Automatic lung segmentation in functional SPECT images using active shape models trained on reference lung shapes from CT
Automatic lung segmentation in functional SPECT images using active shape models trained on reference lung shapes from CT
GrigoriosA‑ris Cheimariotis 0 1 2 4
Mariam Al‑Mashat 0 1 2 4
Kostas Haris 0 1 2 4
Anthony H. Aletras 0 1 2 4
Jonas Jögi 0 1 2 4
Marika Bajc 0 1 2 4
Nicolaos Maglaveras 0 1 2 4
Einar Heiberg 0 1 2 4
0 Department of Biomedical Engineering, Faculty of Engineering, Lund University , Lund , Sweden
1 Department of Clinical Sciences Lund, Clinical Physiology, Skåne University Hospital, Lund University , Lund , Sweden
2 Laboratory of Computing, Medical Informatics and Biomedical-Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki , Thessaloniki , Greece
3 Einar Heiberg
4 Department of Clinical Physiology, Lund University Hospital , 22185 Lund , Sweden
Objective Image segmentation is an essential step in quantifying the extent of reduced or absent lung function. The aim of this study is to develop and validate a new tool for automatic segmentation of lungs in ventilation and perfusion SPECT images and compare automatic and manual SPECT lung segmentations with reference computed tomography (CT) volumes. Methods A total of 77 subjects (69 patients with obstructive lung disease, and 8 subjects without apparent perfusion of ventilation loss) performed low-dose CT followed by ventilation/perfusion (V/P) SPECT examination in a hybrid gamma camera system. In the training phase, lung shapes from the 57 anatomical low-dose CT images were used to construct two active shape models (right lung and left lung) which were then used for image segmentation. The algorithm was validated in 20 patients, comparing its results to reference delineation of corresponding CT images, and by comparing automatic segmentation to manual delineations in SPECT images. Results The Dice coefficient between automatic SPECT delineations and manual SPECT delineations were 0.83± 0.04% for the right and 0.82 ± 0.05% for the left lung. There was statistically significant difference between reference volumes from CT and automatic delineations for the right (R = 0.53, p = 0.02) and left lung (R = 0.69, p < 0.001) in SPECT. There were similar observations when comparing reference volumes from CT and manual delineations in SPECT images, left lung (bias was − 10 ± 491, R = 0.60, p = 0.005) right lung (bias 36 ± 524 ml, R = 0.62, p = 0.004). Conclusion Automated segmentation on SPECT images are on par with manual segmentation on SPECT images. Relative large volumetric differences between manual delineations of functional SPECT images and anatomical CT images confirms that lung segmentation of functional SPECT images is a challenging task. The current algorithm is a first step towards automatic quantification of wide range of measurements.
Image segmentation; V/P SPECT; CT; Active shape model
Ventilation/perfusion single photon emission computed
tomography (V/P SPECT) is an important routine
diagnostic tool, to validate pulmonary function in different diseases
such as pulmonary embolism, pneumonia, heart failure and
tumors. In V/P SPECT both a lung ventilation and perfusion
scan is performed in direct succession. The recommended
methods are based on the registration of ventilation and
perfusion images to measure pulmonary function using
quotient images. V/P SPECT methods have been validated on
pigs, phantom, and in the clinic for diagnosis of pulmonary
embolism by phenotyping character of ventilation/perfusion
]. For phenotyping changes in pulmonary
function, such as left heart failure, Jögi et al. developed a method
to quantify perfusion gradient, which had been visually
recognized since 1966 [
Segmentation is necessary to make objective
measurements of lung function and characterization of
pathophysiological changes. Previous work has been focused on
semiquantitative analysis [
]. User-independent segmentation
of the lung is likely to be an essential further improvement to
these techniques, to facilitate implementation on the larger
scale for routine use. Quantitative segmentation is also the
first step for the assessment of functional lung volumes.
Computed tomography (CT) is the reference method for
lung morphology and lung volume estimation. For many
clinical indications, ventilation and perfusion scintigraphy
functional imaging is preferred and is the indicated method.
To quantify functional changes, image segmentation is
required as quantification methods need to quantify changes
only inside the lung.
To date, there has been little focus on lung
segmentation on V/P SPECT images [
]. He et al. derived the lung
segmentation from V/P SPECT images by utilizing
crossentropy threshold selection. Kwa et al. [
] have focused
on CT images and co-registration with SPECT images. For
this segmentation process, the authors used a global binary
segmentation method. A similar approach was used by
Harris et al. [
], where CT images were registered to SPECT
images to analyze lobar ventilation/perfusion relationships.
Meir et al. [
] used registration of SPECT and CT images
to perform texture analysis and demonstrated the potential to
perform disease classification. Fleming et al. [
a method which uses empirical rules derived from the
analysis of computer simulated images and local thresholds. The
current aforementioned segmentation methods have not been
validated against an independent reference standard.
The main challenge in lung SPECT segmentation is
that there may be perfusion/ventilation defects so that the
parts of the lungs are not possible to differentiate from the
background and the use of thresholding is not sufficient to
segment the lungs. To accurately delineate the lung
contours, anatomical knowledge about the expected lung shape
is required. The active shape model approach (ASM) is a
method which incorporates the expected shape into the
segmentation process, known as a priori information.
CT provides image contrast between lung tissue and
surrounding tissue enabling anatomic lung contour to be more
easily delineated. As such, CT also provides a suitable
reference standard for the volume measurement of lung SPECT
The aim of the study was to develop an algorithm for
automatic segmentation of the lungs in V/P SPECT images
using active shape model of the lungs, trained by 3D shapes
delineated in CT images and validate this automatic SPECT
segmentation method against CT images as well as against
SPECT manual delineations. Furthermore, the study
compares manual segmentation in SPECT images against
reference CT images so as to improve the understanding of
limitations in segmenting V/P SPECT images.
In total 77 subjects were included. All subjects but eight
come from a previously published study with known lung
] The population were as follows; patients with
stable chronic obstructive pulmonary disease (COPD)
defined by spirometry (n = 55; age = 68 ± 5; 25 women;
global obstructive lung disease score (GOLD) [
smoking 15–159 pack-year), and a set of current or former
longtime smokers (n = 14, age = 69 ± 3, 7 women, smoking 24–40
pack-year) that did not have COPD defined by spirometry.
All subjects were over the age of 40 years, clinically stable
and, in the case of COPD patients, without any
exacerbations during the 4 weeks prior to inclusion. In addition to the
above 69 subjects, 8 subjects were included retrospectively
from clinical routine without apparent perfusion of
ventilation loss in SPECT.
The study was approved by the local regional ethics
committee, and informed consent was obtained from all subjects
before enrolment. The subjects were imaged with a low-dose
CT, in conjunction to the V/P SPECT examination.
The material was divided into two sets, one for training
and one for performance evaluation. The evaluation set was
chosen by randomly selecting 20 subjects from the
population with known lung disease. In the evaluation set 12 of
the selected subjects had COPD on spirometry and 8 where
current or former smokers. The used random generator
was Matlab R2014a (Mathworks, USA). The training set
consisted of 57 subjects (the remaining 49 patients and 8
subjects). The purpose of a wide range of subjects from 8
apparently healthy subjects, 14 former or current smokers,
and 55 with COPD was first to develop a robust algorithm
that would be able to also handle both normal and
challenging cases, and second investigate difference in manual lung
segmentation in difficult cases.
The examination was performed as a one-day protocol,
according to the European Guidelines [
], where it is
suggested that the ventilation is performed before the
perfusion, followed by image acquisition.
V/P SPECT and low dose CT imaging were performed
using a Philips Precedence system, which combines a dual
head gamma camera with a Brilliance 16 slice CT.
Imaging begun with a CT overview image and continued with a
diagnostic low dose CT (120 kV, 20 mAs/slice, 16 ×
1.5 collimator, 0.5 s rotation time, and pitch of 0.813). The slice
thickness was 5 mm and incremental value was 5 mm.
Filtered back projection was used for reconstruction. The
CT was used to co-localize the morphological and
functional changes visualized in either of the two modalities
and obtained during free tidal breathing. Thereafter, the
patients in supine position inhaled 30 MBq of Technegas,
Cyclomedica Ltd and ventilation imaging was performed.
This was followed by perfusion images were acquired using
an intravenously injection of 140 MBq 99 m-Technetium
labelled macroaggregated albumin (TechneScan LyoMAA;
Mallinckrodt Medical BV). Care was taken to ensure that
the patient maintained the same supine position. Imaging
parameters were as follows, acquisition was performed in
a 64·64 matrix, zoomed to a pixel size of 6.8 mm with 128
projections over 360°. Sixty-four steps, each of 10 s
duration, were used for the ventilation study, and 64 steps of 5 s
duration were used for the perfusion study. Reconstruction
was performed using ordered subsets expectation
maximization with eight subsets and two iterations.
Segmentation algorithm overview
The segmentation was performed on a sum image of both
ventilation and perfusion. The segmentation method was
based on the active shape model approach [
]. In short,
the active shape model approach a mean shape is place in
the image and are allowed to deform until the shape fits the
image. In a training step both mean shape as well as a priori
information on allowed deformations are extracted.
The training set was used to create two shape priori
models, one for each lung. The training was based on lung shapes
extracted from CT images in the training set. This training
process was performed once and is illustrated in Fig. 1. More
details are given in the section below.
Shape model extraction
Semi‑automated segmentation of CT images
Contours in coronal CT slices were derived using an
automated method that combined simple thresholding based
on Hounsfield units and binary morphological operations
to find the two largest coherent areas representing left and
right lung, respectively. Thereafter further binary
operations were performed to fill holes in the segmented lung
shapes. Based on the edge of the binary objects, contours
were extracted. The contours were carefully inspected by a
trained observer (biomedical laboratory scientist), and were
manually corrected to remove the vessels and other cardiac
CT images (N=57)
Semi-automated segmenta on of lungs
Shape alignment (Procrustes)
Principal component analysis
A priori lung (expected shape) model
structures. For difficult cased another observer (expert
physician) provided second opinion. An example of the
semiautomated segmentation is illustrated in the insert of Fig. 1
which depicts the training process. Two sets of
segmentations were constructed, one for each lung. As a consequence,
the shape representation of the lung in CT images was a set
of points distributed along the lung perimeter for each slice
of the image volume.
The training phase consisted of four main parts:
semiautomatic lung segmentation, shape parameterization,
aligning shapes to each other and principal component analysis
(PCA). The PCA computes orthogonal modes deformation
based on all training examples. Training data consisted of
semi-automatically segmented volumetric CT images. This
information needed to be parameterized so as to permit
usage of the active shape models theory. In the next step,
the set of training shapes were aligned by a version of the
Procrustes algorithm. In short, all points in a shape were
transformed by stretching and rotating to best fit the mean
shape. In the final part, PCA was performed and the outcome
mean lung shape and its main variability modes were input
to the segmentation procedure to constrain the model
deformation to physiological plausible shapes.
The active shape model theory required that we
mathematically described the lung in a way that for each point in
one lung, we could identify the corresponding point in the
other lung [
]. We used separate models for the left and
right lung, respectively. Thus, there was a need for equal
number of points in each lung. We selected three landmarks
which were annotated for each lung in all imaging slices.
These landmarks were defined as point extremas of slice
contours: top, bottom left and bottom right. In between, N
points (pseudo-landmarks) of the original representation
were sampled in equal distances [
]. As a consequence,
every slice contour was described by 3 × (N − 1) points,
where 3 is the number of landmarks and N − 1 is the number
of points between each landmark.
Having the same number of points in each slice is not
enough we also require the same number of slices for each
subject. Therefore, the training shapes were reshaped to keep
a constant number of slices in the z- (from chest to back)
direction. In the training set, the average number of slices
were 30 (resolution in z-direction was 5 mm). We, therefore,
chose to describe each shape with 3 × (N − 1) × 30 points.
The rationale for choosing 30 was to minimize the impact
of slices resampling. The resampling procedure was carried
out as follows: Each point in a slice was connected with a
straight line to the corresponding point in the slices above
and/or below. These connections resulted in 3 (N − 1)
connected line segments. New points were interpolated on the
intersections of these lines with a fixed number 30 slices
with the same distance between them.
Thus, the representation of each lung X was
3 × ( N − 1) × 30 points:
X = x1,y1, z1,.., xn−1, yn−1,zn−1, xn,yn,zn ,
where 3 is the number of landmarks per slice, (N-1) is the
number of points between landmarks, and 30 is the fixed
number of slices.
To measure the shape variation, each lung X was transformed
by translation parameters: tx, ty, tz,scaling parameters s, and
rotation parameters θ, φ into a common frame of reference.
X = Ttx,ty,tz,s, , (X).
The centroid of a lung shape is defined byC = x , y , z ,
where x is the mean value of x1, … xn−1, xn coordinates of
the shape (1) and so forth. By subtracting from each point
of a shape its centroid’s coordinates (x , y , z ), the shape
X = x1 − x , y1 − y , z1 − z , .., xn−1 − x , yn−1 − y , zn−1
−z , xn − x , yn − y , zn − z , is translated and its new
centroid is the image origin (0,0,0). This process was repeated
for all the shapes, so that all shapes had a common centroid.
Calculation of volume was defined by the Frobenius
]. The calculation was simplified due to the
for all i points of a lung shape. Then, all coordinates
were divided by Vol so that all scaled shapes had volume
|X| = 1 . An implementation of the Procrustes
algorithmn rotated each shape so that the sum of distances
D = ∑ Xi − X̃ 2 of each shape Xi to the mean shape
, was minimized (m is the number of
Specifically, the following steps were performed: [
First, every shape was rotated to get aligned to one random
shape of the set. Second, the mean shape was calculated
and then every shape was rotated to align to it. This final
step was repeated until a low value of sum of distances D
was achieved (D < 1).
Principal component analysis
The core of the shape model extraction was principal
component analysis, which was used to define main variations
of the shapes model.
To avoid non-linearity in the aligned training set, all
shapes that were stored in one matrix were projected in
tangent space by scaling them by 1 Xi × X̃ where X̃ is
the mean shape that occurred after the last iteration of the
alignment process. The mean shape was subtracted from
the shape matrix, to calculate the covariance matrix:
Xi − X̃ × Xi − X̃ T .
The eigenvectors pi and eigenvalues i of this
covariance matrix were calculated. A shape instance could be
generated by deforming the mean shape by a linear
combination of eigenvectors:
X̃ + P × b,
where P was a matrix with t number of eigenvectors and b
was the shape coefficients vector. The number of
eigenvectors to retain, t, was chosen so that the model represented
some proportion (e.g. 95%) of the total variance of the data
]. Eigen modes that may be caused by outliers or by bad
alignment were removed because they may have created
unrealistic shapes. The selection of the number of eigen
modes affected the quality of the shape model as well as
the segmentation procedure. If the eigen modes were few
then the segmentation was over constrained and if there were
too many then the segmentation results may have appeared
A flow chart of the segmentation process is illustrated in
Fig. 2. First, a summation of both ventilation and
perfusion image volumes was created. Before summation, the two
respective image volumes were normalized with the
largest pixel intensity. The mean lung shape was placed into
the image volume. The initial location was found by simple
binary segmentation of the lungs. The lung shape iteratively
updated both position and shape based on image intensities
in both ventilation and perfusion images. The active shape
model discarded shapes that were not plausible. This helped
to overcome the possibility of erroneous segmentation due
to matched defects and consequently helped to recognize
Fig. 2 Flow-chart of the
proposed automatic lung
To identify initial location and size, a low threshold (15% of
the maximum intensity of the image) was applied to extract a
binary lung volume. This initial segmentation overestimated
the volume, but was used to calculate the centroid of the
lung. Furthermore, it allowed estimation of upper and lower
limits of the lungs.
The initial shape was the mean shape calculated in the
training phase. The shape was then transferred to fit the limits
defined by binary segmentation. First, the mean shape was
scaled to obtain the volume of binary shape. Then, it was
rotated to be the reference shape. To align the mean shape
x̃ to a shape of reference xref , their inner product Xp was
Xp = X̃ × X ref.
Ven la on & perfusion SPECT images
Create sum image
Binary segmenta on
Ini aliza on of
shape posi on
Get profiles in
normal direc ons
Update posi ons
Project to a priori
model and check
Final segmenta on
By performing single value decomposition on X, we get
U, S, V i.e. the matrices of specific properties that satisfy
the equation X = U × S × VT . The rotated mean shape was
X̃ rot = (V ∗ U ) ∗ X̃ .
Finally, X̃ rotwas translated so as to have the same centroid
as the binary segmented volume.
The initial shape was iteratively updated according to
intensity of pixels that were normal to the contour. For each
update step, the shape was restricted to only generate
contours that were plausible according to the training data [
Each point in the contour was moved to new positions based
on intensity profiles and their derivatives which were normal
to the contour. The location of the minimum of the
derivatives indicated where to place the updated contour point. In
addition, there are two rules that overrode the above
behaviour so as to avoid getting stuck in local minima or maxima:
(a) if the intensity on the contour point was above 30% then
it moved outwards and (b) if the intensity was below 5%
then it moved inwards (intensities were normalized in the
space 0–1). This last rule was applied so as to ensure that
the contour shrunk to the expected position if it extended
outside the lungs. The shape was updated for a fix number
of ten iterations which was after experiments was deemed
sufficiently to achieve convergence.
Constrain to plausible shapes
To ensure that the updated shape was plausible we first
aligned it to the mean shape of the shape model [
Thereafter, we described the difference to the mean shape by
projecting the shape on the shape model basis (i.e. we
calculated b = Φ × (X − X̃ ), where Φ was the matrix with all
eigenvectors that corresponded to the Eigen modes chosen).
Then, if the elements in b were inside the space
− i × √2, i × √2 , where i the eigenvalue that
corresponded to each bi element, the movement calculated in
boundary search was accepted. If not then their values were
adjusted either to i × √2 or− i × √2. Therefore, either the
original b parameters were kept or some were adjusted to the
space − i × √2, i × √2 . This process was iterated
sufficiently for convergence (ten times).
The semi-automated segmentation of the corresponding CT
images was used as the reference standard. The accuracy of
the automatic segmentation was evaluated both as
difference in lung volumes compared to the reference CT
standard, as well as comparison between automatic
segmentation and manual segmentation in SPECT images using Dice
To further evaluate the performance of the algorithm, the
lung contour provided by the algorithm was compared with
the manual SPECT delineation of the lung. Manual SPECT
delineations were performed in a subset of 20 patients by
one observer (biomedical laboratory scientist). For difficult
cases another observer (physician) performed second
opinion. The manual delineations were then compared to the
To validate accuracy of lung shape and position, the
Dice coefficient was computed [
]. The Dice coefficient
D is defined by D = 2 × X ∩ Y ∕ ( X + Y ), where X
represented all voxels of the reference and Y represented all
voxels of the automated method. We also used sensitivity (S)
and precision (P) over the entire image, defined as S= TP/
(TP + FN) and P = TP/(TP + FP) with TP as the number of
true positives (voxels that were part of both reference
segmentation and automatic segmentation result), FP as the
number of false positives (voxels that were segmented but
were not part of the reference segmentation) and FN as the
number of false negatives (voxels that were not segmented
but were part of the reference). Separate analyses were made
for the right and left lungs.
The mean values for reference volumes from CT, manual
SPECT and automatic SPECT delineations for the right
and left lung are presented in Table 1. The volumes from
CT were 1673 ± 582 ml (left lung), and 2080 ± 633 ml
Fig. 3 CT image, manual and automatic segmentation of two
patients. Top row shows a slice from a patient with normal
ventilation/perfusion and the bottom row shows a slice from a patient with
peripheral loss of both ventilation and perfusion. The left column
shows CT images, the middle column shows manual segmentations,
and the right column shows automatic segmentations. Green
delineation colour = left lung, red delineation colour = right lung
4 0 0 0
) 3 0 0 0
e 2 0 0 0
V 1 0 0 0
L e f t l u n g
(right lung). The volumes from automatic delineations
were 1732 ± 403 ml (left lung), and 2085 ± 399 ml (right
lung). The volumes from manual SPECT delineations was
1684 ± 505 ml (left lung), and 2044 ± 554 ml (right lung).
Figure 3 shows two patient examples, where to top row is
a patient without any apparent loss of peripheral ventilation/
perfusion and the bottom row shows a patient with
peripheral loss of both ventilation and perfusion. Left column show
CT image, middle column show manual segmentation, and
right column show automatic segmentation.
and for the right lung, the bias was − 5 ± 540 ml, (Fig. 6,
Comparison between manual SPECT and automatic
There was no observed statistically significant volumetric
difference between manual SPECT and automatic SPECT
delineations for the left and right, Fig. 4, p = 0.4 (left lung)
and p = 0.6 (right lung). A summary of the comparison
between manual SPECT and automatic SPECT delineations
for the right and left lung are presented in Table 2. Figure 5
shows a scatter plot and a Bland–Altman plot comparing
manual and automatic delineations in SPECT images.
Automatic SPECT delineations vs reference CT
There was a significant difference between the reference
volumes from CT and automatic SPECT delineations for
the left lung (R = 0.69, p < 0.001, Fig. 6, left column) and
right lung (R = 0.53, p = 0.02, Fig. 6, right column). For the
left lung, the bias was − 59 ± 420 ml (Fig. 6, left column),
Manual SPECT delineations vs reference CT
There was a statistically significant difference between the
reference volumes from CT and manual delineations on
SPECT images for the left lung (R = 0.60, p = 0.005, Fig. 7,
left column) and right lung (R = 0.62, p = 0.004, Fig. 7, right
column). For the left lung, the bias was − 10 ± 491, (Fig. 7
left column). For the right lung, the bias was 36 ± 524 ml,
(Fig. 7, right column). The coefficient of variation
comparing manual SPECT delineation vs reference volume from CT
are 29, and 25% for left and right lung, respectively.
A method for the automated segmentation of V/P SPECT
images was developed. The automated segmentation and the
manual delineations on V/P SPECT images yielded volumes
that were not significantly different. The shape between
automated segmentation and manual delineation on V/P SPECT
images agreed well. However, both manual and automatic
segmentation did not manage to estimate well with respect
to the reference CT volumes. This highlights the difficulties
in segmentation of lung volumes in functional images.
The idea of using anatomical lung shapes derived from
reference CT lung images and use them to train an active
shape model applied to lung SPECT images is new. The
approach of cross imaging modality using active shape
models were previously proposed by Ordas et al. where the
authors presented an approach using active shape models
extracted from cardiac magnetic resonance imaging for
leftventricle segmentation on SPECT images [
Comparison between CT and SPECT images
The idea of comparing volumes from CT images with both
automatic and manual delineations of V/P SPECT images is
novel. CT images depicts the anatomical parts of the lungs,
while the SPECT images depicts the functional parts of the
lungs. Given this, this method illustrates the challenge of
anatomical segmentation in functional SPECT images.
Importantly, there was no observed statistically
difference between volumes extracted from manual SPECT and
automatic SPECT delineations. The Dice analysis also
confirmed that the results were similar and showed that the
segmentation performance was better for the right lung
compared to the left lung. This is in part expected, as the position
and shape of the left lung is dependent on the position and
shape of the heart, particularly in heart disease.
The results showed a significant difference between
volumes from anatomical reference CT images compared to
both automatic SPECT and manual SPECT delineations
of functional images. One of the explanations is that the
patients that were studied represent a group with very severe
obstructive lung disease with large areas of reduced/absent
ventilation and perfusion in the periphery. Peripheral loss
of function limits the possibilities to find correct
anatomical outlines by both manual SPECT and automated SPECT
segmentations of the lungs.
The active shape model approach correctly estimated the
expected lung shape regardless of loss of
ventilation/perfusion. Peripheral defects still allowed the estimation of
peripheral contour with the proposed automated
segmentation method. An example is illustrated in Fig. 3.
We used CT as a reference standard for lung volumes.
An alternative approach would be to use a lung phantom
as an objective reference standard. The selection of
landmarks is important for active shape appearance models.
In this study, three landmarks (reference points in the
lung contour) were used with a fixed number of contour
points between each landmark (see landmark placement
in Fig. 1). The landmarks worked well for the right lung.
However, for the left lung it was observed that the
landmarks LB and LC often were placed too close to each
other and did not have a distinctive anatomical location.
We tried to only use two landmarks (LA and LC), but this
did not improve the results for the left lung.
Given the results in this study with automated SPECT
segmentation of the lungs it would be beneficial to add
calculation of perfusion gradient and validate it to
existing algorithms. Moreover, we would like to use an active
shape model framework to automatically generate a 3D
lung segment division. This could be accomplished by
transferring manual lung segment charts [
] to the shape
model. This would result in patient-specific lung
segmental charts and could allow for the quantification of regional
functional loss automatically.
The algorithm presented in this study showed results
comparable to manual delineations functional SPECT images.
Relative large differences between manual delineations
of functional SPECT images and anatomical CT images
show that anatomical segmentation of V/P SPECT images
is a challenging task. The present algorithm is a first step
towards automatic quantification of wide range of
measurements, such as perfusion gradients.
Author contributions All authors have contributed significantly to the
paper, and read and approved the final manuscript version.
Compliance with ethical standards
Funding sources This study was funded with grants from the Swedish
Heart and Lung Foundation, The Medical Faculty of Lund University
(ALF) in Sweden and Region of Scania, Sweden, Greek State
Scholarships Foundation and European Social Fund.
Conflict of interest All authors declare no conflict of interest, except
for Einar Heiberg who is the founder of Medviso AB, producing
medical image analysis software.
Open Access This article is distributed under the terms of the Creative
Commons Attribution 4.0 International License
(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give appropriate
credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made.
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