Coronary angiography video segmentation method for assisting cardiovascular disease interventional treatment

Liang, Dongxue; Qiu, Jing; Wang, Lu; Yin, Xiaolei; Xing, Junhui; Yang, Zhiyun; Dong, Jiangzeng; Ma, Zhaoyuan

doi:10.1186/s12880-020-00460-9

Cited by 16 publications

(13 citation statements)

References 29 publications

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“…With the development of artificial intelligence, deep learning technology has been widely used in medical image processing and analysis in recent years, and the accuracy of segmentation and classification on medical images is of great significance to the diagnosis of diseases today. In clinical practice, accurate image segmentation can provide clinicians with quantitative information, which can help clinicians make diagnostic decisions more precisely and efficiently (Liang et al, 2020 ). In addition, the additional information provided by computing methods is subjective and can avoid the objective bias by humans.…”

Section: Introductionmentioning

confidence: 99%

O-Net: A Novel Framework With Deep Fusion of CNN and Transformer for Simultaneous Segmentation and Classification

et al. 2022

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The application of deep learning in the medical field has continuously made huge breakthroughs in recent years. Based on convolutional neural network (CNN), the U-Net framework has become the benchmark of the medical image segmentation task. However, this framework cannot fully learn global information and remote semantic information. The transformer structure has been demonstrated to capture global information relatively better than the U-Net, but the ability to learn local information is not as good as CNN. Therefore, we propose a novel network referred to as the O-Net, which combines the advantages of CNN and transformer to fully use both the global and the local information for improving medical image segmentation and classification. In the encoder part of our proposed O-Net framework, we combine the CNN and the Swin Transformer to acquire both global and local contextual features. In the decoder part, the results of the Swin Transformer and the CNN blocks are fused to get the final results. We have evaluated the proposed network on the synapse multi-organ CT dataset and the ISIC 2017 challenge dataset for the segmentation task. The classification network is simultaneously trained by using the encoder weights of the segmentation network. The experimental results show that our proposed O-Net achieves superior segmentation performance than state-of-the-art approaches, and the segmentation results are beneficial for improving the accuracy of the classification task. The codes and models of this study are available at https://github.com/ortonwang/O-Net.

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Section: Introductionmentioning

confidence: 99%

O-Net: A Novel Framework With Deep Fusion of CNN and Transformer for Simultaneous Segmentation and Classification

et al. 2022

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“…To help improve the clinical diagnosis of these cardiovascular heart diseases, deep learning techniques are used, which are being widely used for the analysis of medical images. Thanks to this computational paradigm, it is possible to know if a patient has stenosis and, more importantly, where it is present [5], [6]. Information that is very valuable to place the stent in the right place [7].…”

Section: Output Reconstructionmentioning

confidence: 99%

Stenosis detection in coronary angiography images using deep learning models

Luque‐Baena

Granados

Jiménez-Partinen

et al. 2022

2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAI

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The emergence of deep learning has caused its massive application to different fields in industry and research, among which is the clinical field, especially in those where the data is structured in the form of images or video. The present proposal intends to develop a coronary angiography image analysis system based on artificial intelligence. These images are radiocontrast X-ray images of the coronary arteries. The proposed system will be able to analyze these coronary angiography images of patients with no obstructive coronary lesions to detect and characterize smooth and irregular coronary arteries and predict the presence of cardiovascular events during follow-up. Deep learning convolutional artificial neural networks will support the algorithmic basis of the proposed system.

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“…The application of artificial intelligence (AI) to coronary angiography (CAG) has only been ascertained in very few medical/biology publications [1][2][3][4]. While the possibilities of such an approach are vast, the first step is arguably to produce accurate segmentation of CAGs, i.e., clearly identifying the coronary tree while excluding other structures.…”

Section: Introductionmentioning

confidence: 99%

Coronary X-ray angiography segmentation using Artificial Intelligence: a multicentric validation study of a deep learning model

Menezes

Silva

et al. 2023

Int J Cardiovasc Imaging

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Introduction We previously developed an artificial intelligence (AI) model for automatic coronary angiography (CAG) segmentation, using deep learning. To validate this approach, the model was applied to a new dataset and results are reported. Methods Retrospective selection of patients undergoing CAG and percutaneous coronary intervention or invasive physiology assessment over a one month period from four centers. A single frame was selected from images containing a lesion with a 50–99% stenosis (visual estimation). Automatic Quantitative Coronary Analysis (QCA) was performed with a validated software. Images were then segmented by the AI model. Lesion diameters, area overlap [based on true positive (TP) and true negative (TN) pixels] and a global segmentation score (GSS – 0 -100 points) - previously developed and published - were measured. Results 123 regions of interest from 117 images across 90 patients were included. There were no significant differences between lesion diameter, percentage diameter stenosis and distal border diameter between the original/segmented images. There was a statistically significant albeit minor difference [0,19 mm (0,09–0,28)] regarding proximal border diameter. Overlap accuracy ((TP + TN)/(TP + TN + FP + FN)), sensitivity (TP / (TP + FN)) and Dice Score (2TP / (2TP + FN + FP)) between original/segmented images was 99,9%, 95,1% and 94,8%, respectively. The GSS was 92 (87–96), similar to the previously obtained value in the training dataset. Conclusion the AI model was capable of accurate CAG segmentation across multiple performance metrics, when applied to a multicentric validation dataset. This paves the way for future research on its clinical uses.

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Coronary angiography video segmentation method for assisting cardiovascular disease interventional treatment

Cited by 16 publications

References 29 publications

O-Net: A Novel Framework With Deep Fusion of CNN and Transformer for Simultaneous Segmentation and Classification

O-Net: A Novel Framework With Deep Fusion of CNN and Transformer for Simultaneous Segmentation and Classification

Stenosis detection in coronary angiography images using deep learning models

Coronary X-ray angiography segmentation using Artificial Intelligence: a multicentric validation study of a deep learning model

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