A Review on Vessel Segmentation of X-Ray Coronary Angiography Images Based on Deep Learning

2021 ◽  
Author(s):  
YUN NING ◽  
JIJUN TONG
Keyword(s):  
Deep Learning ◽  
Coronary Angiography ◽  
Vessel Segmentation ◽  
X Ray

scholarly journals Deep learning segmentation of major vessels in X-ray coronary angiography

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Su Yang ◽  
Jihoon Kweon ◽  
Jae-Hyung Roh ◽  
Jae-Hwan Lee ◽  
Heejun Kang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Coronary Angiography ◽  
Coronary Arteries ◽  
Coronary Vessels ◽  
Quantitative Coronary Angiography ◽  
Vessel Segmentation ◽  
Diagnostic Methods ◽  
X Ray ◽  
Fully Convolutional Networks ◽  
Major Vessel

AbstractX-ray coronary angiography is a primary imaging technique for diagnosing coronary diseases. Although quantitative coronary angiography (QCA) provides morphological information of coronary arteries with objective quantitative measures, considerable training is required to identify the target vessels and understand the tree structure of coronary arteries. Despite the use of computer-aided tools, such as the edge-detection method, manual correction is necessary for accurate segmentation of coronary vessels. In the present study, we proposed a robust method for major vessel segmentation using deep learning models with fully convolutional networks. When angiographic images of 3302 diseased major vessels from 2042 patients were tested, deep learning networks accurately identified and segmented the major vessels in X-ray coronary angiography. The average F1 score reached 0.917, and 93.7% of the images exhibited a high F1 score > 0.8. The most narrowed region at the stenosis was distinctly captured with high connectivity. Robust predictability was validated for the external dataset with different image characteristics. For major vessel segmentation, our approach demonstrated that prediction could be completed in real time with minimal image preprocessing. By applying deep learning segmentation, QCA analysis could be further automated, thereby facilitating the use of QCA-based diagnostic methods.

scholarly journals Comparative Study of Deep Learning Models for Automatic Coronary Stenosis Detection in X-ray Angiography

2020 ◽  
pp. paper75-1-paper75-11
Author(s):  
Viacheslav Danilov ◽  
Olga Gerget ◽  
Kirill Klyshnikov ◽  
Evgeny Ovcharenko ◽  
Alejandro Frangi
Keyword(s):  
Deep Learning ◽  
Coronary Artery ◽  
Coronary Angiography ◽  
Model Complexity ◽  
Validation Dataset ◽  
Learning Models ◽  
Training Time ◽  
X Ray ◽  
Machine Learning Approach ◽  
Artery Disease

The article explores the application of machine learning approach to detect both single-vessel and multivessel coronary artery disease from X-ray angiography. Since the interpretation of coronary angiography images requires interventional cardiologists to have considerable training, our study is aimed at analysing, training, and assessing the potential of the existing object detectors for classifying and detecting coronary artery stenosis using angiographic imaging series. 100 patients who underwent coronary angiography at the Research Institute for Complex Issues of Cardiovascular Diseases were retrospectively enrolled in the study. To automate the medical data analysis, we examined and compared three models (SSD MobileNet V1, Faster-RCNN ResNet-50 V1, FasterRCNN NASNet) with various architecture, network complexity, and a number of weights. To compare developed deep learning models, we used the mean Average Precision (mAP) metric, training time, and inference time. Testing results show that the training/inference time is directly proportional to the model complexity. Thus, Faster-RCNN NASNet demonstrates the slowest inference time. Its mean inference time per one image made up 880 ms. In terms of accuracy, FasterRCNN ResNet-50 V1 demonstrates the highest prediction accuracy. This model has reached the mAP metric of 0.92 on the validation dataset. SSD MobileNet V1 has demonstrated the best inference time with the inference rate of 23 frames per second.

scholarly journals TCT-194 A Fully Automated Classification and Segmentation of X-Ray Coronary Angiography Using Deep Learning Approach

2019 ◽  
Vol 74 (13) ◽  
pp. B193
Author(s):  
Su Yang ◽  
Jihoon Kweon ◽  
Young-Hak Kim ◽  
Jae-Hyung Roh ◽  
Do-Yoon Kang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Coronary Angiography ◽  
Learning Approach ◽  
Automated Classification ◽  
X Ray

scholarly journals Main Coronary Vessel Segmentation Using Deep Learning in Smart Medical

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Zhanchao Xian ◽  
Xiaoqing Wang ◽  
Shaodi Yan ◽  
Dahao Yang ◽  
Junyu Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Coronary Artery ◽  
Automatic Segmentation ◽  
Disease Diagnosis ◽  
Vessel Segmentation ◽  
X Ray ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Main Vessel ◽  
Artery Disease

The automatic segmentation of main vessels on X-ray angiography (XRA) images is of great importance in the smart coronary artery disease diagnosis system. However, existing methods have been developed to this task, but these methods have difficulty in recognizing the coronary artery structure in XRA images. Main vessel segmentation is still a challenging task due to the diversity and small-size region of the vessel in the XRA images. In this study, we propose a robust method for main vessel segmentation by using deep learning architectures with fully convolutional networks. Four deep learning models based on the UNet architecture are evaluated on a clinical dataset, which consists of 3200 X-ray angiography images collected from 1118 patients. Using the precision (Pre), recall (Re), and F1 score (F1) as evaluation metrics, the average Pre, Re, and F1 for main vessel segmentation in the entire experimental dataset is 0.901, 0.898, and 0.900, respectively. 89.8% of the images exhibited a high F1 score >0.8. For the main vessel segmentation in XRA images, our deep learning methods demonstrated that vessels could be segmented in real time with a more optimized implementation, to further facilitate the online diagnosis in smart medical.

Review of Vessel Segmentation and Stenosis classification in X-ray Coronary Angiography

2021 ◽  
Author(s):  
Ying Zhou ◽  
Haopan Guo ◽  
Jiarui Song ◽  
Yan Chen ◽  
Jinjia Wang
Keyword(s):  
Coronary Angiography ◽  
Vessel Segmentation ◽  
X Ray

scholarly journals Analysis of Coronary Angiography Video Interpolation Methods to Reduce X-ray Exposure Frequency Based on Deep Learning

2021 ◽  
Author(s):  
Dong-xue Liang
Keyword(s):  
Deep Learning ◽  
Coronary Angiography ◽  
Coronary Arteries ◽  
Heart Surgery ◽  
Frame Rate ◽  
Video Frame ◽  
X Rays ◽  
Frame Interpolation ◽  
X Ray ◽  
Video Frame Rate

Cardiac coronary angiography is a major technique that assists physicians during interventional heart surgery. Under X-ray irradiation, the physician injects a contrast agent through a catheter and determines the coronary arteries’ state in real time. However, to obtain a more accurate state of the coronary arteries, physicians need to increase the frequency and intensity of X-ray exposure, which will inevitably increase the potential for harm to both the patient and the surgeon. In the work reported here, we use advanced deep learning algorithms to find a method of frame interpolation for coronary angiography videos that reduces the frequency of X-ray exposure by reducing the frame rate of the coronary angiography video, thereby reducing X-ray-induced damage to physicians. We established a new coronary angiography image group dataset containing 95,039 groups of images extracted from 31 videos. Each group includesthree consecutive images, which are used to train the video interpolation network model. We apply six popular frameinterpolation methods to this dataset to confirm that the video frame interpolation technology can reduce the video frame rate and reduce exposure of physicians to X-rays.

scholarly journals Deep learning-based denoising for improved dose efficiency in EDX tomography of nanoparticles

Nanoscale ◽  
2021 ◽  
Author(s):  
Alexander Skorikov ◽  
Wouter Heyvaert ◽  
Wiebke Albrecht ◽  
Daan Pelt ◽  
Sara Bals
Keyword(s):  
Deep Learning ◽  
Electron Tomography ◽  
Energy Dispersive ◽  
Elemental Distribution ◽  
X Ray ◽  
Powerful Approach ◽  
Dose Efficiency

The combination of energy-dispersive X-ray spectroscopy (EDX) and electron tomography is a powerful approach to retrieve the 3D elemental distribution in nanomaterials, providing an unprecedented level of information for complex,...

scholarly journals Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4595
Author(s):  
Parisa Asadi ◽  
Lauren E. Beckingham
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Ct Images ◽  
Ct Imaging ◽  
Learning Method ◽  
Learning Methods ◽  
X Ray ◽  
Machine Learning Methods ◽  
Filtering Techniques

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

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