Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms

Schaap, Michiel; Metz, Coert; Walsum, Theo van; Giessen, Alina G. van der; Weustink, Annick C.; Mollet, Nico R.; Bauer, Christian; Bogunović, Hrvoje; Castro, Carlos; Deng, Xiang; Dikici, Engin; O’Donnell, Thomas; Frenay, Michel; Friman, Ola; Hoyos, Marcela Hernández; Kitslaar, Pieter H.; Krissian, Karl; Kühnel, Caroline; Luengo-Oroz, Miguel; Orkisz, Maciej; Smedby, Örjan; Styner, Martin; Szymczak, Andrzej; Tek, Hüseyin; Warfield, Simon K.; Zambal, Sebastian; Zhang, Yong; Krestin, Gabriël P.; Niessen, Wiro J.

doi:10.1016/j.media.2009.06.003

Cited by 310 publications

(266 citation statements)

References 73 publications

(74 reference statements)

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“…While these techniques (Itu et al, 2012;Nickisch et al, 2015) achieved high accuracy and real-time feedback, they strongly depend on the accuracy of coronary artery tree segmentation and its centerline determination, like the method described by Taylor et al (2013). Manual delineation of the coronary artery centerline is a time consuming and cumbersome task, and most of the commercially available software packages occasionally require substantial manual interaction, especially in images with excessive atherosclerotic plaque or imaging artefacts (Schaap et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis

Zreik

Leßmann

Hamersvelt

et al. 2018

Medical Image Analysis

161

122

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In patients with coronary artery stenoses of intermediate severity, the functional significance needs to be determined. Fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA), is most often used in clinical practice. To reduce the number of ICA procedures, we present a method for automatic identification of patients with functionally significant coronary artery stenoses, employing deep learning analysis of the left ventricle (LV) myocardium in rest coronary CT angiography (CCTA).The study includes consecutively acquired CCTA scans of 166 patients who underwent invasive FFR measurements. To identify patients with a functionally significant coronary artery stenosis, analysis is performed in several stages. First, the LV myocardium is segmented using a multiscale convolutional neural network (CNN). To characterize the segmented LV myocardium, it is subsequently encoded using unsupervised convolutional autoencoder (CAE). As ischemic changes are expected to appear locally, the LV myocardium is divided into a number of spatially connected clusters, and statistics of the encodings are computed as features. Thereafter, patients are classified according to the presence of functionally significant stenosis using an SVM classifier based on the extracted features.Quantitative evaluation of LV myocardium segmentation in 20 images resulted in an average Dice coefficient of 0.91 and an average mean absolute distance between the segmented and reference LV boundaries of 0.7 mm. Twenty CCTA images were used to train the LV myocardium encoder. Classification of patients was evaluated in the remaining 126 CCTA scans in 50 10-fold cross-validation experiments and resulted in an area under the receiver operating characteristic curve of 0.74 ± 0.02. At sensitivity levels 0.60, 0.70 and 0.80, the corresponding specificity was 0.77, 0.71 and 0.59, respectively.The results demonstrate that automatic analysis of the LV myocardium in a single CCTA scan acquired at rest, without assessment of the anatomy of the coronary arteries, can be used to identify patients with functionally significant coronary artery stenosis. This might reduce the number of patients undergoing unnecessary invasive FFR measurements.

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Section: Introductionmentioning

confidence: 99%

Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis

Zreik

Leßmann

Hamersvelt

et al. 2018

Medical Image Analysis

161

122

View full text Add to dashboard Cite

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“…Fifteen cardiac CT datasets, with centerlines available in a total of 53 arteries, were obtained from two different sources: Hôpital Louis Pradel (Bron, France) and Rotterdam Coronary Artery Algorithm Evaluation Framework [11], the latter containing data acquired at the Erasmus Medical Center (Rotterdam, The Netherlands). Cross-sections orthogonal to the centerlines were calculated, from which the required features were extracted.…”

Section: Patient Datamentioning

confidence: 99%

Learning from Only Positive and Unlabeled Data to Detect Lesions in Vascular CT Images

Zuluaga

Hush

Leyton

et al. 2011

Lecture Notes in Computer Science

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Abstract. Detecting vascular lesions is an important task in the diagnosis and follow-up of the coronary heart disease. While most existing solutions tackle calcified and non-calcified plaques separately, we present a new algorithm capable of detecting both types of lesions in CT images. It builds up on a semi-supervised classification framework, in which the training set is made of both unlabeled data and a small amount of data labeled as normal. Our method takes advantage of the arrival of newly acquired data to re-train the classifier and improve its performance. We present results on synthetic data and on datasets from 15 patients. With a small amount of labeled training data our method achieved a 89.8% true positive rate, which is comparable to state-of-the-art supervised methods, and the performance can improve after additional iterations.

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“…For Q = 20, with which we obtain the 93.4% on OV, the average computation time is 7.467 seconds. According to [8], the reference standard centerlines are about 138mm on average, so the speed of tracing is 18.5mm/s. The processor used in the experiment is dual core AMD Opteron 2216 (2.4GHz) and the physical memory is 8GB.…”

Section: Experiments and Validationmentioning

confidence: 99%

Minimum Average-Cost Path for Real Time 3D Coronary Artery Segmentation of CT Images

Zhu

Chung

2011

Lecture Notes in Computer Science

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Abstract. In this paper, we propose a Minimum Average-cost Path (MACP) model for segmenting 3D coronary arteries by minimizing the average edge cost along path in discrete 4D graph constructed by image voxels and associated radii. Prim's Minimum Spanning Tree method is used for efficient optimization of the MACP model. The centerline and the radii of the cross sections of the coronary artery are extracted simultaneously during the optimization. The method does not need any image preprocessing steps and has been intensively validated as an effective approach with the Rotterdam Coronary Artery Algorithm Evaluation Framework [1]. The computational cost of the proposed method is particularly low (7.467 seconds per segment, 18.5mm/s on average), which makes real time segmentation of coronary artery possible. Shortcut problem, which is a classic issue of the minimal path techniques, can also be overcome by the proposed method.

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Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms

Cited by 310 publications

References 73 publications

Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis

Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis

Learning from Only Positive and Unlabeled Data to Detect Lesions in Vascular CT Images

Minimum Average-Cost Path for Real Time 3D Coronary Artery Segmentation of CT Images

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