Coronary Arteries In X-ray Angiograms Research Articles

A Lightweight Network for Accurate Coronary Artery Segmentation Using X-Ray Angiograms.

An accurate and automated segmentation of coronary arteries in X-ray angiograms is essential for cardiologists to diagnose coronary artery disease in clinics. The existing deep learning-based coronary arteries segmentation models focus on using complex networks to improve the accuracy of segmentation while ignoring the computational cost. However, performing such segmentation networks requires a high-performance device with a powerful GPU and a high bandwidth memory. To address this issue, in this study, a lightweight deep learning network is developed for a better balance between computational cost and segmentation accuracy. We have made two efforts in designing the network. On the one hand, we adopt bottleneck residual blocks to replace the internal components in the encoder and decoder of the traditional U-Net to make the network more lightweight. On the other hand, we embed the two attention modules to model long-range dependencies in spatial and channel dimensions for the accuracy of segmentation. In addition, we employ Top-hat transforms and contrast-limited adaptive histogram equalization (CLAHE) as the pre-processing strategy to enhance the coronary arteries to further improve the accuracy. Experimental evaluations conducted on the coronary angiograms dataset show that the proposed lightweight network performs well for accurate coronary artery segmentation, achieving the sensitivity, specificity, accuracy, and area under the curve (AUC) of 0.8770, 0.9789, 0.9729, and 0.9910, respectively. It is noteworthy that the proposed network contains only 0.75 M of parameters, which achieves the best performance by the comparative experiments with popular segmentation networks (such as U-Net with 31.04 M of parameters). Experimental results demonstrate that our network can achieve better performance with an extremely low number of parameters. Furthermore, the generalization experiments indicate that our network can accurately segment coronary angiograms from other coronary angiograms' databases, which demonstrates the strong generalization and robustness of our network.

Weakly supervised vessel segmentation in X-ray angiograms by self-paced learning from noisy labels with suggestive annotation

The segmentation of coronary arteries in X-ray angiograms by convolutional neural networks (CNNs) is promising yet limited by the requirement of precisely annotating all pixels in a large number of training images, which is extremely labor-intensive especially for complex coronary trees. To alleviate the burden on the annotator, we propose a novel weakly supervised training framework that learns from noisy pseudo labels generated from automatic vessel enhancement, rather than accurate labels obtained by fully manual annotation. A typical self-paced learning scheme is used to make the training process robust against label noise while challenged by the systematic biases in pseudo labels, thus leading to the decreased performance of CNNs at test time. To solve this problem, we propose an annotation-refining self-paced learning framework (AR-SPL) to correct the potential errors using suggestive annotation. An elaborate model-vesselness uncertainty estimation is also proposed to enable the minimal annotation cost for suggestive annotation, based on not only the CNNs in training but also the geometric features of coronary arteries derived directly from raw data. Experiments show that our proposed framework achieves 1) comparable accuracy to fully supervised learning, which also significantly outperforms other weakly supervised learning frameworks; 2) largely reduced annotation cost, i.e., 75.18% of annotation time is saved, and only 3.46% of image regions are required to be annotated; and 3) an efficient intervention process, leading to superior performance with even fewer manual interactions.

Automatic Segmentation of Coronary Arteries in X-ray Angiograms using Multiscale Analysis and Artificial Neural Networks

This paper presents a novel method for the automatic segmentation of coronary arteries in X-ray angiograms, based on multiscale analysis and neural networks. The multiscale analysis is performed by using Gaussian filters in the spatial domain and Gabor filters in the frequency domain, which are used as inputs by a multilayer perceptron (MLP) for the enhancement of vessel-like structures. The optimal design of the MLP is selected following a statistical comparative analysis, using a training set of 100 angiograms, and the area under the ROC curve ( A z ) for assessment of the detection performance. The detection results of the proposed method are compared with eleven state-of-the-art blood vessel enhancement methods, obtaining the highest performance of A z = 0.9775 , with a test set of 30 angiograms. The database of 130 X-ray coronary angiograms has been outlined by a specialist and approved by a medical ethics committee. On the other hand, the vessel extraction technique was selected from fourteen binary classification algorithms applied to the multiscale filter response. Finally, the proposed segmentation method is compared with twelve state-of-the-art vessel segmentation methods in terms of six binary evaluation metrics, where the proposed method provided the most accurate coronary arteries segmentation with a classification rate of 0.9698 and Dice coefficient of 0.6857 , using the test set of angiograms. In addition to the experimental results, the performance in the detection and segmentation steps of the proposed method have also shown that it can be highly suitable for systems that perform computer-aided diagnosis in X-ray imaging.

A Novel Multiscale Gaussian-Matched Filter Using Neural Networks for the Segmentation of X-Ray Coronary Angiograms.

The accurate and efficient segmentation of coronary arteries in X-ray angiograms represents an essential task for computer-aided diagnosis. This paper presents a new multiscale Gaussian-matched filter (MGMF) based on artificial neural networks. The proposed method consists of two different stages. In the first stage, MGMF is used for detecting vessel-like structures while reducing image noise. The results of MGMF are compared with those obtained using six GMF-based detection methods in terms of the area (Az) under the receiver operating characteristic (ROC) curve. In the second stage, ten thresholding methods of the state of the art are compared in order to classify the magnitude of the multiscale Gaussian response into vessel and nonvessel pixels, respectively. The accuracy measure is used to analyze the segmentation methods, by comparing the results with a set of 100 X-ray coronary angiograms, which were outlined by a specialist to form the ground truth. Finally, the proposed method is compared with seven state-of-the-art vessel segmentation methods. The vessel detection results using the proposed MGMF method achieved an Az = 0.9357 with a training set of 50 angiograms and Az = 0.9362 with the test set of 50 images. In addition, the segmentation results using the intraclass variance thresholding method provided a segmentation accuracy of 0.9568 with the test set of coronary angiograms.

Multichannel Fully Convolutional Network for Coronary Artery Segmentation in X-Ray Angiograms

Accurate segmentation of coronary arteries in X-ray angiograms is an important step for the quantitative study of coronary artery disease. However, accurate segmentation is a challenging task because coronary arteries are thin tubular structures with relatively low contrast and the presence of artifacts. In this paper, a novel deep-learning-based method is proposed to automatically segment the coronary artery from angiograms by using multichannel fully convolutional networks. Since the artifacts appear in both live images (after the injection of contrast material) and mask images (before the injection of contrast material) and the blood vessels appear only in live images, we take the mask images into consideration to distinguish real blood vessel structures from artifacts. Therefore, both live images and mask images are used as multichannel inputs to provide enhanced vascular structure information. The hierarchical features are then automatically learned to characterize the spatial associations between vessel and background and are further used to achieve the final segmentation. In addition, a dense matching between the live image and mask image is processed for a precise initial alignment. The experimental results demonstrate that our method is effective and robust for coronary artery segmentation, compared with several state-of-the-art methods.

Coronary artery segmentation in X-ray angiograms using gabor filters and differential evolution

Segmentation of coronary arteries in X-ray angiograms represents an essential task for computer-aided diagnosis, since it can help cardiologists in diagnosing and monitoring vascular abnormalities. Due to the main disadvantages of the X-ray angiograms are the nonuniform illumination, and the weak contrast between blood vessels and image background, different vessel enhancement methods have been introduced. In this paper, a novel method for blood vessel enhancement based on Gabor filters tuned using the optimization strategy of Differential evolution (DE) is proposed. Because the Gabor filters are governed by three different parameters, the optimal selection of those parameters is highly desirable in order to maximize the vessel detection rate while reducing the computational cost of the training stage. To obtain the optimal set of parameters for the Gabor filters, the area (Az) under the receiver operating characteristics curve is used as objective function. In the experimental results, the proposed method achieves an Az=0.9388 in a training set of 40 images, and for a test set of 40 images it obtains the highest performance with an Az=0.9538 compared with six state-of-the-art vessel detection methods. Finally, the proposed method achieves an accuracy of 0.9423 for vessel segmentation using the test set. In addition, the experimental results have also shown that the proposed method can be highly suitable for clinical decision support in terms of computational time and vessel segmentation performance.

A novel Gaussian matched filter based on entropy minimization for automatic segmentation of coronary angiograms

This paper presents a new method for automatic detection and segmentation of coronary arteries in X-ray angiograms. In the vessel detection stage, a novel Gaussian matched filter (GMF) based on an entropy minimization fitness function is used to detect blood vessels in angiographic images. The detection results of the proposed Gaussian matched filter are compared with those obtained by five state-of-the-art GMF-based methods using the area (Az) under the receiver operating characteristic (ROC) curve. In the second stage, the inter-class variance thresholding method has proven to be the most efficient compared with six different methods in order to classify vessel and non vessel pixels from the Gaussian filter response using the accuracy measure and the ground-truth angiograms drawn by a specialist. Finally, the proposed method is compared with eight state-of-the-art vessel segmentation methods. Due to the high rating of similarity (0.97) between the highest Az value and the Az value acquired by the fitness function over the whole dataset of angiograms, the result of vessel detection using the proposed GMF demonstrated high performance achieving Az=0.945 with a test set of 45 angiograms. In addition, the results of vessel segmentation with the inter-class variance thresholding method provided an accuracy of 0.961 with the test set of angiograms.

Automatic segmentation of coronary arteries using Gabor filters and thresholding based on multiobjective optimization

Abstract This paper presents a new method consisting of two stages for automatic detection and segmentation of coronary arteries in X-ray angiograms. In the first stage, multiscale Gabor filters are used to detect vessel structures in the angiograms. The results of multiscale Gabor filtering are compared with those obtained by applying multiscale methods based on the top-hat operator, Hessian matrix, and Gaussian matched filters. The performance of the vessel-detection methods is evaluated through the area ( A z ) under the receiver operating characteristic (ROC) curve. In the second stage, coronary arteries are segmented by binarizing the magnitude response of Gabor filters using a new thresholding method based on multiobjective optimization, which is compared with seven thresholding methods. Measures of sensitivity, specificity, accuracy, and positive predictive value are used to analyze the segmentation methods, by comparing the results to the ground-truth markings of the vessels drawn by a specialist. Finally, the proposed method is compared with seven state-of-the-art vessel segmentation methods. The result of vessel detection using multiscale Gabor filters demonstrated high accuracy with A z = 0.961 with a training set of 40 angiograms and A z = 0.952 with an independent test set of 40 angiograms. The results of vessel segmentation with the multiobjective thresholding method provided an average accuracy of 0.881 with the test set of angiograms.

Simulating coronary arteries in x-ray angiograms.

Clinical validation of quantitative coronary angiography (QCA) algorithms is difficult due to the lack of a simple alternative method for accurately measuring in vivo vessel dimensions. We address this problem by embedding simulated coronary artery segments with known geometry in clinical angiograms. Our vessel model accounts for the profile of the vessel, x-ray attenuation in the original background, and noise in the imaging system. We have compared diameter measurements of our computer simulated arteries with measurements of an x-ray Telescopic-Shaped Phantom (XTSP) with the same diameters. The results show that for both uniform and anthropomorphic backgrounds there is good agreement in the measured diameters of XTSP compared to the simulated arteries (Pearson's correlation coefficient 0.99). In addition, the difference in accuracy and precision of the true diameter measures compared to the XTSP and simulated artery diameters was small (mean absolute error across all diameters was < or = 0.11 mm +/- 0.09 mm).

Model-guided labeling of coronary structure.

Assigning anatomic labels to coronary arteries in X-ray angiograms is an important task in medical imaging, motivated by the desire to standardize the assessment of coronary artery disease and to facilitate the three-dimensional (3-D) reconstruction and visualization of the coronary vasculature. However, automatic labeling poses a number of significant challenges, including the presence of noise, artifacts, competing structures, misleading visual cues, and other difficulties associated with a dynamic and inherently complex structure. We have developed a model-guided approach that addresses these challenges and automatically labels the vascular structure in coronary angiographic images. The approach consists of two models: 1) a symbolic model, represented through a directed acyclic graph, that captures vascular tree hierarchies and branch interrelationships and 2) a generalized 3-D model that captures spatial and geometric relationships. Importantly, the approach detects ambiguities (such as vessel overlaps) that may be found in a frame of a ciné sequence, and resolves these ambiguities by considering the information derived from other (unambiguous) frames in the temporal sequence, employing dynamic programming methods to match the image features found in the different (ambiguous and unambiguous) frames. This paper presents this model-guided labeling algorithm and discusses the experimental results obtained from implementing and applying the resulting labeling system to a variety of clinical images. The results indicate the feasibility of achieving robust and consistently accurate image labeling through this model-guided, temporal disambiguation method.