroads.
Radiology
1993;189:357
-359[Free Full Text]
-
Ashizawa K, MacMahon H, Ishida T, et al. Effect of an artificial
neural network on radiologists' performance in the differential diagnosis of
interstitial lung disease using chest radiographs. AJR
1999;172:1311
-1315[Abstract/Free Full Text]
-
Giger ML, Bae KT, MacMahon H. Computerized detection of pulmonary
nodules in computed tomography images. Invest Radiol
1994;29:459
-465[Medline]
-
Gurney JW, Lyddon DM, McKay JA. Determining the likelihood of
malignancy in solitary pulmonary nodules with bayesian analysis. II.
Application. Radiology
1993;186:415
-422[Abstract/Free Full Text]
-
Jain A. Cluster analysis. In: Young T, Fu K, eds.
Handbook of pattern recognition and image processing.
San Diego: Academic Press, 1986:33
-57
-
Döhring W, Linke G. Ein
Programmsystem zur quantitativen Auswertung von Computertomogrammen unter
Anwendung einer digitalen Maskentechnik zur Isolierung interessierender Organe
und Organbereich aus der CT-Wertematrix. Fortschr
Röntgenstr
1986;144:135
-148
-
Boone JM, Gross GW, Greco-Hunt V. Neural networks in radiologic
diagnosis. I. Introduction and illustration. Invest
Radiol 1990;25:1012
-1016[Medline]
-
Remy-Jardin M, Remy J, Giraud F, Wattinne L, Gosselin B. Computed
tomography assessment of ground glass opacity: semiology and significance.
J Thorac Imaging
1993;8:249
-264[Medline]
-
Tsujii O, Freedman MT, Mun SK. Automated segmentation of anatomic
regions in chest radiographs using an adaptive-size hybrid neural network.
Med Phys
1998;25:998
-1007[Medline]
-
Im J-G, Kim IH, Chung MJ, Koo JM, Han MC. Lobular low attenuation
of the lung parenchyma on CT: evaluation of forty-eight patients. J
Comput Assist Tomogr
1996;20:756
-762[Medline]
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Abstract
Introduction
