Vessel extraction in X-ray angiograms using deep learning

Nasr-Esfahani, Ebrahim; Samavi, Shadrokh; Karimi, Nader; Soroushmehr, S. M. Reza; Ward, Kevin R.; Jafari, Mohammad H.; Felfeliyan, Banafshe; Nallamothu, Brahmajee K.; Najarian, Kayvan

doi:10.1109/embc.2016.7590784

Cited by 44 publications

(40 citation statements)

References 11 publications

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“…Coronary artery disease (CAD) is one of the world's leading causes of death, and its incidence is rapidly increasing worldwide [1]- [3]. This disease is caused by the narrowing of blood vessels due to blockage by a plaque consisting of fat, cholesterol, and calcium [1], [2].…”

Section: Introductionmentioning

confidence: 99%

Segmentation of the Main Vessel of the Left Anterior Descending Artery Using Selective Feature Mapping in Coronary Angiography

Kweon

Kim

et al. 2019

IEEE Access

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X-ray angiography, used in the evaluation of coronary artery disease, presents difficulties in the performance of quantitative coronary angiography analysis, by identifying major vessels. These difficulties are due to problems such as non-uniform illumination, low contrast ratio, and the presence of other tissues. Therefore, segmentation of the desired vessels in images containing multiple blood vessels is clinically important. This paper proposes selective feature mapping as a method for segmenting the left anterior descending artery main vessel in coronary angiography images. The proposed method consists of two steps for generating a candidate area of an image and then segmenting it. To generate the candidate area, feature maps that overlap significantly with the area of the ground truth are selected and combined. Segmentation then is performed using a neural network that learns only the ground truth region of the input image. The proposed method consists of eight modules: pre-processing of the angiogram, resizing of ground truth, preprocessing for segmentation, post-processing for segmentation, network, and segmentation network. This method has a precision of about 0.066, recall of 0.091, and an F1 score of 0.094, values which are higher than those generated by the U-Net, one of the conventional techniques. INDEX TERMS Angiogram, convolutional neural networks, deep learning, left anterior descending artery, selective feature map, selective filter, vessel segmentation.

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Section: Introductionmentioning

confidence: 99%

Segmentation of the Main Vessel of the Left Anterior Descending Artery Using Selective Feature Mapping in Coronary Angiography

Kweon

Kim

et al. 2019

IEEE Access

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“…For example, Pranata et al trained computed tomography images of calcaneal fractures using two readily available convolutional neural network structures and finally obtained a classification accuracy of 98% [32]. Nasr-Esfahani et al trained with the CNN model in X-ray angiography images for the purpose of detecting coronary heart disease [33].…”

Section: Related Workmentioning

confidence: 99%

Oil Pipeline Weld Defect Identification System Based on Convolutional Neural Network

2020

KSII TIIS

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The automatic identification and classification of image-based weld defects is a difficult task due to the complex texture of the X-ray images of the weld defect. Several depth learning methods for automatically identifying welds were proposed and tested. In this work, four different depth convolutional neural networks were evaluated and compared on the 1631 image set. The concavity, undercut, bar defects, circular defects, unfused defects and incomplete penetration in the weld image 6 different types of defects are classified. Another contribution of this paper is to train a CNN model "RayNet" for the dataset from scratch. In the experiment part, the parameters of convolution operation are compared and analyzed, in which the experimental part performs a comparative analysis of various parameters in the convolution operation, compares the size of the input image, gives the classification results for each defect, and finally shows the partial feature map during feature extraction with the classification accuracy reaching 96.5%, which is 6.6% higher than the classification accuracy of other existing fine-tuned models, and even improves the classification accuracy compared with the traditional image processing methods, and also proves that the model trained from scratch also has a good performance on small-scale data sets. Our proposed method can assist the evaluators in classifying pipeline welding defects.

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“…Some DL methods have been also applied to X-ray images. For instance, Nasr-Esfahani et al used a CNN to detect vessel regions, a necessary step for coronary artery disease diagnosis (83). Bone age assessment is a common technique to detect growth abnormalities, and currently, it is done manually by comparing the X-ray images from databases.…”

Section: Medical Imagingmentioning

confidence: 99%

Deep Learning in Omics Data Analysis and Precision Medicine

Martorell-Marugán

Tabik

Benhammou

et al. 2019

Computational Biology

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The rise of omics techniques has resulted in an explosion of molecular data in modern biomedical research. Together with information from medical images and clinical data, the field of omics has driven the implementation of personalized medicine. Biomedical and omics datasets are complex and heterogeneous, and extracting meaningful knowledge from this vast amount of information is by far the most important challenge for bioinformatics and machine learning researchers. In this context, there is an increasing interest in the potential of deep learning (DL) methods to create predictive models and to identify complex patterns from these large datasets. This chapter provides an overview of the main applications of DL methods in biomedical research, with focus on omics data analysis and precision medicine applications. DL algorithms and the most popular architectures are introduced first. This is followed by a review of some of the main applications and problems approached by DL in omics data and medical image analysis. Finally, implementations for improving the diagnosis, treatment, and classification of complex diseases are discussed.

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Vessel extraction in X-ray angiograms using deep learning

Cited by 44 publications

References 11 publications

Segmentation of the Main Vessel of the Left Anterior Descending Artery Using Selective Feature Mapping in Coronary Angiography

Segmentation of the Main Vessel of the Left Anterior Descending Artery Using Selective Feature Mapping in Coronary Angiography

Oil Pipeline Weld Defect Identification System Based on Convolutional Neural Network

Deep Learning in Omics Data Analysis and Precision Medicine

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