Automated coronary artery atherosclerosis detection and weakly supervised localization on coronary CT angiography with a deep 3-dimensional convolutional neural network

Candemir, Sema; White, Richard D.; Demirer, Mutlu; Gupta, Vikash; Bigelow, Matthew T.; Prevedello, Luciano M.; Erdal, Barbaros S.

doi:10.1016/j.compmedimag.2020.101721

Cited by 44 publications

(39 citation statements)

References 30 publications

(40 reference statements)

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“…The use of multiple CNN models sequentially (predicting minimum distance map by CNN, merging tracklets with CNN features, then CNN based regression on polar patches along the artery centerlines) mimics the human behavior in vessel wall review, thus this CNN analysis system is not a black box and easily understandable. However, if the CNN model is trained end-to-end directly for classification of vascular diseases from images [41], [42], prediction results are not easily explainable and errors are not clearly identifiable, especially for challenging images. In this paper, we focused on determining whether accurate and fully automated vessel wall analysis based on polar regression was feasible as a proof-of-concept.…”

Section: Discussionmentioning

confidence: 99%

Automated Artery Localization and Vessel Wall Segmentation Using Tracklet Refinement and Polar Conversion

et al. 2020

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Quantitative analysis of blood vessel wall structures is important to study atherosclerotic diseases and assess cardiovascular event risks. To achieve this, accurate identification of vessel luminal and outer wall contours is needed. Computer-assisted tools exist, but manual preprocessing steps, such as region of interest identification and/or boundary initialization, are still needed. In addition, prior knowledge of the ring shape of vessel walls has not been fully explored in designing segmentation methods. In this work, a fully automated artery localization and vessel wall segmentation system is proposed. A tracklet refinement algorithm was adapted to robustly identify the artery of interest from a neural network-based artery centerline identification architecture. Image patches were extracted from the centerlines and converted in a polar coordinate system for vessel wall segmentation. The segmentation method used 3D polar information and overcame problems such as contour discontinuity, complex vessel geometry, and interference from neighboring vessels. Verified by a large (>32000 images) carotid artery dataset collected from multiple sites, the proposed system was shown to better automatically segment the vessel wall than traditional vessel wall segmentation methods or standard convolutional neural network approaches. In addition, a segmentation uncertainty score was estimated to effectively identify slices likely to have errors and prompt manual confirmation of the segmentation. This robust vessel wall segmentation system has applications in different vascular beds and will facilitate vessel wall feature extraction and cardiovascular risk assessment.

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Section: Discussionmentioning

confidence: 99%

Automated Artery Localization and Vessel Wall Segmentation Using Tracklet Refinement and Polar Conversion

et al. 2020

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“…Several limitations underlie these findings: (a) single-center designs with a small sample size, (b) CCTA data were acquired from only 1 or 2 types of CT scanners and (c) biases caused by excluding segments with poor image quality (21,24). Several other DLbased automated algorithms have proven useful in distinguishing stenotic coronary arteries (25,26). However, these results were obtained by using human readers' outcomes rather than ICA as standard references.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic Accuracy and Generalizability of a Deep Learning-Based Fully Automated Algorithm for Coronary Artery Stenosis Detection on CCTA: A Multi-Centre Registry Study

Luo

et al. 2021

Front. Cardiovasc. Med.

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Aims: In this retrospective, multi-center study, we aimed to estimate the diagnostic accuracy and generalizability of an established deep learning (DL)-based fully automated algorithm in detecting coronary stenosis on coronary computed tomography angiography (CCTA).Methods and results: A total of 527 patients (33.0% female, mean age: 62.2 ± 10.2 years) with suspected coronary artery disease (CAD) who underwent CCTA and invasive coronary angiography (ICA) were enrolled from 27 hospitals from January 2016 to August 2019. Using ICA as a standard reference, the diagnostic accuracy of the DL algorithm in the detection of ≥50% stenosis was compared to that of expert readers. In the vessel-based evaluation, the DL algorithm had a higher sensitivity (65.7%) and negative predictive value (NPV) (78.8%) and a significantly higher area under the curve (AUC) (0.83, p < 0.001). In the patient-based evaluation, the DL algorithm achieved a higher sensitivity (90.0%), NPV (52.2%) and AUC (0.81). Generalizability analysis of the DL algorithm was conducted by comparing its diagnostic performance in subgroups stratified by sex, age, geographic area and CT scanner type. The AUCs of the DL algorithm in the aforementioned subgroups ranged from 0.79 to 0.86 and from 0.75 to 0.93 in the vessel-based and patient-based evaluations, both without significant group differences (p > 0.05). The DL algorithm significantly reduced post-processing time (160 [IQR:139–192] seconds), in comparison to manual work (p < 0.001).Conclusions: The DL algorithm performed no inferior to expert readers in CAD diagnosis on CCTA and had good generalizability and time efficiency.

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“…As described above, IVUS, IVOCT, and CCTA are the imaging modalities that can be used to characterize coronary atherosclerotic plaques. Research and development of CAD tools for plaque characterization have relied on image datasets from private [ 66 , 67 , 68 ] or public sources [ 69 , 70 , 71 , 72 , 73 ]. Some of the latter are available only upon request [ 25 , 72 ].…”

Section: Artificial Intelligence (Ai): Characterization Of Plaquementioning

confidence: 99%

Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization

Gudigar

Nayak

Samanth

et al. 2021

IJERPH

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Coronary artery disease is a major cause of morbidity and mortality worldwide. Its underlying histopathology is the atherosclerotic plaque, which comprises lipid, fibrous and—when chronic—calcium components. Intravascular ultrasound (IVUS) and intravascular optical coherence tomography (IVOCT) performed during invasive coronary angiography are reference standards for characterizing the atherosclerotic plaque. Fine image spatial resolution attainable with contemporary coronary computed tomographic angiography (CCTA) has enabled noninvasive plaque assessment, including identifying features associated with vulnerable plaques known to presage acute coronary events. Manual interpretation of IVUS, IVOCT and CCTA images demands scarce physician expertise and high time cost. This has motivated recent research into and development of artificial intelligence (AI)-assisted methods for image processing, feature extraction, plaque identification and characterization. We performed parallel searches of the medical and technical literature from 1995 to 2021 focusing respectively on human plaque characterization using various imaging modalities and the use of AI-assisted computer aided diagnosis (CAD) to detect and classify atherosclerotic plaques, including their composition and the presence of high-risk features denoting vulnerable plaques. A total of 122 publications were selected for evaluation and the analysis was summarized in terms of data sources, methods—machine versus deep learning—and performance metrics. Trends in AI-assisted plaque characterization are detailed and prospective research challenges discussed. Future directions for the development of accurate and efficient CAD systems to characterize plaque noninvasively using CCTA are proposed.

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Automated coronary artery atherosclerosis detection and weakly supervised localization on coronary CT angiography with a deep 3-dimensional convolutional neural network

Cited by 44 publications

References 30 publications

Automated Artery Localization and Vessel Wall Segmentation Using Tracklet Refinement and Polar Conversion

Automated Artery Localization and Vessel Wall Segmentation Using Tracklet Refinement and Polar Conversion

Diagnostic Accuracy and Generalizability of a Deep Learning-Based Fully Automated Algorithm for Coronary Artery Stenosis Detection on CCTA: A Multi-Centre Registry Study

Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization

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