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Automatic Detection and Evaluation of B-lines byLung Ultrasound

Shihong FangNYU Tandon School of Engineering

Brooklyn, NY 11201sf2584@nyu.edu

Abstract—Lung ultrasound is becoming a excellent diagnostictool in researches related to pulmonary congestion. However,the way to detect them is still rely on ultrasound examinerand the criteria of grading these B-lines differs from differentresearchers. In this work, we first propose a new automaticmethod to detect B-lines and compare the testing result with theground truth provided by medical examiner. In the meantime,we introduce a more reasonable and innovative way to quantifyB-lines by considering their occupancy in the rib space and theirintensity values in the ultrasound image. Finally, we try to findthe correlation between the B-line scores that we proposed andthe brain natriuretic peptide (BNP) values.

Index Terms—Pulmonary congestion; Oedema; Lung water;Ultrasound; B-lines; Medical image processing.

I. INTRODUCTION

Patients with acute heart failure are usually diagnosed tohave pulmonary congestion, which happens at the early stageof the syndrome. The syndrome is often a relative long incu-bation period, during which there is a gradual accumulation ofextra-vascular lung water (EVLW), a key variable in prognos-ing and detecting heart failure. Therefore, the assessing EVLWto diagnosis pulmonary congestion becomes a feasible way.Due to its easily availability at the bedside, non-invasiveness,easy implementation, lung ultrasound is increasingly become adiagnostic alternative in the setting of emergency medicine [1].There are mainly two types of artifacts, which can be differ-entiated from the lung ultrasound. One type is normal, withhorizontal, parallel lines beyond the pleura, which we normallycall “A-line”. The other shows vertical, comet-tail artifacts,arising from the pleural line, spreading up to the edge of thescreen, based on terminology and standards, we name them as“B-lines”, shown in Fig. 1.

B-lines are not only the excellent prediction of EVLW [2],but also a sign of alveolar-interstitial syndrome [3]. However,in the previous research study, mostly the lung ultrasoundexaminations were performed by an experienced investigatorand they would come up with different standards to quantifythe B-line score. In most cases, the number of B-lines inthe antero-lateral chest scan is usually summed to generatea quantitative B-line score [4], their conversion table from thenumber of B-lines to B-lines score differs. In few cases they

Advisor: Prof. Yao Wang (email: yw523@nyu.edu), NYU Tandon Schoolof Engineering, 9th Floor, 2 MetroTech Center, Brooklyn, NY 11201.

This material is based upon work supported by Dr. Gabe Rose, The MountSinai Hospital, Mount Sinai St. Luke’s and Mount Sinai West

are simply categorized as positive test and negative test [3].We found that these categorizations are not accurate, as twoor more thin B-lines can be combined into one wide B-lineas B-lines move with lung sliding. In addition, we assumeultrasound examiner may make mistake, and the result in theend would be subjective. Therefore, instead of detecting thenumber of B-lines manually, we investigated an automaticmethod to detect B-lines. Then the testing result is comparedwith the ground truth provided by medical examiner to obtainthe test accuracy. To avoid the ambiguousness caused by onlycounting the number of B-lines, we instead take the occupancyof B-lines into account. To our knowledge, the brightness ofB-lines is also a good indicator to evaluate the pulmonaryodema, we finally quantify the B-lines in a more reasonableand innovative way. In the end, we tried to find out if there isany correlation between our B-lines score and brain natriureticpeptide (BNP) test results when patients were under systolicand diastolic conditions respectively.

Fig. 1: A-line (upper panel) and B-line (lower panel) in lungultrasound image [4]

II. IMAGE PREPROCESSING

A. Image Rectification

To assist the projection along the radial line in the fan-shaped image, we introduce image rectification, which means

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Fig. 2: Image rectification: from fan area(black solid line) torectangle(red dot line)

we convert the original image from a polar coordinate systemto a Cartesian coordinate system.We calculated that the radiusof the fan area is 441 pixels and the range of the angle isfrom -43◦ to 43◦, so a 441 × 87 rectified image is createdafter rectification. In discrete system, transformed points arenot one to one correspondence, so as shown in Fig. 2, upperregion needs to be interpolated and lower region needs to becompressed.

For the pixels whose values are calculated by more thanone pixels in original polar coordinate system, we determinethe pixel value by:

Pr =

∑Ni=1 PoiN

where Pr denotes the value of pixel in rectified image whilePoi represents the group of N pixel’s values which can mapinto the same pixel in the rectified image. At the top of therectangle image, there will be some missing pixels because ofthe sparseness of center of the fan area. To solve this problem,an 8-neighbor-mean interpolation is implemented as:

Pr(i, j) =

∑i+1k=i−1

∑j+1l=j−1 Pr(k, l)

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Fig. 3 shows the ultrasound image before and after thetransformation.

B. Image Segmentation

Because essentially B-line arises from pleural line (Fig. 3),to find B-lines, it is important to first locate pleural line.

Due to the fact that pleural line is the brightest area in theentire ultrasound image, we use Intensity-based searching tofind pleural line. We first compute the vertical projection onthe rectified image by:

v(j) =

H∑i=1

Pr(i, j)

where i and j represent the row and column of the rectifiedimage respectively, H is the number of rows. After we finishthe projection (Fig. 4), we implement the searching algorithmto locate the maximum value along X direction. We assumethis is approximately where the pleural line lies.

Fig. 3: Original ultrasound image (upper panel), rectified im-age with 90◦ counterclockwise rotation (lower panel). Betweentwo ribs (* represents acoustic shadow of rib), there is a pleuralline, which is the interface between the lung and the chest wall(arrows).

Fig. 4: The vertical projection of the rectified image (upperpanel). The yellow solid line stands for maximum value, thedot line stands for the average value, and their crossing, whichis the red solid line, stands for the boundary of pleural linearea.

Instead of relying on one single pleural line, we definea pleural line area, which can better segment B-line areaafterwards. To get the pleural line area, we first calculatethe average value of the vertical project, then we start fromthe maximum value point, going along positive X direction,and label the point reaches the average intensity value. Fromthis point, we get the pleural line area, the remaining partcorresponds to the potential B-line area.

To obtain the width of pleural line, we take the horizontalprojection of the pleural line area as show in Fig. 5. Typically,we use the same method we use to get the horizontal boundaryof pleural line area.

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Fig. 5: The horizontal projection of the pleural line area. Theyellow solid line stands for maximum value, the dot line standsfor the average value, and their crossing, which is the red solidline stands for the boundary of pleural line area.

Thus far, we have found the pleural line space and whenwe extend the vertical boundary of pleural line area to the leftend of the entire rectified image. As show in Fig. 6, we canobtain the potential B-line area, which will be used for furtherprocess.

Fig. 6: Potential B-line area (within the blue box)

C. Image De-noising

In some samples we got, there are some cases with heavynoise (Fig. 7), in this section, we try to get rid of these noise.

As show in Fig. 3, normally there will be dark space underthe ribs, using this characteristic, we search the pixels’ valuesnear the horizontal boundary of B-line area, take an averageof these values and for each column of B-line area, we get aindex to describe the noise. In the next step, we get a row ofnumbers which can represent the noise. The de-noising stepinvolves letting every row of B-line area subtract this row. Inthe end, we get the de-noising B-line area image.

Fig. 7: Noisy sample (upper panel). Noisy B-line area afterimage segmentation (middle panel). B-line area after de-noising (lower panel).

III. B-LINE DETECTION

A. Lung Ultrasound Descriptor

Thus far, all the work we have done is based on analyzingone single screenshot. As a matter of fact, lung ultrasoundimaging is recorded within a short time window. Thus, wefinally could get a video clip for one lung ultrasound scan.We generate a lung ultrasound descriptor for each video clip.

From the characteristics of B-lines, we can see B-linesappear as white stripes in B-line area. When we do horizontalprojection on B-line area, we can simply plot the projectionvalues (Fig. 8). Because the length of the B-line area varies,we also need to normalize the projection by the length of theB-line area. The raising part of the curve apparently indicatesthere is one or more B-lines in that horizontal region.

Fig. 8: B-line area and its horizontal projection.

For each frame of the video clip, there will be a plot of thehorizontal projection. Each plot then forms a column and westack the columns together to get a image, which we defineit the lung ultrasound descriptor. Therefore, the length of thedescriptor equals the number of frame in each video clip whilethe width equals the width of the B-line area.

One example is show in Fig. 9

Fig. 9: Lung ultrasound descriptor sample.

B. B-line Descriptor

As shown in Fig. 9, the brighter part of lung ultrasounddescriptor is made because there is one or more B-lines inthat frame. To distinguish B-line from the background noise, athreshold must be set. Considering the variance of the intensityvalues, here we adopt the Otsu’s Method [5], which is aautomatic threshold algorithm.

The Otsu’s Method is based on a very simple idea: find thethreshold that minimizes the weighted within-class variance.The weighted within-class variance is:

σ2w = w0(t)σ

20(t) + w1(t)σ

21(t)

where weights w0,1 are the probabilities of the two classesseparated by a threshold t and σ2

0,1 are variances of these twoclasses. The class probability w0,1 is computed from the Lhistograms:

w0(t) =

t−1∑i=0

p(i), w1(t) =

L−1∑i=t

p(i)

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After we find the optimal value t, we convert the grayscalelung ultrasound image to the binary B-line descriptor image.At the same time, we also get the grayscale B-line descriptor(Fig. 10) by filtering the lung ultrasound descriptor image.

Fig. 10: B-line descriptor.

C. Revision

Ostu’d Method has its limitations: it assumes there are twoclasses in the image, which is true when we have B-lines in thediagnostic videos. However, when there is no B-lines, Otsu’smethod would still generate B-line descriptor. In this section,we try to avoid this situation by selecting some features thatcan first classify all our data set into B-lines and non-B-lineclasses.

1) Feature Selection: We notice that for the cases whenthere is no B-line, the horizontal projection values of their B-line areas would be smaller than those with B-line. Therefore,their grayscale B-line descriptor would seems lighter. Here,we define the average intensity value of the grayscale B-linedescriptor f to be our first feature.

In addition, as show in Fig. 11, when there is B-line, itsvertical projection value of the ultrasound descriptor changesa lot. That’s reasonable because B-line moves, becomes brightand faints out as the pleural sliding. So B-line will causeripples in the plot of the vertical projection of the ultrasounddescriptor, which leads to a high variance value. We define thevariance values vb to be our second feature.

Fig. 11: Vertical projection of the ultrasound descriptor. Sam-ple video with B-line (left panel). Sample video without B-line(right panel).

2) Classification: We are given 440 ultrasound diagnosticvideo clips in total. Half of them are labeled to have B-lines.After collecting two features’ values f, vb of these all datasets, we sample the training set while holding out nearly 40%of the data for testing.

In the training set, we use 5-fold cross validation to tune theparameters. We mainly use SVM classifier to do the training.As we can see from the Table. I, best parameters set found tobe linear kernel and penalty parameter C equals 10.

TABLE I: Testing results using various SVM classifiers

Kernel C gamma Accuracylinear 1 — 0.937linear 10 — 0.948linear 100 — 0.948linear 1000 — 0.944

rbf 1 0.001 0.579rbf 1 0.0001 0.524rbf 10 0.001 0.798rbf 10 0.0001 0.583rbf 100 0.001 0.917rbf 100 0.0001 0.798rbf 1000 0.001 0.937rbf 1000 0.0001 0.917

case precision recall f1-score supportw/o B-line 1.00 0.91 0.95 78w/ B-line 0.93 1.00 0.96 91

Note: In the first row, we treat “w/o B-line class” as positive and in the secondrow, we see “w/ B-line class” as positive, “support” means number of positivesamples.

3) Improvement: As shown in Table.I, the bottle neck liesthat there are some non B-line cases our algorithm consideras B-lines. We looked through these samples that our SVMclassifier mislabled (Fig. 12), we found a lot of them sharesomething in common: they have heavy A-lines and when wedo horizontal projection on their potential B-line area, theirplots appear similar to those with B-lines (Fig. 13). We thendecide to find another feature and modify our algorithm: firstfind the those samples with heavy A-lines and classify themdirectly into non B-line cases, and apply our SVM classifierto do the classification on the remaining cases.

Basically, we find that when there is A-line, there will beone or more spikes on the plot of the vertical projection onthe B-line area (Fig. 13). Take advantage of this characteristic,we decided to create a A-line descriptor, which is very similarto B-line descriptor.

One thing unlike B-line descriptor, we take vertical projec-tion on the potential B-line area and form one row, we stackthese rows and finally get A-line descriptor. Apparently, thelength of A-line descriptor is the length of B-line area whilethe width of A-line descriptor equals the number of frame.

We can easily find that there will some spikes shown inthe plot of the vertical projection of these A-line descriptor ifthe sample is with heavy A-lines. This would cause the firstorder derivative of the projection become more rugged and thevariance of it is unusually higher than those without A-line,which is shown in Fig. 14. Therefore, we define the variance(va) of the first order derivative to be our third feature.

4) A-line Detection: We use all three features f, vb, vawe have come up with to classify A-line and non A-line

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(a) (b)

(c)

Fig. 12: Snapshots of heavy A-lines sample

Fig. 13: Potential B-line area and its horizontal projection ofsample (a) in Fig. 12

cases. Considering these three features might have non-linearrelationships, we decide to use tree-based classifier. To preventour model having serious over-fitting problems, here we im-plement Bagging technique to solve A-line detection problem[6] We use the same data set and split setting as we use whenwe do classification previously.

For testing result, we list the top three classifier’s parametersetting, which is shown in Table. II, we can see that with thebest parameter setting, the precision of the A-line classificationis 100%, and the recall is a little bit lower, which is finebecause this classification serves as first filtering and later wewill still perform SVM to filter out those without B-lines.

5) Re-classification: Through A-line detection, we alreadypicked out some examples with heavy A-lines and classifythem into non B-line cases directly. Now we apply ourtrained SVM classifier to perform classification again on ourremaining test samples. Together with A-line detection, weget our final test results show in Table. III, which achievesimprovement compared to the results we simply implementSVM classifier.

(a) A-line descriptor

(b) Vertical projection on A-line descriptor

(c) First order derivative of the projection above

Fig. 14: Sample only with A-lines (left panel) and Samplewith simply B-lines (right panel)

TABLE II: Testing results using Random Forest Modeling

MSL MSS C MF MD S Std3 4 entropy 1 None 0.988 0.0101 4 gini 1 10 0.984 0.0153 8 gini 2 10 0.984 0.006

Note: MSL: min sample leaf, MSS: min sample split, C: criterion, MF:max feature, MD: max depth, S: mean validation score, Std: standard de-viation validation score

case precision recall f1-score supportw/ A-line 1.00 0.73 0.85 15w/o A-line 0.97 1.00 0.99 154

Note: In the first row, we treat “w/ A-line class” as positive and in the secondrow, we see “w/o A-line class” as positive, “support” means number of positivesamples.

TABLE III: Testing results using hybrid classifiers

case precision recall f1-score supportw/o B-line 0.99 0.96 0.98 84w/ B-line 0.97 0.99 0.98 85

Note: In the first row, we treat “w/o B-line class” as positive and in the secondrow, we see “w/ B-line class” as positive, “support” means number of positivesamples.

D. Summary

This section includes the core idea of our algorithm. Forthose with B-lines, we generate the both the binary B-linedescriptor and grayscale B-line descriptor, which also includesthe intensity information of B-lines. Since rather than staticscreen shot, dynamic examination would be more sensitivein detecting B-lines that move with pleural sliding images[2],we apply morphological filtering on the B-line descriptor toensure the connectivity between frames. The flow chart is draw

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in Figure. 15.

Fig. 15: Flow chart of B-line detection

In the end, we sum over the number of pixels in the binaryB-line descriptor and normalize with the number of frames toget our binary B-line score, meanwhile we sum over all theintensity values of the pixel in the grayscale B-line descriptorand do the same normalization to get our grayscale B-linescore.

IV. EVALUATION

Under doctor’s request, we try to find the correlation be-tween our B-line score and BNP values. Because all the pa-tients sit in the upright position and underwent lung ultrasoundof eight chest zones [7], which means we have eight diagnosticultrasound videos from each chest zone for each patient, thuswe have eight B-line scores. In addition, we have two differenttypes of B-line score, which we will discuss separately. Inthis section, we first choose one of these scores each timeand correlate it with BNP values. Then we sum up all theeight B-line scores to get a synthetic B-line score and compareit with BNP values. On doctor’s suggestion, we should lookinto their correlation when patients were under systolic anddiastolic conditions respectively.

A. B-line score (Binary) evaluation

Fig. 16 shows the plot of each B-line score (Binary) vs.the BNP values. We simply sum up all the eight score to getthe synthetic score, Fig. 17 shows the plot of synthetic B-linescore (Binary) vs. the BNP values. However, we hardly seeany correlation between any of these.

B. B-line score (Grayscale) evaluation

Fig. 18 shows the plot of each B-line score (Grayscale) vs.the BNP values. Like B-line score (Binary) evaluation, wesimply sum up all the eight score to get the synthetic score,Fig. 19 shows the plot of synthetic B-line score (Grayscale)vs. the BNP values. However, we hardly see any correlationbetween any of these.

V. CONCLUSION

This paper mainly propose a new method to quantify B-line in lung ultrasound image. In the meantime, we developa algorithm that can automatically detect if there is any B-line in the image. However, we could not verify if there is

Fig. 16: Correlation between eight individual B-line scores(Binary) and BNP values

Fig. 17: Correlation between synthetic B-line scores (Binary)and BNP values

any correlation between the B-line score that we proposedand the BNP values. Moreover, previous study has shown thatultrasound can predict well EVLW [8]. Therefore, we believemore information contains EVLW would help us verify theusefulness of our B-line scoring system.

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Fig. 18: Correlation between eight individual B-line scores(Grayscale) and BNP values

Fig. 19: Correlation between synthetic B-line scores(Grayscale) and BNP values

REFERENCES

[1] A. S. Liteplo, K. A. Marill, T. Villen, R. M. Miller, A. F. Murray, P. E.Croft, R. Capp, and V. E. Noble, “Emergency thoracic ultrasound in thedifferentiation of the etiology of shortness of breath (etudes): Sonographicb-lines and n-terminal pro-brain-type natriuretic peptide in diagnosingcongestive heart failure,” Academic Emergency Medicine, vol. 16, no. 3,pp. 201–210, 2009.

[2] P. Enghard, S. Rademacher, J. Nee, D. Hasper, U. Engert, A. Jorres, andJ. M. Kruse, “Simplified lung ultrasound protocol shows excellent pre-diction of extravascular lung water in ventilated intensive care patients,”Critical Care, vol. 19, no. 1, p. 1, 2015.

[3] D. Lichtenstein, G. Meziere, P. Biderman, A. Gepner, and O. Barre, “Thecomet-tail artifact: an ultrasound sign of alveolar-interstitial syndrome,”American journal of respiratory and critical care medicine, vol. 156,no. 5, pp. 1640–1646, 1997.

[4] E. Picano and P. A. Pellikka, “Ultrasound of extravascular lung water:a new standard for pulmonary congestion,” European heart journal, p.ehw164, 2016.

[5] “A threshold selection method from gray-level histograms,” IEEE Trans-actions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62–66, Jan1979.

[6] G. James, D. Witten, T. Hastie, and R. Tibshirani, An introduction tostatistical learning. Springer, 2013, vol. 6.

[7] S. E. Frasure, D. K. Matilsky, S. D. Siadecki, E. Platz, T. Saul, andR. E. Lewiss, “Impact of patient positioning on lung ultrasound findingsin acute heart failure,” European Heart Journal: Acute CardiovascularCare, vol. 4, no. 4, pp. 326–332, 2015.

[8] G. Volpicelli, S. Skurzak, E. Boero, G. Carpinteri, M. Tengattini, V. Ste-fanone, L. Luberto, A. Anile, E. Cerutti, G. Radeschi et al., “Lung ultra-sound predicts well extravascular lung water but is of limited usefulnessin the prediction of wedge pressure,” The Journal of the American Societyof Anesthesiologists, vol. 121, no. 2, pp. 320–327, 2014.