Automated knowledge-based detection of nonobstructive and obstructive arteriallesions from coronary CT angiographyDongwoo Kang, Piotr J. Slomka, Ryo Nakazato, Reza Arsanjani, Victor Y. Cheng, James K. Min, Debiao Li,

Daniel S. Berman, C.-C. Jay Kuo, and Damini Dey Citation: Medical Physics 40, 041912 (2013); doi: 10.1118/1.4794480 View online: http://dx.doi.org/10.1118/1.4794480 View Table of Contents: http://scitation.aip.org/content/aapm/journal/medphys/40/4?ver=pdfcov Published by the American Association of Physicists in Medicine

Automated knowledge-based detection of nonobstructive and obstructivearterial lesions from coronary CT angiography

Dongwoo KangDepartment of Electrical Engineering, University of Southern California, Los Angeles, California 90089

Piotr J. Slomka, Ryo Nakazato, Reza Arsanjani, Victor Y. Cheng, and James K. MinDepartments of Imaging and Medicine and Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center,Los Angeles, California 90048

Debiao LiBiomedical Imaging Research Institute, Department of Biomedical Sciences, Cedars-Sinai Medical Center,Los Angeles, California 90048

Daniel S. BermanDepartments of Imaging and Medicine and Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center,Los Angeles, California 90048

C.-C. Jay KuoDepartment of Electrical Engineering, University of Southern California, Los Angeles, California 90089

Damini Deya)

Biomedical Imaging Research Institute, Department of Biomedical Sciences, Cedars-Sinai Medical Center,Los Angeles, California 90048

(Received 12 September 2012; revised 29 January 2013; accepted for publication 20 February 2013;published 22 March 2013)

Purpose: Visual analysis of three-dimensional (3D) coronary computed tomography angiography(CCTA) remains challenging due to large number of image slices and tortuous character of the vessels.The authors aimed to develop a robust, automated algorithm for unsupervised computer detection ofcoronary artery lesions.Methods: The authors’ knowledge-based algorithm consists of centerline extraction, vessel classi-fication, vessel linearization, lumen segmentation with scan-specific lumen attenuation ranges, andlesion location detection. Presence and location of lesions are identified using a multi-pass algorithmwhich considers expected or “normal” vessel tapering and luminal stenosis from the segmented ves-sel. Expected luminal diameter is derived from the scan by automated piecewise least squares linefitting over proximal and mid segments (67%) of the coronary artery considering the locations of thesmall branches attached to the main coronary arteries.Results: The authors applied this algorithm to 42 CCTA patient datasets, acquired with dual-sourceCT, where 21 datasets had 45 lesions with stenosis ≥25%. The reference standard was providedby visual and quantitative identification of lesions with any stenosis ≥25% by three expert readersusing consensus reading. The authors algorithm identified 42 lesions (93%) confirmed by the expertreaders. There were 46 additional lesions detected; 23 out of 39 (59%) of these were less-stenosedlesions. When the artery was divided into 15 coronary segments according to standard cardiologyreporting guidelines, per-segment basis, sensitivity was 93% and per-segment specificity was 81%using 10-fold cross-validation.Conclusions: The authors’ algorithm shows promising results in the detection of both obstruc-tive and nonobstructive CCTA lesions. © 2013 American Association of Physicists in Medicine.[http://dx.doi.org/10.1118/1.4794480]

Key words: coronary CT angiography, centerline extraction, lesion detection, lumen segmentation,stenosis quantification

I. INTRODUCTION

Coronary artery disease (CAD) is the leading cause of deathworldwide for both men and women.1 Three-dimensional(3D) coronary computed tomography angiography (CCTA)with the use of multidetector CT scanners is increasinglyemployed for noninvasive evaluation of CAD, having shownhigh accuracy and negative predictive value for detection of

coronary artery stenosis in comparison with invasive coronaryangiography.2–6 Beyond stenosis, CCTA also permits non-invasive assessment of atherosclerotic plaque and coronaryartery remodeling.7–9

Although computer-aided extraction of the coronary arter-ies is often employed to aid visual analysis,10–14 clinical as-sessment of CCTA and lesion detection is based on visualanalysis, which is time consuming and subject to observer

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variability.15 It was reported that acquiring expertise in CCTAmay take more than 1 yr.15 Automatic software that detectsand identifies the coronary artery lesions would reduce suchobserver variability as well as the time for assessment.

Many computer-aided algorithms for detecting and diag-nosing various abnormalities have been developed for medi-cal imaging, such as detection and quantification of chronicobstructive pulmonary disease in lung,16–20 colon cancer,21–24

and lesions in mammograms.25–28 Detection and quantifica-tion of coronary artery lesions are particularly challengingdue to limited spatial resolution and coronary artery motion,even smaller plaque size than arteries, and complex and vari-able coronary artery anatomies. Automated lesion detectionrequires accurate extraction of coronary artery centerlines,classification of normal and abnormal lumen cross sections,quantification of luminal stenosis, and classification of lesionswith the different degree of stenosis.

Obtaining a reliable coronary centerline from CCTA is akey step which serves as a starting point for lumen segmenta-tion and stenosis grade calculation for lesion detection. Manyapproaches have been proposed on centerline extraction: thin-ning based techniques,23, 29–31 tracking methods,32–34 minimalpath techniques,10, 13, 35 and distance transform methods36–39

have been presented.To date, only a few studies have attempted automatic de-

tection of lesions;40–44 only obstructive lesions (with steno-sis ≥50%) were detected. To our knowledge, there has beenno attempt of automatic detection of nonobstructive lesions(25%–49%). However, nonobstructive lesions (25%–49%)have been shown to be a clinically significant predictor of fu-ture coronary events.45, 46 We have previously described a pre-liminary algorithm for automated detection of both obstruc-tive and nonobstructive lesions, validated on 19 patients.47

In this work, we describe an improved automated algorithm,which was validated on detection of lesions in the left ante-rior descending (LAD), left circumflex (LCX) and right coro-nary artery (RCA) of 42 consecutive patients (126 arteries,252 proximal and mid segments). The aim in our study was todevelop a robust automated algorithm to detect both obstruc-tive and nonobstructive lesions from CCTA and validate it incomparison with three experienced expert readers.

II. METHODS

Our algorithm can be divided into following four mainsteps, as described below: (1) centerline extraction and ves-sel classification, (2) vessel linearization, (3) lumen segmen-tation, and (4) lesion location detection. The algorithm re-quires two user-defined points as input, at the ostium of RCAand the left main (LM) coronary artery, and second, the plac-ing of a standard circular region of interest in the aorta atthe level of the LM ostium, to obtain scan-specific attenua-tion range for luminal contrast as previously described.48, 49

Note that algorithms for the automatic segmentation of aortaand detection of the origin of coronary arteries have beenpreviously proposed;50 thus, they were not the focus of thiswork. The algorithm uses a 3D CCTA image dataset (typi-cally the best phase) as input and a binary mask of coronary

tree that is initially computed by the commercially availablecoronary artery segmentation software (Circulation, SyngoMMWP Version VE31A, Siemens Medical Solutions, Forch-heim, Germany). The binary mask was used as a first step forcenterline extraction. Following this step, our approach usesscan-specific threshold levels with recursive region grow-ing for accurate lumen segmentation. The remaining processof lesion detection is automated. The luminal attenuation inCCTA differs with acquisition protocols and between patientsscanned with the same protocol. Plaque attenuation thresholdshave been shown to vary significantly with intracoronary lu-men attenuation51 and reconstruction parameters choice52 andare patient and scan-specific. Therefore, scan-specific attenu-ation threshold levels for lumen and plaque48, 49 are importantfor accurate lumen segmentation and were utilized in our al-gorithm. Our knowledge-based algorithm allows normal ta-pering of the coronary arteries and identifies and adjusts forarterial branch points. The flowchart of the proposed auto-mated lesion detection algorithm is given in Fig. 1.

Our study consisted of 42 consecutive patients, who under-went CCTA for clinical reasons at the Cedars-Sinai MedicalCenter. All CCTA datasets were acquired on the dual-source64-slice CT scanner (Definition Siemens Medical Solution,Forchheim, Germany) with a gantry-rotation time of 330 msand standard collimation of 0.6 mm, and had good-excellentimage quality. CCTA scan parameters were 512 × 512 ma-trix, voxel size 0.38 × 0.38 × 0.3 mm3, and typically con-sisted of 400–500 slices per dataset. Of the 42 patients, 10patients subsequently underwent invasive coronary angiogra-phy (ICA) within 1 month of the CCTA scan.

II.A. Centerline extraction and classification of threemain arteries

In this section, we present an automatic centerline extrac-tion algorithm as our first step, which also classifies the threemain coronary arteries: LAD, LCX, and RCA.

Initially, 3D thinning53 is applied to the input mask (CCTAdataset bounded by the initial lumen mask), resulting in a col-lection of N center points xi in the arteries including aorta.Subsequently, these N points are connected using graph the-ory. An undirected graph G = 〈V,E〉 is defined as a set ofnodes (V = {p1, p2,...,pN }) and a set of edges (E) that con-nect adjacent nodes. A non-negative weight we is assigned oneach edge e ∈ E, where we is defined as the Euclidean dis-tance between two nodes that are connected by an edge e. Wedeleted the thinning points inside the aorta by detecting thecenter of aorta pa which is found by using Dijkstra’s shortestpath method54 applied between the ostium point of RCA pR

and the ostium point of the LM pL, so that the points in the leftand right coronary arteries are not connected to each other.

A temporal end point ptemp1 ∈ V of RCA, the far-thest point from the ostium pR, is found using Dijk-stra’s shortest path method. Among the points pi ∈ {V− {point s on the path from pR to ptemp1}}, another temporalend point ptemp2 ∈ V of RCA is found by searching the maxi-mum cost that is the sum of distances from pR and ptemp1 ∈ V

using Dijkstra’s shortest path. The end points ptemp1 ∈ V and

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FIG. 1. Flowchart of the proposed method.

ptemp2 ∈ V are further classified into the end points of pos-terior descending artery (PDA)-RCA and posterior lateralbranch (PLB)-RCA utilizing anatomical knowledge of rela-tive artery positions.

Similarly, the end point of the first left coronary artery (ei-ther LAD or LCX) is also found using Dijkstra’s shortest pathmethod. The LAD and the LCX are further classified usingknowledge-based rules of relative artery positions. The endpoint of the Ramus artery is also identified if it exists. In eachof the three main coronary arteries, the end points of smallbranches, such as the first diagonal branch (D1) on LAD andthe first obtuse marginal branch (OM1) on LCX, are also de-tected using Dijkstra’s shortest path method and anatomicalknowledge of relative artery position. The first row in Fig. 2shows the results of an extracted centerline and a vessel clas-sified as LAD.

II.B. Vessel linearization

The vessels are subsequently converted to a linearized rep-resentation for further image processing. At each point pi

∈ V of the extracted centerlines, two basis vectors, whichspan the cross-sectional plane perpendicular to the centerline,are calculated. The basis vectors define the cross-sectionalplanes perpendicular to the centerline, which are used tomap the arteries to linearized image coordinates. These cross-sectional planes (size 8 × 8 mm2, corresponding to 21 × 21matrix with a voxel-size of 0.38 mm, the smallest voxel di-mension of the coronary CTA volume) are stacked up alongthe centerlines, resulting in 3D linear volume of coronary ar-teries. The second row in Fig. 2 shows an example of the re-sulting linearized vessels.

II.C. Lumen segmentation and calciumvolume measurement

Plaque attenuation has been shown to vary significantlywith intracoronary lumen attenuation55, 56 and reconstruc-tion kernels.51, 57 Thus, application of scan-specific or evenplaque-specific attenuation thresholds is essential.48, 49 Sinceattenuation ranges for lumen and plaque can depend on thepatient and the acquisition protocol, we compute these atten-uation ranges automatically from the scan using a validatedmethod previously described by our group.48, 49 Attenuation

FIG. 2. The first row shows an example of extracted centerlines in mid LADand D1. Each column shows an image at different angles with axes perpen-dicular to each other. The second row shows vessel linearization (LAD) of thefirst row in three orthogonal directions. Both rows show the same location ofa lesion (25%–49% stenosis by expert visual grading). The third row alsoshows linearized vessel (LAD) of a normal CCTA dataset. The outline (thirdrow) shows segmented lumen using our method. Detected lesion locationsare marked by points around the detected locations.

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FIG. 3. Example of lumen segmentation and lesion detection in LAD. Range of proximal LAD lesion (stenosis 25%–49%) marked by expert is shown as asmall box at around x = 27 mm – 48 mm. Lumen diameters computed from the segmented lumen are shown and their cropped lumen diameters by anatomicalknowledge are also shown. Expected “normal” luminal diameter is derived from the scan by automated piecewise line fitting between branch points, and takesinto account “normal tapering” present in the dataset. The locations of the lesions with ≥25% stenosis detected by the algorithm, concordant with the expertobserver, is marked with vertical arrows.

thresholds for the lumen, noncalcified, and calcified plaqueare found from the image histogram of the “normal bloodpool” region-of-interest placed in the aortic root, and are ad-justed for proximal-to-distal decrease in contrast.48, 49 Scan-specific attenuation range for the lumen is defined by theupper attenuation threshold for noncalcified plaque and thelower attenuation threshold for calcified plaque.48, 49 The lu-men is then identified by recursive region-growing in the lin-earized volume, using the computed attenuation range, whichallows exclusion of calcified and noncalcified plaque. An ex-ample of the lumen segmentation is shown in the third row inFig. 2. Additionally, calcium volumes are measured using theattenuation threshold for calcified plaque from the linearizedvolume.

II.D. Detection of lesions with stenosis

From the previous steps, presence and location of lesionsare identified by a knowledge-based algorithm, using the lu-men segmentation performed with scan-specific lumen atten-uation range. Lumen diameters for each 2D cross section arefirst obtained from the segmented lumen areas. Expected or“normal” luminal diameter is derived from the scan by au-tomated piecewise least squares line fitting (Fig. 3) over theproximal and mid segments (67%) of the coronary artery be-tween branch points detected; this computation allows us totake into account any “normal tapering” present in the dataset.The lumen diameters at all positions are first cropped, consid-ering expected dimensions of the coronary arteries,58 and thenthe lumen diameters after branch points are cropped again byusing the lumen diameters before the branch points. Lesiondetection is then performed in multiple passes through thecoronary artery, using the following steps:

(i) In the first pass, all possible lesions are found by con-sidering the difference, d, from the piecewise fittedline, as below:

d = (as + b) − ls , (1)

where s is the distance in mm from the ostium, ls is theluminal diameter at s, and a and b are obtained fromleast-squares line fitting. In this first pass, branch-points are excluded by cropping above the fitted line,as shown in Fig. 3.

(ii) For each possible lesion, the algorithm searches lo-cally for proximal and distal normal references byconsidering d in Eq. (1).

(iii) In a second pass, we compute a stenosis estimateSe(%) for each possible lesion, as below:

Se =[

1 − ls

(as + b)

]× 100. (2)

If Se is greater than or equal to 25%, the lesion is con-sidered for the next step.

(iv) For lesions with Se ≥ 25%, stenosis is computed foreach cross section corresponding to s between thedetected proximal and distal limits, considering bothreference limits, as previously published:59

St =[

1 − ls

lp − sp

sd(lp − ld )

]× 100, (3)

where ls, lp, ld are the luminal diameters for crosssection corresponding to s, proximal, and distal ref-erences; sp and sd are the linear distances between theproximal reference and the distal reference, and thecross section corresponding to s, respectively.

Lesions with maximum stenosis St ≥ 25% are marked(Fig. 4) in the region of maximum stenosis as well as the prox-imal and distal reference segments.

At branch point locations, lesions can be missed due tothe wider lumen diameter at the branch. Therefore, in addi-tion to the above lesions with stenosis detection algorithm,a search for calcified lesions was performed at each branchpoint by searching above automated measuring volume us-ing scan-specific attenuation threshold levels for lumen andplaque,48, 49 and a calcified lesion was added at that location if

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FIG. 4. An example of 3D volume rendering (a), detected nonobstructivelesion (mixed plaque) in a CCTA image (b), and according ICA image (c).Arrows in (a)–(c) indicate the location of the same lesion (25%–49% steno-sis by expert visual grading from CCTA and 34.0% stenosis by quantitativeanalysis from ICA).

detected. The user has the option in our software to manuallyaccept or reject any identified lesions as needed (calcified ornoncalcified). Once rejected, the lesion is marked as correctedand the annotated limits of the lesions are deleted.

II.E. Visual assessment and reference standard

All datasets were first visually assessed in a standard andsystematic way, by three experienced expert readers, usingconsensus reading to minimize the interobserver variability;these readers were three experienced imaging cardiologists.Segmental analysis was based on the standard 15-segmentAmerican Heart Association.60 Each segment of the coronaryartery tree was graded for the presence and type of plaqueor stenosis, as recommended by published guidelines of theSociety of Cardiovascular CT,61 and all coronary lesions withstenosis ≥25% were identified. This was used as the referencestandard for algorithm performance in this study. For com-parison, a second blinded reader (imaging cardiologist withLevel III CT certification, with 1 yr of experience with car-diac CT) also independently identified all coronary lesionswith stenosis ≥25%. The observer agreement for this readerwith the reference standard was 94.8% (kappa 0.84, 95% con-fidence interval 0.75 to 0.92, p < 0.0001).

II.F. Statistical analysis

Our algorithm requires three user-interactions—placingcircular region-of-interest at the aortic root, and marking the

FIG. 5. The first row shows an example of extracted centerlines in mid LADand D1. Each column shows an image at different angles with axes perpen-dicular to each other. The second row shows vessel linearization (LAD) ofthe first row in three orthogonal directions. Both rows show the same loca-tion of a lesion (25%–49% stenosis by expert visual grading). The outline(third row) shows segmented lumen using our method.

origin of the left main and the right coronary artery. The per-formance of our algorithm was examined where two differentreaders ran the program independently with the three interac-tions on all the cases. The results of the reproducibility eval-uation are shown in the result. The agreement of the lesiondetection results derived from the proposed automated lesiondetection software was measured using the kappa statistics, aswell as sensitivity, specificity, and receiver-operator charac-teristic (ROC) analysis, with Analyse-it software.62 A p-value< 0.05 was considered statistically significant.

III. RESULTS

We tested the algorithm on 42 consecutive patients[26 male]. The mean age was 60 ± 12 yr and the mean bodyweight was 83 ± 10 kg. About 21 patients had coronary le-sions with stenosis greater than or equal to 25%. In these pa-tients, 45 lesions with stenosis ≥25% were identified. Eightout of the remaining 21 patients had lesions with stenosis<25% and 13 patients did not have any lesions (no luminalstenosis or plaque). In the 45 lesions with stenosis ≥25%, 20lesions were obstructive (≥50%).

The proposed algorithm ran successfully (Figs. 5–7) on allthe proximal and mid coronary artery segments in all patients,with an execution time of around 50 s for centerline extractionfor all coronary arteries and <2 s for all subsequent steps ona standard 2.5 GHz personal computer running windows XP.

TABLE I. Performance characteristics of the proposed algorithm of lesion (≥25% stenosis) detection in N = 42 patients (13 complete normal). In a total of45 lesions with ≥25% stenosis, 6 were of severe stenosis (≥70%), and 14 were obstructive stenosis (≥50%). Sensitivity was 93%, specificity was 81%, andaccuracy was 83% per segment.

Lesions found by Expert (≥25% stenosis) True lesions found by algorithm Sensitivity (%) Specificity (%) Accuracy (%)

Per segment LAD 24 21 88 83 85LCX 10 10 100 80 82RCA 11 11 100 81 83

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FIG. 6. Example of lumen segmentation and lesion detection in LAD. Range of proximal LAD lesion (stenosis 25%–49%) marked by expert is shown as boxesat around x = 0 mm – 38 mm and 53 mm – 68 mm. Lumen diameters computed from the segmented lumen are shown and their cropped lumen diameters byanatomical knowledge are also shown. Expected “normal” luminal diameter is derived from the scan by automated piecewise line fitting between branch points,and takes into account “normal tapering” present in the dataset. The locations of the lesions with ≥25% stenosis detected by the algorithm, concordant with theexpert observer, is marked with vertical arrows. The second vertical line, which is at around x = 60 mm, is the lesion detected additionally by calcium volumemeasurement.

The algorithm was validated by standard 10-fold cross-validation, as described in Ref. 63 by dividing the 42 patientdatasets into 10 subsamples, and training and testing 10 times.In the 45 lesions with stenosis ≥25%, the proposed automatedalgorithm correctly identified 21/24 lesions in the LAD, 10/10in the LCX, and 11/11 in the RCA. Figures 8 and 9 show twopatient examples from our study.

In total, the proposed automated algorithm correctly identi-fied true lesions yielding a sensitivity of 93% (42/45) on a per-segment basis (Table I). Three lesions (25%–49% stenosis)were missed by our approach, when there is no proximal ordistal normal references around the location of stenosis. Also,on a per-segment basis, the proposed algorithm showed 81%specificity, 83% accuracy, 98% negative predictive value, and52% positive predictive value. For obstructive lesions, thesensitivity was 100%, the specificity was 84%, and the ac-curacy was 86%.

FIG. 7. An example of 3D volume rendering (a), detected nonobstructivelesion (mixed plaque) by stenosis calculation (CCTA image) (b), and detectednonobstructive lesion (mixed plaque) by calcium volume measurement at abranch point (CCTA image) (c). Arrows in (a)–(c) indicate the locations ofthe detected lesions (25%–49% stenosis). This patient did not undergo ICA.

FIG. 8. Detection of lesion with stenosis. Arrows indicate the location oflesions. Detected lesions with stenosis by primarily noncalcified plaque in themid segment (the first row, 59% stenosis by quantitative analysis and 50%–69% stenosis by expert visual grading). The second row shows accordingICA images of the first row (36.2% stenosis by quantitative analysis).

FIG. 9. Detection of lesion with stenosis. Arrows indicate the location oflesions. Detected lesions with stenosis by mixed plaque in the proximal seg-ment (the first row, 70% stenosis by quantitative analysis and 90%–99%stenosis by expert visual grading). The second row shows according ICAimages of the first row (62.4% stenosis by quantitative analysis).

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TABLE II. Breakdown of detected false positive lesions.

LAD LCX RCA

Stenosis <25% by expert 9 6 8Normal segment with narrowing [nostenosis or plaque (normal variant)]

2 1 2

Undetected small vessel branch 2 2 5Unclear image contrast + blurring 0 2 0

There were 39 false positive detections (Table II) in the252 coronary artery segments of 42 patients by the proposedalgorithm resulting in an average of 0.15 per segment; the rea-sons for these false positives are described in Table II. Twentythree false positives out of 39 (59%) were lesions with <25%stenosis as assessed by the expert observer (Fig. 10). Whenexcluding the 23 detections that were lesions having steno-sis <25% and considering only the detections that were notrelated with stenotic atherosclerosis as false positives, speci-ficity was increased to 92% and the average false positive ra-tio was 0.06 per segment. Out of the remaining 16 false pos-itive detection marks, five cases were associated with normalsegment with narrowing without plaque, nine cases were as-sociated with undetected small branches, and two cases wereassociated with unclear image contrast and blurring.

Additionally, 10 out of our 42 patients underwent ICA,using the Inova digital x-ray system from GE Healthcarewith multiple views of the left and right coronary artery toidentify the projection in which the segment appeared moststenotic. Standard cardiac catheterization technique was em-ployed. Acquired images were transferred to an AGFA Heart-lab workstation for quantitative coronary catheter angiogra-phy (QCA) analysis. Ten cases were interpreted for steno-sis by consensus of two experienced readers, and a separatedinvestigator independently performed QCA for all 10 cases.Reference luminal proximal and distal positions to the steno-sis were defined by readers, and then QCA software on theworkstation detected luminal edges, located site of maximalstenosis, and quantified the maximal stenosis64 (Figs. 8 and9). Based on ICA-based QCA, 10 out of 10 patients had steno-sis ≥25%, in agreement with the results of our algorithm onCCTA (Figs. 8 and 9).

III.A. Program reproducibility

There was very good agreement between two independentreaders for running the lesion detection software, with an ob-

FIG. 10. An example of “false positive” lesion. The algorithm detected thislocation as a lesion with stenosis ≥25%, but expert readers graded it <25%stenosis.

TABLE III. Program reproducibility. Results when different readers ran theprogram.

Sensitivity Specificity ROC-AUC

Program (reader 1) 93% 81% 0.87Program (reader 2) 93% 80% 0.87

served agreement of 94.8% (kappa 0.89, 95% confidence in-terval 0.83 to 0.95, p < 0.0001). For comparison, this pro-gram agreement was comparable to agreement of the sec-ond blinded reader with the reference standard (agreement94.8%, kappa 0.84, 95% confidence interval 0.75 to 0.92,p < 0.0001).

Table III shows the sensitivity, specificity, and ROC-area-under-the-curve (ROC-AUC) for the two readers running theprogram, compared to the reference standard. The ROC-AUCwas 0.87 for both readers running the program.

IV. DISCUSSION

Our results demonstrate high agreement with three expertreaders in consensus in a sizable patient population (42 pa-tients), and the algorithm was validated by standard 10-foldcross-validation. The algorithm showed a sensitivity of 93%and a negative predictive value of 98% on a per-segment basisin the main three coronary arteries, which is the most desir-able. Our specificity was relatively low (81%) due to 39 ad-ditional detections on a per-segments basis, which could bemanually rejected, as in clinically used software for coronarycalcium scoring.65, 66 From a clinical standpoint, it is essen-tial for the computer-aided system to have a high sensitivitysince the false positive results can be discarded by one-button-click. By identifying all potential lesions, the system wouldaid the physician by quickly identifying all lesions. Further-more, plaque burden could be measured automatically overall identified lesions.48, 49

IV.A. Previous studies

Our proposed study is in line with the few previousstudies attempting automated lesion detection from CCTA.Halpern42 and Arnoldi40 published validation papers usingcommercial software with expert human interpretation, wherethey detected obstructive lesions only (with ≥50% steno-sis). Dinesh41 proposed a method, which utilized manual cen-terlines and artery classification, and did not provide spe-cific stenosis calculation, and were evaluated with the smallnumber of patients (eight patients). Recently, Kelm43 andGoldberg44 also published automated detection of obstructive(≥50% stenosis) coronary artery lesions from CCTA.

One of the main advances as compared to previously pub-lished work is that our method can accurately detect bothobstructive and nonobstructive lesions (25%–49% stenosis),whereas previous studies detected obstructive lesions only(≥50% stenosis). This is of particular clinical value sincelesions with nonobstructive stenosis have been shown to

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contribute to cardiovascular events.45, 46 Nonobstructive le-sions (25%–49%) are more challenging to detect due to thesubtle narrowing of the lumen. Additionally, compared toother previous methods that detected only obstructive lesions,our proposed algorithm also identified arterial branch pointsto improve automated detection of lesions.

Our automated software showed high reproducibility whenrun by two independent observers (94.8% agreement). Thesmall interobserver variability was due to the three manualuser-interactions; setting two ostial points and setting a ROI inaorta. Those manual interactions produced slightly differentlumen segmentation and estimated normal luminal referencediameters, resulting in different stenosis calculations. Auto-matic algorithms have been previously proposed for thesesteps50 and these could be combined with our lesion detec-tion technique. The remaining processes were all automatic.

There were a few limitations in our study. Invasive coro-nary angiography was not performed for all patients. In ourstudy, the reference standard was clinically utilized visualdetection and grading of lesions, by three expert readers inconsensus. However, stenosis calculation by expert readerswas not available. The algorithm was not fully automated,but required three mouse clicks to define ostial locations andaortic region-of-interest. A binary mask was used as a firststep for centerline extraction. However, the algorithm can becombined with existing well-established commercial softwareperforming lumen segmentation, to provide clinical tools forlesion detection. Reduced specificity due to the additional le-sions found by the software poses a challenge. Currently, onlythe major proximal and mid branches are classified. Auto-mated or semiautomated labeling of all coronary brancheswould potentially reduce the number of false positives. Forrobust performance, even in CCTA datasets with fair imagequality, our algorithm used only the lumen for automatedidentification of lesions; however, assessment of the vesselwall in combination with lumen may improve our lesion de-tection results, and this needs to be further evaluated. Lesionswith stenosis ≥25% only were considered in our study; wehave, however, also evaluated the results for the detection oflesions with stenosis ≥50%.

V. CONCLUSION

In conclusion, we developed a novel automated algorithmfor detection and localization of obstructive and nonobstruc-tive arterial lesions from CCTA, which performed with highsensitivity compared to three experienced expert readers.

ACKNOWLEDGMENTS

The authors wish to thank Mr. Amit Ramesh for his helpwith the software and Mr. David Choi for help with dataanonymization. This study was supported by AHA Grant No.09GRNT2330000 (PI: Damini Dey).

a)Author to whom correspondence should be addressed. Electronic mail:Damini.Dey@cshs.org

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