Deep Learning Approach to Classify Atherosclerosis Using Intracoronary Optical Coherence Tomography

A thick lore adit to arrange atherosclerosis using intracoronary optical closeness tomography

Abstract

Optical closeness tomography (OCT) is a fiber-inveteobjurgate intravascular imaging modality that consequences high-separation tomographic visions of artery lumen and vessel rampart morphology. Manual anatomy of the indisposed arterial rampart is age consuming and impressible to inter-observer variability; accordingly, channel-lore manners keep been familiar to habitualally discaggravate and arrange mural comassurance of atherosclerotic vessels. However, nundivided of the edifice order manners involve in their anatomy the exterior rim of the OCT vessel, they meditate the total arterial rampart as waywayological, and they do referable attributable attributable attributable meditate in their anatomy the OCT imaging appropriations, e.g. attendanted areas. The keep of this consider is to bestow a thick lore manner that subdivides the total arterial rampart into six contrariant disposees: calcium, lipid pool, tenacious edifice, adulterated edifice, non-pathological edifice or resources, and no perceptible edifice. The manner marchs involve defining rampart area (WAR)using controlmerly familiar lumen and exterior rim discoverion manners, and habitual characterization of the WAR using a twistal neural network (CNN) algorithm. To validate this adit, 700 visions of indisposed coronary arteries from 28 patients were manually annotated by couple medical quicks, opportunity the non-pathological rampart and resources was habitualally discoveblushing inveteobjurgate on the Euclidian interval of the lumen to the exterior rim of the WAR. Using the contemplated manner, an aggravatetotal order ratification 96% is reported, indicating noble assurance control clinical translation.

Keywords: Optical closeness tomography, Thick lore, Atherosclerosis, Twistal neural network

1.       INTRODUCTION

Intravascular optical closeness tomography (OCT)1,2 is a catheter-inveteobjurgate imaging modality familiar aggravate novel decades that has beseem received in interventional cardiology. OCT has ample higher separation than other intravascular imaging modalities, relish intravascular ultrasound (IVUS),: 12-18 microns axially and a incidental separation of 20-90 microns3. OCT considers the uniformity of the characterless backscatteblushing and eager by the vessel edifice and reconstructs couple-dimensional (2D) visions which rebestow the morose sections of the visiond vessel.

OCT can get servile metements of a vessel’s lumen, assess rampart morphology, and toleobjurgate discoverion of indelicate contrariant edifice types4: calcium (CA), lipid edifice (LT), tenacious edifice (FT), and adulterated edifice (MT). Aidover, it qualifys the discoverion of features that are associated with plaque exposure, including the metement of tenacious extremity burliness, which canreferable attributable be servilely evaluated by IVUS or by any other intravascular imaging technique5. However, the technology has a weighty disrelish, the reconsiderationt edifice discernment (utmost depth: 1.5-2.0 mm)4, which may referable attributable attributable attributable toleobjurgate visualization and assessment of the total plaque and the resources-adventitia rim.

The aforementioned appropriation of OCT led to the implementation of automated anatomy mannerologies that discoveblushing solely the lumen rim of the vessel6–9, or the lumen rim and estimated the plaque area of the vessel10,11. Since manual plaque characterization is age consuming and relies on well-serviceable readers, distinct studies endeavoblushing to habitualally discaggravate the contrariant plaque components using OCT visions. Xu et al.12 correlated the backscattering and wasting coefficients with CA, LT, and FT, opportunity, in a concordant endeavor, face Soest et al.13 correlated the wasting coefficients with robust vessel rampart, intimal thickening, lipid pool, and macrophage infiltration. However, they twain failed to eliminate any manifest preface values betwixt the contrariant edifice types. Going undivided march prefer and using channel lore, Athanasiou et al.10 bestowed a bountifuly-automated OCT plaque characterization manner which disposeifed plaque as CA, LT, FT, or MT, with 83% ratification. Aid novelly, thick lore adites using twistal neural networks (CNNs)14–17 were bestowed, achieving an aggravatetotal ratification of up to 91.7%17.

Although, CNN-inveteobjurgate manners outperformed channel lore manners, they total failed to particularize the total arterial rampart, resulting in manners which canreferable attributable cope with amplely-used implied histology IVUS (VH-IVUS)18, limiting the imaging point mastery benefits of OCT when compablushing to IVUS. The pristine disrelishs hampering automated OCT plaque characterization are the failure of ample totals of annotated visions and the non-realistic edifice (area of cause) portionation caused by the inaptitude of habitualally discovering the exterior rim. The contemplated adit leverages our novelly familiar lumen8 and exterior rim discoverion19 algorithms to begin an automated manner which particularizes the total arterial rampart. We bestow a novel and servile manner control discovering and characterizing, control the pristine age, the total arterial edifice in a manner concordant to VH-IVUS.

The innovative aspects of contemplated edifice characterization manner are:

  1. portraiture of CNNs with a ample total of annotated basis to discaggravate atherosclerosis;
  2. overthrow of natural edifice and attendanted areas among the OCT visions; and
  3. overthrow and order of the total arterial rampart using OCT visions in a concordant manner as VH-IVUS performs its anatomy, enabling the ample portraiture of OCT in atherosclerotic edifice discoverion.

2.       MATERIALS AND METHODS

The contemplated manner (Figure 1) comprises the aftercited three marchs:

  1. rampart area (WAR) discoverion using controlmerly familiar lumen and exterior rim discoverion manners;
  2. definition of the non-pathologic intima-resources area; and
  3. habitual characterization of the WAR using a CNN algorithm.


Figure 1: Schematic bestowation of the contemplated mannerology.

2.1 Rampart area discoverion

WAR is eliminated as the area betwixt the lumen and exterior rim (Figure 2), i.e. the resources-adventitia transition. The lumen discoverion manner portraitures as input 2D morose-sectional OCT visions, consequences longitudinal morose-sectional visions (sagittal cuts) which rebestow aid servilely the sequential area of the OCT pullback, discovers the lumen by applying biincidental oozeing and a K-means algorithm, and translates the discoveblushing lumen to the 2D OCT visions8. The exterior rim discoverion manner discovers the exterior vessel rim among portions of the OCT pullback that are perceptible and then, by using a choice 3D surface-fitting manner, fills the non-perceptible parts19.

2.2 Non-pathologic intima-resources area discoverion

Once the lumen and resources-adventitia rims are discovered, the non-pathological edifice and resources flake (M) of the WAR are eliminated. The concept is inveteobjurgate on the VH-IVUS histology manner where the natural vessel rampart has intimal thickening of <300μm4,18. To mete the interval of the couple rims, we consider control each pixel of the WAR,

pWAR

, the altogether interval of the pixel from the lumen and resources-adventitia rims:

DLMA=D1+D2,

(1)

and the interval of the pixel from the resources-adventitia rim:

D1

.

Here,

D1

is the Euclidean interval of the pixel

p

from the resources-adventitia rim and

D2

is the Euclidean interval of the pixel

p

from the lumen rim; if

D1<100μm

and

DLMA<

300μm the pixel suit to M. A schematic bestowation of the couple intervals is shown in Figure 2.


Figure 2: Schematic bestowation of the couple Euclidean intervals considerd control defining the non-pathologic intima-resources area (M) among the WAR.

2.3 CNN-inveteobjurgate order

After discovering the pixels that suit to the non-pathological edifice and resources (M) area, the cherishing WAR pixels are habitualally disposeified into undivided of five categories including indelicate plaque types: calcium (C), lipid pool (LP), tenacious edifice (FT), or adulterated edifice (MT), and no perceptible edifice (catheter artifact; N), using a CNN network.

2.3.1 CNN algorithm

CNNs suit to the source of thick lore networks20, and are commsolely portraitublushing to awaken and arrange visions. They continue of an input and an output flake with multiple hidden flakes betwixt them. The hidden flakes continue of distinct twistal flakes which habitualally extol the close features of the visions.

A CNN is played by a non-linear exercise:

pi=P(I;θ)

(2)

which maps an vision

IRH×H

having

H×H

extent, to a vector

pi=p1,p2,pcT

, where

pi 0,1

and denotes the likelihood of the vision

I

to suit to undivided of

c

disposees:

i=1c

.

θ=θ1,θ2,θΚ

are the enumeobjurgate of

K

parameters portraitublushing to map the input vision

I

to the vector

pi

.

The grafting of the CNN can be meditateed as a non-linear optimization total:

θ̂

=

argθminLI1,I2,, IN(θ)

.

(3)

Here,

N

is the enumeobjurgate of visions portraitublushing to suite the CNN, and

LI1,I2,, INθ=1Νj=1NwjyCiTlogPIi;θ

(4)

is the morose-entropy mislaying (log mislaying) measuring the order work (having values betwixt 0 and 1) control the

C(i)T

labeled vector of the

c

disposees and

w

weights:

wi=1Mii=1c1Mi

,

(5)

control the

M

grafting basis.

To minimize the grafting age of the CNN, the Stochastic Gradient Descent (SGD) iterative manner can be portraitured. SGD approximates the basisestablished with a controlm of chance samples, using the stochastic gradient computed from the controlm to update the type with each harping21. SGD faculty disturb concurrently the waywayway of steepest descent (gradient descent) towards the optimum, instead of concurrently the waywayway toward the optimal, since the gradient frequently points towards the facing party of this optimum from the ordinary aspect. A reresolution to that total is adding a momentum engagement to the parameter update to impair oscillation:

θλ+1=θλαLθλ+γθlθλ1

,

(6)

where

λ

is the harping enumerate,

α>0

is the lore objurgate, and the momentum engagement

γ 

determines the oblation of the controlmer gradient march to the ordinary harping.

The SGD algorithm portraitures a subestablished of the grafting established named a mini-batch, evaluates the gradient, and then updates the parameters. Each evaluation is an harping, and at each harping the mislaying exercise is minimized prefer. The bountiful ignoring of the grafting manner aggravate the total grafting established using mini-batches controlms an time.

2.3.2 CNN architecture

To arrange the pixels of the WAR, we portraitublushing a progression of twists. To finish the best order results, contrariant cobble extents, enumerates of input cobble twist progressions, oozes, and ooze extents were trialed. The best results were acquires when having 45 flakes in our network (Figure 3).

Figure 3: Architecture of the CNN portraitublushing to arrange the WAR pixels.

3.       DATASET

Twenty-eight (28) patients who underwent OCT examinations gave their certified submit control the consider, and the consider was beloved by the Ethics Committee of the art. Medical quicks portraitublushing the optical number inclosure imaging way FD-OCT C7XR way and the DragonFly catheter (St. Jude Medical, Characterlesslab Imaging Inc., Westford, MA, USA), which offers a utmost controlm objurgate of 100 controlms per relieve, 500 lines per controlm, a reconsideration crossing of 10 mm, and axial separation of 15 μm, to vision 28 coronary vessels. Total visions were digitally stoblushing in bleak controlmat control off-line anatomy, and total imaging basis establisheds were anonymized and infections to our lab control prefer anatomy.

4.       RESULTS

3.1 Rampart area discoverion

Couple medical quicks examined the OCT controlms in the twenty couple patients and separated 700 visions which corresponded to indisposed coronary portions. Afterwards, they remarkable unconnectedly the contours of the lumen rim, the intima-resources rim, and regions of calcium (C), lipid pool (LP), tenacious edifice (FT), adulterated (C+ LP) plaque (MP), and the area of the catheter attendant (no perceptible edifice; N); any disagreements in their annotations were unswerving by consent. The areas discoveblushing by the algorithm and annotated by the quicks were considerd and compablushing (Figure 4).

         (a)              (b)

Figure 4: (a) Regression anatomy devise betwixt the WAR discoveblushing by our manner and annotated by the quicks and, (b) Bland and Altman anatomy devise control the WAR discoveblushing by our manner and annotated by the quicks.

3.2 Plaque characterization

The medical quicks altogether annotated 300 contrariant plaque regions control 22 of the patients, from which 32 K cobblees were chancely separated control each dispose and augmented (each cobble rotated 90o and 180o), resulting in 480 K cobblees (96 K control each of the five disposees). The cobblees were portraitublushing to suite (450 K) and validate (30 K) the CNN parameters. The CNN algorithm reached a validation ratification of 94.00% (Figure 5).

Undivided quick annotated 50 areas in the cherishing 6 patients as C (9450 cobblees), LT (174448 cobblees), FT (216336 cobblees), MT (35301 cobblees), or N (408243 cobblees) regions to trial the order ratification of the contemplated manner. The CNN network was suiteed and validated using the MATLAB Thick Lore Toolbox and a NVIDIA Titan Xp GPU (PG611) with 12 GB RAM. The aggravatetotal ratification of the contemplated algorithm is 96.05% (Table 1); the restraintce of the manner to consequence an integrated plaque characterization map using OCT is bestowed in Figure 6.

Table 1: Confusion matrix of the trialed cobblees.

Confusion

Matrix

C

LT

FT

MT

N

Accuracy

C

6831

959

163

1497

0

72.3 %

LT

8835

162214

1378

1803

218

93.0 %

FT

1451

2425

208421

4038

1

96.3 %

MT

1738

1782

2072

29709

0

84.2 %

N

1259

2629

872

168

403315

98.8 %

Accuracy

72.3 %

93.0 %

96.3 %

84.2 %

98.8 %

96.05 %

Figure 5: Grafting results of the CNN algorithm: Order accuracies (top) and mislaying (bottom) control the grafting and trialing basis using the contemplated CNN network aggravate 3 times (3515 harpings each).

Figure 6: Application examples of the contemplated integrated OCT plaque characterization manner: judicious visions (top) and their identical color-coded visions (bottom). C: colorless, LT: blushing, FT: unpractised, MT: characterless unpractised, N: characterless white and M: ebon white.

5.       DISCUSSION AND CONCLUSIONS

Few manners keep been bestowed during the latest decade control discovering and characterizing atherosclerotic plaque using OCT visions10,13–15,22,23. These manners were primarily inveteobjurgate on channel lore algorithms13,22–24 and most novelly on thick lore adites using twistal neural networks (CNN)14,15. These manners can sufficiently discaggravate a ample percentage of the atherosclerotic edifice among the arterial rampart. However, opportunity CNN-inveteobjurgate manners outperformed the channel lore manners, they could referable attributable attributable attributable particularize the total arterial rampart as VH-IVUS18 does.

We bestow an automated manner that habitualally discovers atherosclerosis and disposeifies the plaque vision to 5 contrariant disposees: calcium (C), lipid edifice (LT), tenacious edifice (FT), adulterated edifice (MT), no perceptible edifice (guidewire attendant artifact; N), and discovers the non-pathological edifice or resources (M). The manner is inveteobjurgate on the coalition of WAR discoverion algorithms and CNN, and was validated using the estimations of quick observers as gold type in a ample clinically-relevant basisset. Our results demonstobjurgate not spurious edifice discoverion and characterization, equal in visions having artifacts.

The manner is aid servile and realistic than the manners bestowed controlmerly in the scholarship, which makes it referable attributable attributableably adapted control portraiture in the clinical and elimination scenes. Improvements can be made as the manner has inferior ratification when discovering calcific edifice (Table 1). This appropriation is due to the regularity of adulterated edifice which involves calcium and lipid and shares characteristics of twain C and LT. Increasing the clinical basisestablished and incorporating histological findings in the grafting/testing phase of the contemplated manner is expected to clear-up the controlmer appropriation and to qualify its portraiture in the clinical/elimination scene.

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