Pulmonary Nodule Segmentation in Lung CT Images by Post Processing U-Nets Using Average Ensemble Learning Technique
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Date
2020-06-24
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Addis Ababa University
Abstract
Pulmonary nodules are potential manifestation of lung cancer and accurate segmentation of
different pulmonary nodules from lung Computed Tomography (CT) scan images is important
clinical relevance in diagnosis, prognosis and treatment of lung cancer. However, due to the
highly heterogeneous type, size, location and shape of nodules, segmentation of pulmonary
nodules is very challenging. In this study we propose and present an improved pulmonary nodule
segmentation method based on thresholding and morphological operators, lungs region
segmentation algorithm, modified U-Net model for pulmonary nodules detection and
segmentation, and average ensemble learning as a post processing technique. First, as a preprocessing
stage
that
includes
lungs
region
segmentation,
normalization,
cropping
and
resizing
has
done on
the
raw
input
CT
scan
images.
Thresholding
and
morphological
operators
algorithm
is
simple
and
yields
good
result
in
accurately
segmenting
the
lung
parenchyma
to
reduce
the
search
space.
Then
modified
U-Net
model
is
loaded
to
detect
and
segment
pulmonary
nodules.
U-Net
is
a
widely
used
Convolutional
Neural
Network
(CNN)
structure
for
end-to-end
segmentation
and
can
be
used
on
the
entire
image
classification
labels
over
the
entire
input
pixels
so
that
it
is
more
efficient
and
expected
to
yield
better
performance.
Furthermore,
instead
of
picking
the
best
U-Net
network
structure,
we
applied
average
ensemble
learning
method
as
a
post
processing
technique.
An
ensemble
of
three
U-Net
models
having
different
network
structure,
trained
on
the
same
dataset
with
different
hyper-parameters,
can
generally
improve
the
overall
segmentation
performance.
The
performance
of
our
proposed
method
is
trained,
tested,
and
evaluated
using
858
lung
CT
images
and
their
corresponding
ground
truth
nodule
masks
obtained
from
Lung
Nodule
Analysis 2016
(LUNA16)
dataset
and
achieved
evaluation
results
of
0.848
mean
Dice
Similarity
Coefficient
(DSC),
0.965
mean
accuracy,
0.826
mean
sensitivity,
and 0.983
mean
specificity.
Moreover,
we
compared
our
test
results
with
other
methods
results
to
show
our
approach’s
performance.
Experiments
and
these
preliminary
results
showed
that
our
proposed
method
can
effectively
improve
the
segmentation
accuracy
of
pulmonary
nodules
and
the
effectiveness
of
our
approach.
Generally,
the
algorithms
used
here
are
simple,
effective
and
practical.
Description
Keywords
CT, LUNA16, thresholding and morphological operators, pulmonary nodules, U-Net, average ensemble learning, DSC