A Review of Modern Approaches for Coronary Angiography Imaging Analysis

• URL: http://arxiv.org/abs/2209.13997v1
• Date: Wed, 28 Sep 2022 11:12:04 GMT
• Title: A Review of Modern Approaches for Coronary Angiography Imaging Analysis
• Authors: Maxim Popov, Temirgali Aimyshev, Eldar Ismailov, Ablay Bulegenov, Siamac Fazli
• Abstract summary: Coronary Heart Disease (CHD) is a leading cause of death in the modern world. Deep learning-based algorithms, such as segmentation networks and detectors, play an important role in assisting medical professionals by providing timely analysis of a patient's angiograms.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Coronary Heart Disease (CHD) is a leading cause of death in the modern world. The development of modern analytical tools for diagnostics and treatment of CHD is receiving substantial attention from the scientific community. Deep learning-based algorithms, such as segmentation networks and detectors, play an important role in assisting medical professionals by providing timely analysis of a patient's angiograms. This paper focuses on X-Ray Coronary Angiography (XCA), which is considered to be a "gold standard" in the diagnosis and treatment of CHD. First, we describe publicly available datasets of XCA images. Then, classical and modern techniques of image preprocessing are reviewed. In addition, common frame selection techniques are discussed, which are an important factor of input quality and thus model performance. In the following two chapters we discuss modern vessel segmentation and stenosis detection networks and, finally, open problems and current limitations of the current state-of-the-art.

Related papers

• CADICA: a new dataset for coronary artery disease detection by using invasive coronary angiography [1.5404452377809545]
  Coronary artery disease (CAD) remains the leading cause of death globally. Deep learning classification methods are well-developed in other areas of medical imaging. One of the most important reasons is the lack of available and high-quality open-access datasets.
  arXiv  Detail & Related papers  (2024-02-01T13:03:13Z)
• Object Detection for Automated Coronary Artery Using Deep Learning [0.0]
  In our paper, we utilize the object detection method on X-ray angiography images to precisely identify the location of coronary artery stenosis. This model enables automatic and real-time detection of stenosis locations, assisting in the crucial and sensitive decision-making process.
  arXiv  Detail & Related papers  (2023-12-19T13:14:52Z)
• SSASS: Semi-Supervised Approach for Stenosis Segmentation [9.767759441883008]
  The complexity of coronary artery structures combined with the inherent noise in X-ray images poses a considerable challenge to this task. We introduce a semi-supervised approach for cardiovascular stenosis segmentation. Our approach demonstrated an exceptional performance in the Automatic Region-based Coronary Artery Disease diagnostics.
  arXiv  Detail & Related papers  (2023-11-17T02:01:19Z)
• StenUNet: Automatic Stenosis Detection from X-ray Coronary Angiography [5.430434855741553]
  The severity of coronary artery disease (CAD) is quantified by the location, degree of narrowing (stenosis) and number of arteries involved. The MICCAI grand challenge: Automatic Region-based Coronary Artery Disease diagnostics using the X-ray angiography imagEs (ARCADE) curated a dataset with stenosis annotations. We propose the architecture and algorithm StenUNet to accurately detect stenosis from X-ray Coronary Angiography.
  arXiv  Detail & Related papers  (2023-10-23T14:04:18Z)
• A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron [0.24466725954625884]
  The Coronavirus (COVID-19) outbreak in December 2019 has become an ongoing threat to humans worldwide. Deep learning (DL) techniques have proved helpful in analysis and delineation of infectious regions in radiological images in a timely manner. This paper makes an in-depth survey of DL techniques and draws a taxonomy based on diagnostic strategies and learning approaches.
  arXiv  Detail & Related papers  (2022-02-13T17:44:33Z)
• Generative Residual Attention Network for Disease Detection [51.60842580044539]
  We present a novel approach for disease generation in X-rays using a conditional generative adversarial learning. We generate a corresponding radiology image in a target domain while preserving the identity of the patient. We then use the generated X-ray image in the target domain to augment our training to improve the detection performance.
  arXiv  Detail & Related papers  (2021-10-25T14:15:57Z)
• In-Line Image Transformations for Imbalanced, Multiclass Computer Vision Classification of Lung Chest X-Rays [91.3755431537592]
  This study aims to leverage a body of literature in order to apply image transformations that would serve to balance the lack of COVID-19 LCXR data. Deep learning techniques such as convolutional neural networks (CNNs) are able to select features that distinguish between healthy and disease states. This study utilizes a simple CNN architecture for high-performance multiclass LCXR classification at 94 percent accuracy.
  arXiv  Detail & Related papers  (2021-04-06T02:01:43Z)
• Variational Knowledge Distillation for Disease Classification in Chest X-Rays [102.04931207504173]
  We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
  arXiv  Detail & Related papers  (2021-03-19T14:13:56Z)
• Machine Learning Methods for Histopathological Image Analysis: A Review [62.14548392474976]
  Histopathological images (HIs) are the gold standard for evaluating some types of tumors for cancer diagnosis. One of the ways of accelerating such an analysis is to use computer-aided diagnosis (CAD) systems.
  arXiv  Detail & Related papers  (2021-02-07T19:12:32Z)
• Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization [112.2628296775395]
  Clinical decision support using deep neural networks has become a topic of steadily growing interest. clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
  arXiv  Detail & Related papers  (2020-10-09T14:39:27Z)
• Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19 [71.41929762209328]
  The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world. Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19. The recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists.
  arXiv  Detail & Related papers  (2020-04-06T15:21:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.