# Current Advances in Computational Lung Ultrasound Imaging: A Review

Tianqi Yang, Oktay Karakuş, Nantheera Anantrasirichai, Alin Achim Tianqi Yang,
Nantheera Anantrasirichai and Alin Achim are with the Visual Information
Laboratory, University of Bristol, Bristol BS1 5DD, U.K.Oktay Karakuş is in
the School of Computer Science and Informatics at Cardiff University. Cathays,
Cardiff, CF24 4AGe-mail<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

In the field of biomedical imaging, ultrasonography has become common
practice, and used as an important auxiliary diagnostic tool with unique
advantages, such as being non-ionising and often portable. This article
reviews the state of the art in medical ultrasound image processing and in
particular its applications in the examination of the lungs. First, we briefly
introduce the basis of lung ultrasound examination. We focus on (i) the
characteristics of lung ultrasonography, and (ii) its ability to detect a
variety of diseases through the identification of various artefacts exhibiting
on lung ultrasound images. We group medical ultrasound image computing methods
into two categories: (1) model-based methods, and (2) data-driven methods. We
particularly discuss inverse problem-based methods exploited in ultrasound
image despeckling, deconvolution, and line artefacts detection for the former,
whilst we exemplify various works based on deep/machine learning, which
exploit various network architectures through supervised, weakly supervised,
and unsupervised learning for the data-driven approaches.

###### Index Terms:

Lung Ultrasound, Ultrasound Imaging, Inverse Problems, Lung Artefacts, Machine
Learning.

##  I  Introduction

In recent years, with the development of ultrasound (US) along with the
advancement of medical imaging technologies, the examination and treatment
performance of US in clinical applications, especially in the diagnosis of
lung disease, has gradually been recognized and accepted by clinicians. This
advancement has not only changed disease treatment, prognosis and patient
management, but also became a visual stethoscope and auxiliary diagnosis tool
that radiologists can rely on. US examination has consequently become an
important medical imaging method, which is an effective supplement to other
medical imaging modalities such as chest X-ray, chest computed tomography
(CT), bronchoscopy, and magnetic resonance imaging (MRI).

Among the various clinical applications of US, lung US (LUS) has been
increasingly used as a support tool in not just lung but even nephrological
and rheumatological disease diagnoses, and in fluid status assessment [1]. It
is regarded as a prospective routine practice for bedside patient assessment
[2, 3]. On the one hand, this is because LUS is a fast, portable, radiation-
free, bedside and non-invasive imaging technique. On the other hand, LUS also
helps in assessing the fluid status of patients in intensive care as well as
in deciding management strategies for a range of conditions. Indeed,
respiratory disease is extremely common, amounting to more than 414.6 million
cases around the world [4]. Severe lung problems cause the death of more than
100 thousand people in the UK every year [5]. According to the British Lung
Foundation, somebody dies due to lung disease in the UK every five minutes
[5]. Lung disease is in fact the third common cause of death in the UK after
heart disease and cancer [5]. Following the start of the COVID-19 pandemic in
2020, analysis and diagnosis of lung disease has become even more crucial.
Rising demand has been spurred for timely and accurate diagnosis, as well as
patient monitoring [6].

Well known image quality issues affecting ultrasonography (speckle noise, low
resolution), together with the peculiarities and added difficulties of LUS
investigations (small acoustic window, energy dissipation in air) make the
tools of computational imaging ever more important in reconstructing
information of the highest quality in order to rip the benefits of LUS in a
variety of clinical applications. This paper aims to provide a comprehensive
review of the state-of-the-art computational LUS imaging approaches. On the
one hand, we will review model-based methods, which are reasonably well
studied in the relevant literature. On the other hand, with the current
advances in machine learning (ML) and artificial intelligence (AI), approaches
that learn representative models and features from a (usually high ) number of
training data will be paid attention in this review, especially deep learning
(DL) methods, because they have brought remarkable improvements to LUS image
analysis as well as to LUS image quality. Since the current state of LUS
development has not yet reached maturity, this review aims to provide
inspiration and references for the development of yet more accurate and
efficient computational LUS imaging approaches.

The remainder of this contribution is organised as follows: We describe the
basic features and clinical applications of LUS imagery in Section II. Section
III presents processing methods for the reconstruction of high quality
ultrasound images within the two sub-classes of model-based and data-driven
methods. Furthermore, Section IV reviews model-based as well as data-driven
approaches specifically developed for LUS image processing. The discussions in
Section V serve the purpose of revealing future research directions by
analysing current challenges, and Section VI gives a short conclusion of the
reviewed work.

##  II  Lung Ultrasound and its Clinical Applications

This section is intended to provide a brief overview of medical ultrasound
imaging technology, with a specific focus on LUS. We describe the devices that
clinicians normally use, the standards that should be followed, and the lung
artefacts that diagnosis is based on.

Figure 1: Example images for various types of Lung signs. [7] (a) Pleural line
(linear probe), (b) Bat sign (linear probe), (c) Seashore sign (linear probe,
B-mode on the left and M-mode on the right), (d) A-line (convex probe), (e)
B-line (convex probe), (f) Atelectasis (linear probe), (g) Bronchial inflation
(convex probe), (h) Lung point (convex probe, B-mode on the top and M-mode on
the bottom), (i) Pleural effusion (convex probe), (j) and (k) Curtain sign
(convex probe).

###  II-A Instrumentation

When sound waves are transferred into the body, they are transmitted until
they are completely attenuated, the echoes received from the different depths
can be envelop detected and displayed, and hence cross-sectional US images of
the human body can be formed [8].

A widely accepted linear model of US image formation was proposed in [9] based
on the physical propagation characteristics of sound waves. The received
signal can be expressed in the time domain as

$r(\textbf{r}_{0},t)=h(\mathbf{r},t)\circledast
f_{m}(\mathbf{r})|_{\mathbf{r}=\mathbf{r}_{0}}$ (1)

where $\mathbf{r}_{0}$ represents the surface of the receiver, $\mathbf{r}$
refers to the location of the imaged tissue, $f_{m}(\mathbf{r})$ is the tissue
reflectively function (TRF), and $h(\mathbf{r},t)$ refers to the system point
spread function (PSF).

Probes are the actual ultrasonic transducers, which employ the piezoelectric
effect to perform the bidirectional conversion of mechanical waves into
electrical signals and vice versa. A non-exhaustive list of most commonly used
clinical probes is given in Table I. In LUS imaging, for the observation of
in-depth lesions, low-frequency convex array probes or low-frequency phased
array probes are usually utilised, whereas high-frequency linear array probes
are used for superficial pleural or sub-pleural detection. Image resolution
increases with frequency, at the cost of penetration depth [10]. When the
density of the lung changes, artefacts which are used to distinguish sick and
normal lungs can be detected, since some diseases can lead to an increase of
the liquid content. Specifically, in a normal lung, the ultrasound beams
disperse and become less organised as the waves travel within the alveoli that
are full of air. This leads to fewer acoustic beams being reflected back to
the probe, and therefore the region under the pleural line appears simply as a
uniform background pattern finely sparkling with some linear echogenic
horizontal lines [11]. When the air content decreases and lung density
increases due to the presence in the lung of transudate, the acoustic mismatch
between the lung and the surrounding tissues is lowered, and the ultrasound
beam can be partly reflected at deeper zones and repeatedly [12]. This
phenomenon creates discrete vertical hyperechoic reverberation artefacts. This
provides clinicians the possibility to not only assess and evaluate conditions
that are peculiar to the lungs but determine a fluid overload in the body.

Depending on clinical applications, working principles, tasks and operating
systems, LUS images can be acquired in different modes, mostly in M-mode and
B-mode [13]. An M-mode recording is conventionally displayed with the abscissa
representing time, and the ordinate showing distance from the transducer, the
latter derived from the time delay from echo emission to reflection and
detection [14]. Since it can record rapid motions, it is usually used to
detect the presence or absence of lung sliding. B-mode uses the amplitude of
the echo pulse to modulate the brightness of the display. The abscissa and
ordinate of the display correspond to the position of the sound velocity scan,
thus forming an ultrasonic cross-sectional image modulated by the brightness
[15]. It is a good choice for monitoring the existence of B-lines.

A number of vendors have developed LUS scanners, including the Canon Xario
Series111http://www.kangdamed.net/en/index.php/Xarioxilie/159.html, Aplio
Series (by GE)222http://www.kangdamed.net/en/index.php/Aplio/154.html and
Sonosite333https://www.sonosite.com/products. In order to satisfy eventual
point-of-care (PoC) needs, many of these vendors have been developing portable
devices, such as Clarius and Butterfly iQ+, whose apps support high-resolution
real-time lung monitoring under the choice of various modes.

###  II-B LUS Standardization

The imaging devices and the management of the patients varies in different
countries. Hence, to promote an effective global medical exchange, a
standardization for the international protocol of LUS is needed. Soldati et.
al. [16] had developed a standardized approach for the use of equipment and
the acquisition protocol, specifically for COVID-19 patients. A scoring system
for severity classification was also proposed. An artificial intelligence
system was developed to support the generation of standardized datasets [6].

Another standardisation effort was made by Allinovi and Hayes [17] who
demonstrated that a simplified 8-site B-lines score holds an almost identical
predictive power to a 28-site score in quantifying the extent of extravascular
lung water in children. The use of 8-site lung ultrasound assessments for
children on dialysis was therefore proposed, to accurately quantify fluid
overload in children.

###  II-C Lung Signs and Conditions

The lungs are the main air-containing organs of the human body. In a
pathological condition, the air-liquid ratio in the lungs changes, and
abnormal artifacts may appear during a US examination (see examples in Figures
1). The analysis of LUS images is based on these signs.

The common features in all clinical conditions, both local to the lungs (e.g.
pneumonia, chronic obstructive pulmonary disease (COPD)) and those manifesting
themselves in the lungs (e.g. kidney disease, COVID-19) are the presence in
LUS of two types of artefacts, known as A- and B-lines. These may carry
important information about the severity of diseases. Hence, the majority of
research conducted in this area starts from detecting and quantifying these
linear features in LUS images. The A-lines (horizontal linear structures) are
a kind of artifact caused by multiple reflection of sound waves due to the
difference of acoustic impedance between the pleura and the lung. Their
presence are indicative of a healthy lung, whereas the B-lines, appearing as a
vertical comet-tail artifact arising from the pleural line, relate to
decreased lung aeration [18] and can be indicative of multiple diseases [19,
20, 17]. Therefore, detecting B-lines has become common practice when
evaluating various diseases with LUS [21]. The position of an artifact
relative to the pleural line (which is the horizontal echo reflection formed
by the surface of visceral and parietal pleura) is also important in
determining whether the detected artifact is a B-line or merely air or another
organ [22]. Therefore, in some studies, the pleural line is used as reference
for the positioning of other line artefacts [23, 24].

Indirect signs in LUS can reveal the severity of lung disease. In Table III,
we present a non-exhaustive list of diseases, observable with LUS, along with
their peculiarities. Apart from the listed conditions, LUS plays important
role in intensive care units (ICU) conditions. It can effectively detect lung
consolidations with a thickness greater than 20 mm in ICU patients [25],
evaluate the respiratory system and circulatory system in real time. LUS is
suitable for detecting neonatal respiratory distress syndrome (NRDS), meconium
aspiration syndrome, acute pneumothorax and occult atelectasis as well. It is
also worth noting that LUS signs have also been shown to become a good
indicator of extravascular lung water [26], and fluid overload in infants and
children on dialysis when compared to other measures [27, 1].

TABLE I: Ultrasound probes[28] Probe |  Applications
---|---
The curvilinear transducer |  This is the most commonly used probe for lung ultrasound, operating at relatively low frequency (1 ~ 5MHz). It has good penetration and enables examination of a larger field of the pleural surface.
The linear transducer |  This transducer operates at a higher frequency, it is good at interrogating a pleural surface with a maximal depth of 4cm or so. One selects this probe when wanting the highest resolution examination of an abnormality at the pleural surface.
The phased array transducer |  This is a relatively low frequency phased array transducer (1 ~ 3MHz), which becomes a reasonable selection for lung ultrasound where gross and diffuse pathology is suspected. This would include pulmonary oedema and large pleural effusion or pneumothorax detection.
TABLE II: Lung signs Sign |  Details
---|---
Pleural line and pleural sliding |  The horizontal linear high echo below the ribs constitutes the pleural line. The relative motion of the parietal pleura and the visceral pleura forms a reciprocating movement with breathing.
A-line |  In B-mode, A-lines are a series of hyperechoic lines that are equally spaced and parallel to the pleural line and occur below the pleural line.
Quadrilateral sign and sine wave sign |  Below the pleural line, a line regular and roughly parallel to the pleural line (the lung line), together with the pleural line and the shadow of the ribs, display a quad sign. In M-mode, the movement of the lung line is sinusoidal [25].
B-line |  A vertical artifact with clear boundaries and moving synchronously with lung sliding.
Lung consolidation and atelectasis |  The echogenic US appearance of the lungs is similar to that of the liver or spleen.
Bronchial inflation sign |  A dividing point seen in real-time ultrasound where the lung sliding exists and disappears alternately with breathing movement.
Lung points |  A dividing point seen in real-time ultrasound where the lung sliding exists and disappears alternately with breathing movement.
Curtain sign |  As air-filled lung moves up and down with the breathing motion and other abdominal organs are covered periodically.
TABLE III: Lung conditions Condition |  Details
---|---
Pneumothorax |  The disappearance of the pulmonary sliding sign in the initial stage and the M-mode LUS shows a superposition of parallel lines that lack motion characteristics [25].
Pneumonia and COVID-19 |  Varying degrees of lung consolidation and abnormal pleural lines, some with fusion of B-lines and pleural effusion. Early stage of COVID-19 pneumonia shows single and/or confluent vertical artifacts [6, 29, 30]. Further evolution of COVID-19 pneumonia shows evident consolidations and widespread patchy artifactual changes [31]. Moreover, ultrasound elastography, a technique developed to measure elastic properties of superficial lung tissue, has shown ability to evaluate lung disease and potential for COVID-19 pneumonia diagnosis [32].
Chronic obstructive pulmonary disease(COPD) |  Pulmonary A-line, pulmonary sliding sign and no right ventricular overload [7].
Acute respiratory distress syndrome (ARDS) |  Lung consolidation with bronchial inflation sign, abnormal pleural line, diffuse pulmonary edema, and disappearance of A-lines. extra-vascular lung water (EVLW) is another indicator to classify the severity of ARDS [33].
Lung cancer |  US can clearly captures the chest wall, pleura and peripheral lung lesions, and can show the morphology, boundary and blood flow of the lesions and the anatomical relationship between fine structures and the surrounding tissue, which provides basic information for clinical diagnosis [7]. Through their US morphologies, one can distinguish benign and malignant lung tumors.
Congenital diaphragmatic hernia (CDH) |  i. partial absence of the hyperechoic line representing the normal diaphragmatic profile, ii. partial absence of the pleural line in the affected hemithorax, iii. absence of A lines in the affected area, iv. presence of multi-layered area with hyperechoic contents in motion (normal gut), and v. possible presence of parenchymatous organs inside the thorax (i.e., liver or spleen).

##  III  LUS Image Enhancement and Reconstruction

In order to accurately identify the various signs of disease in LUS images,
the reconstruction of high quality images through observations and the
extraction of information therein are necessary. Typically, this process
involves solving an inverse problem whereby a set of unknown deterministic
parameters which are observed through a linear transformation and corrupted by
noise are estimated [34]. The solution however may not be unique, or it may be
highly sensitive to changes in the data, which illustrates the ill-posedness
of the problem. In the following, we discuss the most widely studied inverse
problems in conjunction with medical US image analysis, which are also
relevant to LUS. Approaches peculiar to LUS specifically are discussed in
Section IV.

###  III-A Model-Based Methods

####  III-A1 Despeckling

Generally, the final envelope detected LUS image is composed of two elements,
the useful signal component (corresponding to structure inside the human body)
and the noise component (comprising multiplicative speckle and additive
measurement noise).

$f(x,y)=g(x,y)n(x,y)+w(x,y),\quad(x,y)\in Z^{2}$ (2)

where $g(x,y)$ and $f(x,y)$ represent the speckle free and the observed
signals, respectively. $n(x,y)$ and $w(x,y)$ represent the multiplicative and
additive noise components, respectively. $(x,y)$ are the two-dimensional
spatial coordinates. Since the influence of the additive noise is far less
obvious than that of the multiplicative noise [35], the image formation model
(2) can usually be approximated by

$f(x,y)=g(x,y)n(x,y),\quad\text{where }(x,y)\in Z^{2}.$ (3)

The term $n(x,y)$ corresponds to speckle noise, which is an inherent
phenomenon in LUS images. The early work described speckle noise
characteristics in the wavelet domain with a heavy-tailed $\alpha$-stable
distributions [35].

Speckle noise has been shown to be correlated with the tissue structure [35],
so its statistical description generally depends on the type of tissue and the
imaging system. It exhibits granular patterns, which obscure fine anatomical
details, and thereby reduce the diagnostic accuracy. Speckle noise is hence
normally regarded as an undesirable phenomenon in most clinical applications.
However, sometimes speckle noise can also constitute useful information, such
as when used for speckle tracking (i.e. motion estimation) and lung tissue
characterization [8, 36, 37]. To mitigate speckle noise, a logarithmic
transformation is usually employed to convert the multiplicative
characteristics into an additive model.

Recently, Choi and Jeong [38] have improved descpeckling performance by
combining various approaches, including speckle reduction anisotropic
diffusion (SRAD) filter[39], discrete wavelet transform (DWT) using symmetric
characteristics, gradient domain guided image filtering (GDGIF) and weighted
guided image filtering (WGIF). Compared with previous denoising methods, their
algorithm showed superior despeckling performance, better feature information
conservation, and lower computational cost. Another stat-of-the-art technique
proposed by Chen et. al. [40] introduced the alternating direction multiplier
(ADMM) algorithm [41] to optimize the denoising effect of the new speckle
noise recovery model based on adaptive variational method. This computational
framework for solving optimization problems is shown to be suitable for
solving distributed convex optimization problems, and therefore is suitable
for LUS image denoising tasks.

####  III-A2 Deconvolution

Another important post-acquisition operation often needed in ultrasonography
is image deconvolution, whereby US images are modelled as a convolution
between a blurring kernel or point spread function (PSF), and the tissue
reflectivity function [42, 43, 44]. The linear image formation model can be
rewritten as

$y(r)=h(r)\circledast x(r)+n(r),\quad r\in R,$ (4)

where $y(r)$ is the image pixel observed at position $r$, $x(r)$ is the TRF to
be estimated, $h(r)$ is the system PSF, $n(r)$ is the additive measurement
noise. $R$ refers to the image domain [8].

Deconvolution in LUS imaging is a tool to improve visual quality and achieve
better contrast. This translates in easier interpretation for the physicians.
The most common strategies for medical US image restoration is represented by
MAP-based deconvolution. It tackles the problem using a two-step scheme: the
PSF is estimated first and subsequently image restoration is performed. The
main advantages of these strategies lie in (i) two dimensional or even three
dimensional PSFs can be accounted for [45, 46], (ii) no assumption is made on
the PSF, neither on the number of zeros or poles, and nor on the position of
the zeros in the complex plane, and (iii) more advanced models than white
Gaussian can be assumed for the tissue reflectivity [47, 48]. Although
commonly employed, MAP deconvolution brings an increased computational cost,
even if simple schemes are employed such as based on Wiener filtering or
$\ell_{1}$-norm optimization. Another problem is that the PSF estimation is
still tedious. In particular this is due to the need for phase unwrapping
procedures where non-minimum phase PSFs are considered.

Due to the inherent bandwidth limitations of US scanners and the adverse
effects of measurement noise, LUS image deconvolution is very sensitive to
errors occurring in the PSF estimation. Even slight errors in the PSF
estimates can lead to obvious artefacts that render the reconstructed images
worthless. The method of Michailovich et. al. [49] might be a possible
solution to address this issue. They proposed a “hybrid” deconvolution
technique, which was based only on partial information about the PSF,
especially its power spectrum to estimate tissue reflectivity. While directly
estimating the reflectance of the tissue from the relevant radio-frequency
(RF) data, the proposed approach simultaneously eliminated errors caused by
inaccuracies in PSF estimation.

The method proposed by Pham et. al. [50] even overcame the main limitation of
the former related to the requirement for PSF estimation. This algorithm for
dealing with a sequence of fast-changing US images was based on the
combination of two different techniques: deconvolution robust principal
component analysis (DRPCA) and blind deconvolution (BD). It provided similar
performances as previous work, but the PSF was assumed to be spatial-
temporally invariant, and the algorithm appeared to be computational complex.

###  III-B Data-Driven Approaches

####  III-B1 Despeckling

Deep learning methods are able to play a compelling role in improving the
quality of US images, in terms of speckle mitigation in particular. Such data-
driven systems can indeed be leveraged across the US imaging domain [51],
including LUS.

The work in [52] proved the applicability of CNNs as a method that can quickly
and accurately perform image restoration. A multi-resolution fully
convolutional neural network (FCN) was used to approximate an ultrasound image
of “CT quality”. This end-to-end framework effectively improved the image
resolution and contrast while preserving all the relevant anatomical and
pathological information. It offered low complexity, making it applicable in
real-time settings. However, in practice, CT-US paired data is difficult to
obtain, so the US data used for training were simulated. In real applications,
the shortage of such multimodal data pairs can reduce the applicability of the
method.

The method proposed by Feng et. al. [53] was shown to retain all relevant
anatomical and pathological information in the restored images. They
introduced US-Net by enhancing the CNN structure detail and proposed a new
hybrid loss function designed for speckle noise removal.

####  III-B2 Deconvolution

Deep neural networks have also found applications in ultrasound image
restoration. Perdios et. al. [54] made it possible to apply stacked denoising
autoencoders (SDA) to the recovery of LUS images. They explored both a linear
measurement case where a known Gaussian random matrix was used as the
measurement matrix (SDA–CNL) and a non-linear measurement case where the
weight matrices and bias vectors were learned (SDA–CL). It was shown that a
4-layer SDA–CL outperforms a state-of-the-art compressed sensing algorithm
without the need to tune any hyper-parameter. While increasing the quality of
the reconstructed image is the main objective, reducing the calculation time
is also an important requirement in applications. Yoon and Ye [55] proposed a
novel DL approach that interpolates the missing RF data by utilizing the
sparsity of the RF data in the Fourier domain. The algorithm effectively
reduced the data rate without sacrificing the image quality and is also
applicable to LUS images. The CNN can be trained using a RF data measured by
the linear array transducer for a particular organ, and can be extended for
other types of transducers and/or different body parts.

###  III-C Hybrid Techniques

The key to image reconstruction is to find a good sparse representation of the
image to be reconstructed (which is also true for model-based approaches). DL
methods assume that the representation is provided by deep neural networks, so
it is nonlinear. The model parameters of the representation must be learned
from a large amount of data. Hybrid approaches were therefore proposed. In
hybrid approaches, neural networks are normally used to learn the prior of the
observed data such that the size of the required dataset can be reduced.

There are obvious theoretical connections between DL networks and traditional
iterative algorithms. Jin et. al. [56] explored the relationship between CNNs
and iterative optimization methods for inverse problems where the normal
operator associated with the forward model is a convolution. The proposed
method, which is called FBPConvNet, combined filtered back projection (FBP)
with a multiresolution CNN. The direct inversion realized by FBP encapsulated
the physical model of the system, but led to artifacts when the problem is
ill-posed; the CNN combined multiresolution decomposition and residual
learning in order to learn to remove these artifacts while preserving image
structure. The structure of the CNN was based on U-Net [57], with the addition
of residual learning. This approach was motivated by the convolutional
structure of inverse problems in biomedical imaging, including LUS.

Chang et. al. [58] proposed a general framework to train a single deep neural
network that solves arbitrary linear inverse problems. They observed that in
optimization algorithms for linear inverse problems, signal priors usually
appear in the form of proximal operators. Thus, the proposed network acted as
a proximal operator for an optimization algorithm and projected similar image
signals onto the set of natural images defined by the decision boundary of a
classifier. The learned projection operator combined the high flexibility of
deep neural nets with the wide applicability of traditional signal priors.
This has the potential to lower significantly the costs involved in the design
of specialized hardware, where LUS is a clear potential beneficiary.

Similar to other US images, LUS images exhibit speckle and require pre-
processing. With virtually no modifications, the aforementioned despeckling
and deconvolution techniques can be adopted specifically for LUS imaging, and
therefore contribute to better feature extraction in high resolution LUS
images.

##  IV Computational LUS Image Analysis

This section presents the latest advances in computational imaging and image
analysis methods utilising LUS. The LUS image processing methods are grouped
into two major categories, specifically into the long developed model-based
approaches and the data-driven (or learning-based) methods. In subsection
IV-A, we talk about LUS line artefacts identification in a model-based way.
Subsection IV-B reviews data-driven approaches that have been developed in
clinical LUS interpretation including artefact localization, segmentation and
so on.

###  IV-A Model-based Methods

Most tasks of LUS analysis are related to the identification of line
artefacts. An added benefit of model-based approaches is that they constitute
an unsupervised framework for the labelling of LUS images (e.g. B-lines) in
cases where annotated data are not available for machine learning.

To mask line artefacts on LUS images, the authors in [21] generated a
normalized gray scale map that can be used for delineation of different
structures in the images. They used a random walk method to delineate the
pleural line, and then excluded the upper pleural region before identifying
B-lines. This was achieved by an alternate sequential filtration, and
subsequently applying the top-hat filter to ensure that B-lines are laterally
detached. Finally, a Gaussian model was fitted to each detected B-line, and
the peak point of the fitted Gaussian models corresponding to the B-lines are
calculated and used to accurately determine the position of B-lines. B-lines
were then overlaid on the B-mode images. Similarly, to implemented automatic
pleural line detection, the authors of [23] masked a LUS image with
expectation maximization (EM) based thresholding, after which hidden Markov
model (HMM) and Viterbi algorithm (VA) were used to highlight the pleural
line.

Line detection in LUS can also be posed as an inverse problem, whereby the
line information (position and orientation) is estimated from the
observations. The solution however may not be unique, or it may be highly
sensitive to variations in the data, which illustrates the ill-posedness of
the problem. Regularization with specific penalty functions (or by employing
specific priors if the problem is addressed in a Bayesian framework), is then
necessary to solve such problems. Normally, the regularization term is related
to the prior information of the parameter to be estimated [8]. In the
literature, there are three main categories of such approaches, which include
(i) statistical methods, (ii) regularized geometric modeling methods, and
(iii) methods based on sparse representations [59] or, sometimes, a
combination thereof. In the following, we discuss the most widely studied
inverse problems in conjunction with LUS image analysis.

In the very first work adopting an inverse problem formulation,
Anantrasirichai et. al. [60] proposed an innovative way of detecting line
artefacts in LUS images by employing the Radon transform, converting an image
into a space of lines, and solving the following optimization problem:

$\displaystyle\hat{x}=\arg\min_{\hat{x}}\left\\{\frac{1}{2}\|y-\mathcal{R}^{-1}x\|_{2}^{2}+\alpha\|x\|+\beta\|\bigtriangledown\mathcal{R}^{-1}x\|\right\\},$
(5)

where $\mathcal{R}$ represents Radon transform, $\alpha$ and $\beta$ are
regularisation constants and $\hat{x}$ is the set of detected lines. The ADMM
algorithm was used to solve this optimisation problem, offering a fast
convergence rate. The scheme firstly detected the pleural line in order to
locate the lung space. Then, the local peaks of the Radon transform were
detected and line-type classification was done following clinical definitions,
in the spatial image domain. B-lines, A-lines and Z-lines were hence
successfully identified. In [61], the authors further extended the method by
combining the Radon transform with the PSF of the US acquisition system in a
single equation thereby achieving line detection and deconvolution
simultaneously. To enhance line detection performance and the visualization of
restored lines, they included an additional convolution factor in the Radon
transform domain with an unknown blurring kernel. The penalty function
employed was the $\ell_{p}$-norm with norm order values of $(0\leq p\leq 1)$
in order to promote sparsity in the Radon space.

Extending the above work in the context of evaluating COVID-19 patients,
Karakus et. al. [62] improved the line detection performance by regularizing
the solution using the Cauchy proximal splitting (CPS) algorithm [63] to
promote statistical sparsity by utilising the Cauchy-based penalty function.

Figure 2: B-line detection results of Moshavegh et. al. [21]. (a) LUS scan
containing two B-lines. (b) Pleural line is outlined and two B-lines are
detected in (a) and overlaid. Figure 3: Segmentation algorithm steps of
Brusasco et. al. [64]. Upper left: sub-pleural selection from the original
ultrasound scan. Upper right: K-means classification to divide pixels into two
subsets. Bottom left: B-line detection. Bottom right: alternated sequential
filter consisting in iterative morphological openings and closings. Figure 4:
Pleural line (black dotted line) detection result of Leonardo et. al.[23].
Figure 5: Line artefacts in lung ultrasound images Red, yellow, blue and green
lines represent the pleural lines, B-lines, A-lines and Z-lines, respectively.
[61]. Figure 6: Line detection results of Karakus et. al. [62]. Left: original
B-mode ultrasound image. Right: detected lines.

###  IV-B Data-Driven Approaches

Compared to conventional model-based approaches, ML based methods can provide
improved performance in medical image analysis tasks, since they are able to
capture more complex patterns in the data. ML approaches are often driven by
patterns contained in the data, which are generally required for training and
testing purposes. Therefore, the dataset used for ML is of great importance.
Moreover, in a study on automated segmentation and scoring of COVID-19 LUS
images, Roshankhah et. al. [65] also pointed out that the splitting strategy
of the training and test datasets can influence the performance of the CNN.

In the sequel, we summarise the data-driven methods and split them into three
main classes, fully-supervised, weakly-supervised and transfer learning.

####  IV-B1 Fully-supervised Learning

Various studies in the literature have shown that the data-driven algorithms
are effective tools for medical ultrasound image analysis, and they have
already started to make an impact in LUS image analysis. As a first example,
the work in [24] implemented a fully-supervised method, where a total of 1450
pneumonia and 1605 normal lung images were analyzed with a three-layer feed-
forward neural network. The method combined image processing and vector
classifiers to identify consolidations of pediatric pneumonia. After the
identification of the pleural line and the removal of skin/soft tissue,
feature extraction was carried out based on the analysis of brightness
distribution patterns presented in rectangular segments from the images. In
the end, the method was able to correctly identify pneumonia infiltrates with
a sensitivity of 90.9% and a specificity of 100%.

Later, Wang et. al.[26] used a generic CNN structure to perform real-time lung
B-line quantification to diagnose and quantify pulmonary edema. The training
was implemented on a small dataset composed of 4864 clinical LUS images. The
approach used (i) data augmentation to increase the variety of the data, (ii)
dropout to avoid over-fitting, and (iii) rectified linear units (ReLU) to
realize a faster training speed. Despite an apparently low 43.4% absolute
accuracy in the test set, the intra-class correlation of 0.791 indicates
substantial agreement of the neural network algorithm with the human-
identified B-line counts. The B-line identification and clinical
classification time was around 0.1s, nearly reaching real-time processing.

Baloescu et. al. [66] proposed to use a relatively shallow custom-made network
(CNN) architecture with 3-D filters (called 3D-CsNet) for the purpose of
automatically distinguishing between the presence and absence of B-lines as
well as assessing B-line severity. The backbone of 3D-CsNet is a CNN
consisting of eight intermediate layers followed by two fully connected layer.
As it alleviates the need for pre-training, the proposed algorithm is fast,
employing few trainable parameters, and it is flexible and easy to deploy.
However, this method has a weaker performance on multiclass severity rating
(0.65 kappa) compared to its binary rating (0.88 kappa), because a higher
number of categories may result in lower agreement when it comes to DL
performance for the same amount of data.

To improve the diagnosis efficiency, the approach introduced by Kulhare et.
al. in [67] automatically segmented LUS features in simulated animal models
with six single-class single shot detection (SSD) neural nets, making it
suitable for on-device inference tasks. However, the success of application to
human LUS images has not been investigated.

Motivated by the COVID-19 pandemic, researchers have also been
enthusiastically investigating various solutions to the application of the
COVID diagnosis. McDermott et. al. [68] and Soldati et. al. [31] have
illustrated the applicability of LUS imaging to COVID-19 symptoms detection.
By comparing LUS protocols and image features, the former work suggested the
possibility of aiding in interpreting LUS images autonomously or semi-
autonomously, and the latter clarified the urgent need of the diagnostic and
prognostic role of LUS in COVID-19. One of the supervised method examples is
the quantitative and automatic LUS scoring system developed by Chen et. al.
[69], using multi-layer fully connected neural networks (FNNs) for evaluating
COVID-19 pneumonia. The authors introduced a curve-to-linear conversion
process, thereby reducing the feature extraction problem from two dimensions
to one dimension. One and two-layer FNNs with hidden nodes were used to learn
the data features. ADAM optimizer was applied to fit the data with ReLu as the
activation function. With all the settings, the neural network with
128$\times$256 neurons gave the highest accuracy of 87%.

####  IV-B2 Weakly-supervised Learning

Even though the aforementioned approaches have brought improvements in LUS
image interpretation, it cannot be denied that they require a high amount of
labelled LUS images for training. This limitation has motivated researchers to
develop weakly-supervised learning algorithms, where smaller amount of
training samples are required. Van Sloun and Demi [70] explored weakly-
supervised DL techniques, requiring only a single label per frame for
training, and exploited gradient-weighted class activation mapping (grad-CAM)
[71] to perform B-line localization. The network has been applied and tested
both in-vitro and in-vivo. Notably, the proposed method can also serve as a
region-of-interest selector for further quantitative analysis of segmentation
in the LUS data that contain the most relevant information.

Different from previous work, which relies on attention and CAMs to find the
region in the image where the lesion is, the method proposed by Kerdegari et.
al. [72] can localize B-line regions in LUS videos both spatially and
temporally. They trained a RNN in a weakly-supervised manner. They leveraged
temporal analysis networks, and used spatiotemporal attention to find the most
important frames (i.e., B-line frames) and localized B-line regions within a
video. Their proposed attention model achieved the highest F1 score of 83.2%
when benchmarked against previous techniques.

Subhankar et. al. [73] presented an extended and fully-annotated version of
the ICLUS-DB database444https://iclus-web.bluetensor.ai.. They presented a
deep network, derived from spatial transformer networks (STN) [74]. Two
transformed versions of the input image are introduced to localize
pathological artifacts. Then a feature extractor generates the final
prediction. The network simultaneously predicts the disease severity score
associated to an input frame and provides localization of pathological
artefacts in a weakly-supervised way by inferring it from simple frame-based
classification labels. In [73], a method based on uninorms was used to
effectively aggregate the frame score at video level. An F1-score prediction
of 65.1 an was reported when performing the prediction at frame level. For
video-based score prediction, because of the low inter-annotator agreement and
the small number of video-level samples annotation, an F1-score of 61%, a
precision of 70% and a recall of 60% were reported. When evaluating
segmentation results, the pixel-wise accuracy reaches 96% and a binary Dice
score is 0.75. Tsai et. al. [75] further extended the idea of STN to
Regularised Spatial Transformer Network (Reg-STN) via implementing a video-
based (weakly supervised) and a frame-based (supervised) labelling approach.
The video-based labelling approach reaches 91.12% accuracy for 10-fold cross
validation, and the frame-based reaches 92.38%. This was the first attempt at
full automation of LUS evaluation of lung pathology, and thus proposed an
evaluation tool specifically for COVID-19.

Inspired by the idea of support vector machines (SVM), which usually does not
require a large number of training data, Veeramani and Muthusamy [76] proposed
a method called relevance vector machines (RVM) to detect abnormalities in LUS
images. Specifically, through pre-processing by an adaptive median filter and
feature extraction, a complete local binary pattern mask was generated. Then,
the binary RVM decides whether the given input LUS image corresponds to a
healthy or unhealthy subject. If abnormal, the images will then be classified
by the multi-level RVM into one of the lung diseases considered. This method
finally leads to a classification accuracy of above 90% and a 100%
specificity.

####  IV-B3 Transfer Learning

Transfer learning (TL) in medical tasks helps deep learning models achieve
better performances when there are scarce medical images, and can also be
defined as a solution to reducing training time. Horry et. al. [77] compared
the performance of 8 widespread network structures, which are amenable to
transfer learning for COVID-19 inspection with 3 modalities, including X-ray,
CT and LUS. The results showed that their DL models are suitable for
performing contrast enhancement on LUS images. The VGG19 outperforms all other
models. The work in [78] used two pre-trained V-Unet and X-Unet models on 200
and 400 images respectively. In the former structure, the last three fully
connected layers of a typical symmetrical U-Net structure were excluded and
new layers for the expanding path were added, so that the VGG16 can be
mimicked. The expanding path works reversely to the VGG16, with up-sampling
through deconvolution by transposing convolutional layers. Then the V-Unet was
pre-trained using ImageNet. The X-Unet is constructed by a U-Net pre-trained
with grayscale salient object images. Almeida et. al. [79] proposed the use of
MobileNets (light-weight neural networks) to support the diagnosis of
COVID-19. MobileNet was also pretrained on ImageNet, substituting the last
layer for a fully connected layer with a ReLu activation and the output layer
with a softmax activation. Multitask learning was achieved by training
different MobileNet models for each pathological indication. Even with limited
training data, the MobileNet achieved accuracy values above 95% for all
pneumonia indications.

Compared with pre-training on real-life objects, for example ImageNet, the
pre-training on LUS images may help detecting specific lung patterns such as
B-Lines. Born et. al. [80] published the first dataset of lung POCUS
recordings of COVID-19, pneumonia, and healthy patients. The collected data is
heterogeneous but was pre-processed manually to remove artifacts and checked
by a medical doctor for quality. They also proposed a model based on a pre-
trained CNN (POCOVID-Net) on the available data and evaluated using 5-fold
cross validation. The POCOVID-NET contains a convolutional part in the form of
VGG16 followed by one hidden layer with ReLu activation, and batch
normalization followed by the output layer with SoftMax activation. The
results reveal the efficiency in detecting COVID-19 by reaching a sensitivity
of 0.96, a specificity of 0.79, and F1-score of 0.92.

Xue et. al. [81] introduced the idea of contrastive learning into severity
assessment through LUS images, which falls under the transfer learning
umbrella. The framework includes two modules: (i) a dual-level supervised
multiple instance learning module (DSA-MIL) that leverages supervision from
both the LUS zone scores and the patient severity of pneumonia; and (ii) a
modality alignment contrastive learning module (MA-CLR) that combines
representations of LUS and clinical information without dropping
discriminative features. A staged representation transfer (SRT) strategy was
introduced to train the nonlinear mapping, leveraging the semantic and
discriminative information from the training data, providing an accuracy of
75% for 4-way severity assessment and 87.5% for two-way classification.

Panicker et. al. [82] proposed an U-net based network structure called LUSNet
and trained it in a similar way to contrastive learning. Fused images were
used to train the LUSNet to learn the pleural region of interest in an
unsupervised manner, and then the LUSNet was trained in a supervised manner to
classify the images into five classes of increasing severity of infection.
Images used for unsupervised training were fused with gray scale images by
exploiting acoustic propagation features.This technique has the potential to
improve the performance of the neural network when the input dataset is
limited and diverse. Experiments on over 5000 frames of COVID-19 videos showed
an average five-fold cross-validation accuracy, sensitivity, and specificity
of 97%, 93%, and 98% respectively.

Figure 7: Examples of vectors in specific regions used to compute brightness profiles: healthy (green), rib-bone (blue), and pneumonia (red). [24]. Figure 8: Sample results for SSD detection models results of Kulhare et. al. [67]. (a) B-line, (b) pleural line, (c) A-line, (d) pleural effusion, (e) consolidation, (f) merged B-line. Figure 9: B-line localization results in the work of [70]. Top: clinical B-mode input data. Bottom: corresponding class activation maps (CAM) for frames containing multiple B-lines. Figure 10: Four examples of B-mode input image frames (first column), their annotations (second column), semantic segmentations (third column) and contours of COVID-19 markers by deep learning (forth column) [73]. Figure 11: Comparison of two examples from the LUS data between the original mask, predicted masks from V-Unet TL, V-Unet FT, X-Unet FT (All layers), and X-Unet FT (BBFrozen). Yellow and green boxes indicate the defects from V-Unet and XUnet respectively [78]. Figure 12: Segmentation examples for a representative frame from five lung zones with different severity. Upper: ground truth, Lower: prediction, Cyan: pleural line, Green: A-line, Brown: B-line, Purple: consolidation [81]. TABLE IV: Computational Techniques for LUS Image Analysis Category |  Approaches |  Contributions |  Limitations
---|---|---|---
Model-based |  Alternate sequential filtering and top-hat filtering [21] |  Successfully differentiate between the healthy and unhealthy class with a p-value of 0.015. Average number of B-lines of patients and healthy group are 0.28$\pm$0.06 and 0.03$\pm$0.06 respectively. |  Lack the evaluation on larger dataset with different degrees of edema; Non real-time; No quantification considered.
Simple linear regression (SLR) and robust linear regression (RLR) [64] |  Provides a reliable operator-independent assessment of EVLW in ARDS, which is comparable or superior to previous scores. |  Not suitable for the case when multiple B-lines coalesce into a single white line.
Hidden Markov model, Viterbi algorithm [23] |  Average overall accuracy in detecting the pleura is 84% and 94% for convex and linear probes, respectively. |  -
Radon transform based inverse problem solved by ADMM [60, 61] |  Improves the performance of B-line detection approaches by up to 50%. |  Non real-time.
Radon transform based non-convex optimization with Cauchy-based penalty function [62] |  Up to 88% detection accuracy on 12-segment LUS for 9 COVID-19 patients. |  Non real-time.
Data-driven |  Characteristic vector classifiers [24] |  90.9% Sensitivity and 100% specificity for pneumonia infiltrates identification. |  Only severe and radiographically evident cases are analyzed; The data only come from a single patient; The algorithm requires validation for individual ultra-sonographers.
A basis CNN with data augmentation and dropout to avoid over-fitting [26] |  Requires littele computing power; Low number of parameters; Nearly real-time; Adaptive to other clinical tasks. |  Lack comet-rich samples; Labeled by a single observer; Manually count the number of B-lines.
3D-CsNet [66] |  A small number of trainable parameters; Effective in poorer quality datasets. |  Deficient categories of the training data leads to a weaker performance on multiclass severity rating compared to binary rating.
Single shot neural net [67] |  Light-weight network structure makes it suitable for on-device inference tasks. |  The application to human LUS images has not been investigated.
multilayer FNNs [69] |  The work is not dependent on arbitrary or human-decided thresholds and is capable of getting robust classification result based on limited training data. |  The results are evaluated on visual determination of the B-score and no artefacts are considered other than B-lines.
CNN for B-line detection and weakly-supervised localization through CAM [70] |  Nearly real-time; the algorithm can also serve as a region-of-interest selector for further quantitative analysis of segments. |  The characterization of adequate phenotyping of various lung pathologies is needed.
RNN [72] |  Detect the B-line artefacts and localize them both spatially and temporally within LUS videos. |  Focused on B-line artefacts only.
STN based deep network [73] |  Extended and fully-annotated version of the ICLUS-DB database. |  The precise demographics of the patient group in the database remain unknown; The possible inclusion of false positive cases; Noisy labels in the database.
multi-level RVM [76] |  Classification accuracy of above 90% and 100% specificity when tested on the dataset. |  The size of the dataset is not known.
SVM [23] |  The need of small dataset by using SVM. |  The learning stage is time-consuming.
V-Unet and X-Unet [78] |  Short training time; Accurate when scare medical images. |  No regularization may lead to over-fitting to US images only; Performance is dataset-dependent.
POCOVID-Net [80] |  The first dataset of lung POCUS recordings of COVID-19, pneumonia, and healthy patients. |  The dataset is restricted to convex probe.
A DSA-MIL module to predict the patentseverity and a MA-CLR for combination ofthe LUS data and the clinical information [81] |  Issues related to data heterogeneity, multi-modality and non-linearity are alleviated. |  The distribution of lung zone information is ignored.
LUSNet [82] |  Improves the performance of the neural network significantly and also aids the labelling by combining acoustic propagation features with gray scale images. |  Restricted only to COVID-19.

###  IV-C Model-based vs Data-driven LUS Image Analysis

Table IV provides a summary of all the aforementioned methods. Fig 2 to Fig 12
show some example results from the reviewed literature.

For the model-based approaches, the most obvious advantage is the lack of need
for training, and that a large dataset is not required. Given a specific task,
the model-based methods are able to show good performances in terms of
accuracy as the feature extraction algorithms are purposely designed by
experts. Some rapid-converging algorithms enable the analysis of LUS image
sequences in a relatively short amount of time, but it is still hard to
achieve real-time processing. Meanwhile, being inherently explainable, the
effects of the algorithms can be intuitively understood in each processing
step, so they are straightforward to modify.

However, considering the task-oriented nature of the model-based methods, the
ability of generalization can be limited in complex clinical cases. Therefore,
data-driven approaches are investigated. In DL methods, network parameters
need training through a large amount of data, so as to achieve the purposes of
object detection, recognition, segmentation and so on. When enough labeled
data are available, DL can achieve or even exceed the performance of humans
[83]. The supervised learning approaches have reached the accuracy required
for clinical diagnosis [84]. However, for LUS, it is usually difficult to
acquire a large number of multi-category data, so attempts to design weakly-
supervised and transfer learning methods have been made. For transfer
learning, the network trained with LUS images will have a better performance
than that trained with natural images [80]. In clinical scenarios, the high
detection efficiency is a must, and the data processing speed should be fast.
Therefore, real-time tasks and computationally efficient ML approaches have
also been studied.

##  V  Challenges and Future Research Directions

From the literature reviewed above, the importance of LUS in assisting
clinical diagnosis has been demonstrated thoroughly. However, although LUS has
a proven capability to intercept alterations along the lung surface and value
in treatment monitoring, it is still facing challenges in real application.

Despite the fact that model-based methods can fulfil the aim of line
identification, their generalization ability is not as good as data-driven
approaches such as ML techniques. But in future researches, one should not
neglect the inherent interpretability of the classical algorithms, which may
help alleviate the black box problem of ML/DL techniques.

As the application of data-driven approaches is predominant in the medical
field, data standardization becomes an essential problem. This concept
concerns various aspects of LUS image processing, such as data collection,
data labelling, and algorithm validation etc. So far, there is no
regularization in LUS image collection. Therefore the quality and the form of
the images are dependent on the equipment and the operator. This inconsistency
in data collection along with the lack of labeling standards brings
difficulties in providing an accurate ground truth of the lung condition,
because the diagnosis results varies among clinicians. Without a well-labelled
dataset to rely on, the output of the trained network would not be trusty
enough in clinical cases. Accordingly, gold rules for setting up standardized
datasets are urgently needed. What’s more, to maximize working efficiency, the
requirement on real-time artefact detection is also highly expected by
clinicians.

Even though the challenges mentioned above have not been adequately tackled
yet, several prospective data-driven techniques in medical image processing
are worth studying. For example, transfer learning is a feasible solution to
the lack of suitable datasets. A pretrained network can be first obtained on
ImageNet, and then be used for LUS image training. However the performance has
not been demonstrated to be as good as targeted training on medical image
datasets [85]. More recently, looking at current trends in the ML community
with respect to DL, we identify as key area which can be relevant for LUS
image processing that of self-supervised learning. Different from fully
supervised learning, self-supervised learning does not require manual labeling
since it generates them by itself [86]. In this sense, self-supervised
learning can be considered as a subset of unsupervised learning methods. Self-
supervised learning methods are attractive as they have been able to utilize
unlabelled data to learn the underlying representations. This is where self-
supervised methods play a vital tole in fuelling the progress of DL without
the need for expensive annotations and learn feature representations where
data provide supervision [87]. The successful application of self-supervised
learning in MRIs [88] sets a hope in its usage in other medical modalities
including LUS. Moreover, by compacting light-weigh network structures, the
computational efficiency will be possibly further improved.

Computational approaches have the potential to make quantitative LUS
assessment faster and more reliable, particularly in nonexpert hands [89].
Automatic detection, localization and quantification of B-lines are tasks of
great clinical interest and huge potential for clinical applications. Building
upon the idea of quantifying B-lines with neural networks [69], hybrid (i.e.
combined model-based and data-driven) quantitative LUS approaches will open up
new prospects for use not only as a diagnostic but also as a monitoring tool.

##  VI  Conclusions

Clinical applications of LUS have become more and more numerous. In this
article, we reviewed recent research progress in computational LUS image
reconstruction and analysis, including model-based methods as well as data-
driven methods. The approaches presented in this article all show the
possibility of assisting disease diagnosis by detecting and quantifying line
structures, especially through B-lines. The model-based methods are
unsupervised and explainable, whilst the data-driven methods provide better
generalization and more rapid development. In future studies, the combination
of the two methodologies can be a trend, as a result of which the requirement
of labelled dataset can be relaxed while keeping the performances of the
algorithms. Furthermore, the availability of the LUS dataset is improving,
which will likely nurture to algorithmic development.

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