Deep learning-based intravascular ultrasound segmentation for the assessment of coronary artery disease

Nishi, Takeshi; Yamashita, Rikiya; Imura, Shinji; Tateishi, Kazuya; Kitahara, Hideki; Kobayashi, Yoshio; Yock, Paul G.; Fitzgerald, Peter J.; Honda, Yasuhiro

doi:10.1016/j.ijcard.2021.03.020

Cited by 32 publications

(22 citation statements)

References 8 publications

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“…IVUS, by allowing direct visualization of vessel architecture, can help in the earlier identification and management of these complications. Nishi et al developed a ML model to compute the luminal area and the vessel area accurately, as well as the stent area, which exhibited an excellent correlation between ML-derived and expert-derived dimensions while dramatically reducing the time required for segmentation of IVUS images (37 s) compared with expert analysis (30 h) [ 145 ].…”

Section: Artificial Intelligence In the Field Of Intracoronary Imagingmentioning

confidence: 99%

Current and Future Applications of Artificial Intelligence in Coronary Artery Disease

et al. 2022

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Cardiovascular diseases (CVDs) carry significant morbidity and mortality and are associated with substantial economic burden on healthcare systems around the world. Coronary artery disease, as one disease entity under the CVDs umbrella, had a prevalence of 7.2% among adults in the United States and incurred a financial burden of 360 billion US dollars in the years 2016–2017. The introduction of artificial intelligence (AI) and machine learning over the last two decades has unlocked new dimensions in the field of cardiovascular medicine. From automatic interpretations of heart rhythm disorders via smartwatches, to assisting in complex decision-making, AI has quickly expanded its realms in medicine and has demonstrated itself as a promising tool in helping clinicians guide treatment decisions. Understanding complex genetic interactions and developing clinical risk prediction models, advanced cardiac imaging, and improving mortality outcomes are just a few areas where AI has been applied in the domain of coronary artery disease. Through this review, we sought to summarize the advances in AI relating to coronary artery disease, current limitations, and future perspectives.

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Section: Artificial Intelligence In the Field Of Intracoronary Imagingmentioning

confidence: 99%

Current and Future Applications of Artificial Intelligence in Coronary Artery Disease

et al. 2022

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“…Classification results utilizing fine-tuned networks compete with human expert performance [ 25 ]. Recent research has focused on applying deep learning techniques to segment retinal optical coherence tomography (OCT) images [ 26 , 27 , 28 ]. Combining CNN and graph search methods, OCT retinal images are segmented.…”

Section: Literature Reviewmentioning

confidence: 99%

“…CNN was initially demonstrated in medical image analysis in the work of [ 23 ] for lung nodule diagnosis. Numerous medical imaging techniques are based on this concept [ 24 , 25 , 26 , 27 ]. Using a pre-trained network as a feature generator and fine-tuning a pre-trained network to categorize medical pictures are two strategies to transmit the information stored in the pre-trained CNNs.…”

Section: Introductionmentioning

confidence: 99%

Detecting Coronary Artery Disease from Computed Tomography Images Using a Deep Learning Technique

2022

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In recent times, coronary artery disease (CAD) has become one of the leading causes of morbidity and mortality across the globe. Diagnosing the presence and severity of CAD in individuals is essential for choosing the best course of treatment. Presently, computed tomography (CT) provides high spatial resolution images of the heart and coronary arteries in a short period. On the other hand, there are many challenges in analyzing cardiac CT scans for signs of CAD. Research studies apply machine learning (ML) for high accuracy and consistent performance to overcome the limitations. It allows excellent visualization of the coronary arteries with high spatial resolution. Convolutional neural networks (CNN) are widely applied in medical image processing to identify diseases. However, there is a demand for efficient feature extraction to enhance the performance of ML techniques. The feature extraction process is one of the factors in improving ML techniques’ efficiency. Thus, the study intends to develop a method to detect CAD from CT angiography images. It proposes a feature extraction method and a CNN model for detecting the CAD in minimum time with optimal accuracy. Two datasets are utilized to evaluate the performance of the proposed model. The present work is unique in applying a feature extraction model with CNN for CAD detection. The experimental analysis shows that the proposed method achieves 99.2% and 98.73% prediction accuracy, with F1 scores of 98.95 and 98.82 for benchmark datasets. In addition, the outcome suggests that the proposed CNN model achieves the area under the receiver operating characteristic and precision-recall curve of 0.92 and 0.96, 0.91 and 0.90 for datasets 1 and 2, respectively. The findings highlight that the performance of the proposed feature extraction and CNN model is superior to the existing models.

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“…51 Neural networks in particular have been utilized with promising results to address many challenges specific to ultrasound images, 52 including segmentation of images containing speckle and distinguishing between low-contrast lesions. 51,[53][54][55][56][57][58][59][60][61] Separately from segmentation, neural nets have been used in the context of ultrasound imaging for other tasks including beamforming, [62][63][64][65][66][67] super-resolution imaging, 68 dealiasing of high-velocity blood flow, 69 and sub-Nyquist sampling. 70,71 While many classification approaches operate on the intensity information alone and/or at fixed scales, deep learning operates on low-, mid-, and high-level features at any spatial scale.…”

Section: Introductionmentioning

confidence: 99%

“…51,53,73 Neural net-based classifications have been demonstrated in a variety of ultrasound applications, including classification of tissue types in side-viewing intravascular ultrasound. 54,55 Deep learning segmentation has been used for wall thickness measurements, 56 plaque detection, 53,54,57,58 plaque vulnerability measurements, 59 localization of the external elastic membrane, 51 and wall compression measurements. 60 Alternatively, the goal of this study is to develop and demonstrate the initial feasibility of automated segmentation in occluded arteries using a forward-viewing, guidewire-based imaging system.…”

Section: Introductionmentioning

confidence: 99%

Supervised segmentation for guiding peripheral revascularization with forward‐viewing, robotically steered ultrasound guidewire

et al. 2023

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Background Approximately 500 000 patients present with critical limb ischemia (CLI) each year in the U.S., requiring revascularization to avoid amputation. While peripheral arteries can be revascularized via minimally invasive procedures, 25% of cases with chronic total occlusions are unsuccessful due to inability to route the guidewire beyond the proximal occlusion. Improvements to guidewire navigation would lead to limb salvage in a greater number of patients. Purpose Integrating ultrasound imaging into the guidewire could enable direct visualization of routes for guidewire advancement. In order to navigate a robotically‐steerable guidewire with integrated imaging beyond a chronic occlusion proximal to the symptomatic lesion for revascularization, acquired ultrasound images must be segmented to visualize the path for guidewire advancement. Methods The first approach for automated segmentation of viable paths through occlusions in peripheral arteries is demonstrated in simulations and experimentally‐acquired data with a forward‐viewing, robotically‐steered guidewire imaging system. B‐mode ultrasound images formed via synthetic aperture focusing (SAF) were segmented using a supervised approach (U‐net architecture). A total of 2500 simulated images were used to train the classifier to distinguish the vessel wall and occlusion from viable paths for guidewire advancement. First, the size of the synthetic aperture resulting in the highest classification performance was determined in simulations (90 test images) and compared with traditional classifiers (global thresholding, local adaptive thresholding, and hierarchical classification). Next, classification performance as a function of the diameter of the remaining lumen (0.5 to 1.5 mm) in the partially‐occluded artery was tested using both simulated (60 test images at each of 7 diameters) and experimental data sets. Experimental test data sets were acquired in four 3D‐printed phantoms from human anatomy and six ex vivo porcine arteries. Accuracy of classifying the path through the artery was evaluated using microcomputed tomography of phantoms and ex vivo arteries as a ground truth for comparison. Results An aperture size of 3.8 mm resulted in the best‐performing classification based on sensitivity and Jaccard index, with a significant increase in Jaccard index (p < 0.05) as aperture diameter increased. In comparing the performance of the supervised classifier and traditional classification strategies with simulated test data, sensitivity and F1 score for U‐net were 0.95 ± 0.02 and 0.96 ± 0.01, respectively, compared to 0.83 ± 0.03 and 0.41 ± 0.13 for the best‐performing conventional approach, hierarchical classification. In simulated test images, sensitivity (p < 0.05) and Jaccard index both increased with increasing artery diameter (p < 0.05). Classification of images acquired in artery phantoms with remaining lumen diameters ≥ 0.75 mm resulted in accuracies > 90%, while mean accuracy decreased to 82% when artery diameter decreased to 0.5 mm. For testing i...

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Deep learning-based intravascular ultrasound segmentation for the assessment of coronary artery disease

Cited by 32 publications

References 8 publications

Current and Future Applications of Artificial Intelligence in Coronary Artery Disease

Current and Future Applications of Artificial Intelligence in Coronary Artery Disease

Detecting Coronary Artery Disease from Computed Tomography Images Using a Deep Learning Technique

Supervised segmentation for guiding peripheral revascularization with forward‐viewing, robotically steered ultrasound guidewire

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