Abstract mortalities. There were 1.69 million deaths in

Abstract
– Lung Cancer is the most deadly cancer. 
Early detection of the disease can improve survival rate. Automation of
lung nodules detection aid radiologists in quickly and accurately diagnosing
the disease. Developing computer aided diagnosis (CADx) systems for lung cancer
is a challenging task. Several components make up CADx. One of the most
significant components is lung segmentation, an essential prerequisite to efficiently
detect and classify lung nodules. Lung segmentation is the process of
segregating lungs fields from other tissues in the CT image. Conventional
methods for lung segmentation either do not accurately segments normal and
abnormal lungs or rely heavily on user generated features for the lungs. Deep
learning has outperformed other methods in image processing tasks. Recently a
new architecture has been proposed and implemented exclusively for medical
images to solve this problem namely U-Net convolutional network. In this study u-net
has been implemented on lungs dataset consisting of 267 CT images of lungs and
their corresponding segmentation maps. The accuracy and loss achieved is .
Considering the computational constraints the results obtained are state of the
art.

Keywords – U-Net convolutional
network; Lung Parenchyma; Segmentation methods; Thoracic CT Scans;

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I. INTRODUCTION

Globally, lung
cancer is the leading cause of cancer mortalities. There were 1.69 million
deaths in 2015 due to lung cancer 1. Early detection of lung cancer increases
the survival rate, but is like searching for needle in the haystack. Abnormal
small, round or oval shaped growth in the lung called lung nodules may be the
first sign of lung cancer and detection of those is very exhaustive given the
complex structure of lungs. Computed Tomography (CT) is an important diagnostic
modality to detect lung nodules. The automation of detection and diagnosis of
lung nodules benefits both the radiologists and patients. Accurate detection of
lung nodule at an early stage leads to proper treatment and saves patients
life. There are several computer aided diagnosis (CAD) systems developed over
the years to assist in the diagnosis of lung cancer. CAD system components
include lung segmentation, nodule detection and segmentation, false nodule
reduction, nodule classification. Each component is complicated in its own way.
The significance of segmentation of
the lungs from chest CT scans has been elaborated in 2. Lung segmentation is
a prerequisite for the subsequent automated analysis of lung nodules since it
allows for the estimation of lung volumes and detection and quantification of
abnormalities within the lungs. In case of erroneous lung segmentation, findings
might be missed or findings outside the lungs might be included in the analysis.
The importance of accurate lung segmentation for the automated detection of
nodules is illustrated in 5. Their experiments have showed that accuracy of
nodule detection has increased when lung segmentation has been applied. A naive
lung segmentation algorithm was applied to 60 scan, 17% of nodules were not
detected as a consequence of improper lung segmentation. Another lung
segmentation algorithm improved the results and only 5% of nodules were not
detected.

The task of lung
segmentation is challenging because of the complexity in the lung region and the
existence of similar density structures, such as arteries, veins, bronchi and
bronchioles, and the use of different scanning devices with different scanning protocols
3. In general, the existing techniques for lung segmentation can be classified
into different categories based on: a) Intensity. Eg. Thresholding. b) Region.
Eg. Region Growing. c) Shape. Eg. Sobel. d) Edge. Eg. Wavelet Transform. e) Machine Learning. Eg. SVM.

 

The
objective of this work is to apply  unet
segmentation network to extract lung fields from the CT scans.

 

 

                                                                                                                                                      
II. RELATED WORK

Automatic lung segmentation has always been a
challenging task. Several algorithms have been proposed that address the
problem of accurately extracting lung region from the CT images. Conventional
methods mostly rely on the attenuation values of different areas on the CT. The
fact that similar regions have same intensity helped the development of a
popular intensity based segmenation method namely thresholding. Tremendous
number of research papers has developed thresholding methods with many
variations. Traditional algorithms are two-dimensional and process each axial
section of the scan separately. 3D image processing of CT scans has been
developed that take into account height, width and depth of an image.

Most of the methods for lung segmentation begin by
determining the lung fields using optimal gray-level thresholding and connected
components or region growing method. The detected lung region contains trachea
and bronchi which has to be removed from the image.  When the lungs are joined in the anterior or
posterior junctions, they have to be separated to obtain the left and right
lung regions only. Post processing include applying of the morphological
operations on the segmented image to smoothen the borders and fill the gaps.
The literature of lung segmentation include numerous papers which are described
below:

Hu et al (2001) were the first to publish a threshold-based lung
segmentation method based on the method described above. Ukil and Reinhardt (2005) improved
upon the method proposed by Hu et al (2001) by introducing a smoothing at
the mediastinal area based on the airway tree to guarantee consistency among
segmentations of different subjects and intra-subject over time. Sluimer et al (2005) and van
Rikxoort et al (2009a) describe 3D threshold-based methods largely based on
the method of Hu et al (2001). Sun et al (2006) presented a 3D method for the
segmentation of the lungs from thick-slice CT images. First, a preprocessing
was applied in which the signal-to-noise ratio was improved by applying an
anisotropic filter, followed by a wavelet transform-based interpolation method

to construct 3D volume data. In these 3D volume data,
the lungs were obtained by region

growing using gray-value, homogeneity and gradient
magnitude as input. Cavities inside the

resulting lung region were filled using morphological
closing.

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