Deep Learning Segmentation in 2D X-ray Images and Non-Rigid Registration in Multi-Modality Images of Coronary Arteries

Park, Tae‐Yong; Khang, Seungwoo; Jeong, Heeryeol; Koo, Kyoyeong; Lee, Jeongjin; Kang, Ho Chul

doi:10.3390/diagnostics12040778

Cited by 13 publications

(3 citation statements)

References 37 publications

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“…Another important reason for frame selection is that it reduces that number of input dimensions, which makes the DNN less likely to overfit. Frame selection is usually performed manually [45], [60], [61] either without any additional modalities or in combination with ECG signals, which are collected synchronously with XCA [62]. In order to automate the frame selection process and thus assist radiologists, several algorithmic approaches have been proposed.…”

Section: Best Frame Selectionmentioning

confidence: 99%

A Review of Modern Approaches for Coronary Angiography Imaging Analysis

Popov¹,

Aimyshev²,

Ismailov³

et al. 2022

Preprint

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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.

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Section: Best Frame Selectionmentioning

confidence: 99%

A Review of Modern Approaches for Coronary Angiography Imaging Analysis

Popov¹,

Aimyshev²,

Ismailov³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…To this end, several registration-based approaches for procedure guidance have been proposed to reduce the uncertainty inherent in 2D X-ray images via feature matching between 2D X-ray images and three-dimensional (3D) CT images [4], [5], [6], [7], [8], [9], [10]. In [4], statistical motion models of coronary arteries based on 4D CT angiography (CTA) were introduced, and a 2D/3D+t coronary artery registration method using motion models based on cardiac and respiratory information was proposed in [5].…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, Pernus et al [6] used intensity gradients of 2D X-ray images to match 3D vascular geometry and Zhu et al [7] introduced a matching method based on iterative closest graphs using coarse-to-refine vessel matching for rigid and non-rigid transformation. Recently, convolutional neural network (CNN) models have been used to extract the central lines of coronary arteries in 2D X-ray images and energy function-based 3D deformation has been utilized for real-time registration [8], [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of Partially Broken Vascular Structures in X-Ray Images via Vesselness-Loss-Based Multi-Scale Generative Adversarial Networks

Han,

Koo,

Jung

et al. 2023

IEEE Access

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Coronary artery procedures are primarily performed based on X-ray angiography images. However, coronary arteries in X-ray images are often partially broken, complicating diagnoses and procedures owing to lack of visibility. In this paper, we propose a fully automatic method to restore locally broken parts of coronary arteries in X-ray images without using any external information, such as computed tomography images. To this end, we design a new multi-scale generative adversarial network and a vesselness-loss function. The proposed method is optimized for focus on elongated structures and can be utilized in various clinical applications. The proposed method is evaluated and compared with four other existing methods using the performance metrics, PSNR, MSE, and SSIM, and the result shows 34.3, 0.18, and 0.91 averages, respectively for each metric. Based on the performance result, the blocked regions are plausibly reconstructed into such original shapes of blood vessels, which can aid in image-based guiding catheter manipulation during coronary artery procedures. Eventually, the proposed method can be utilized in various clinical applications, e.g., image-based planning and guidance of coronary procedures and prior simulation of results.

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Coronary Vessel Segmentation in X-ray Angiography Images Using Edge-Based Tracking Method

Lalinia,

Sahafi

2024

Sens Imaging

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Automated extraction of coronary arteries is an essential process in the diagnosis of treatment for coronary artery disease (CAD) with computer assistance. Accurately outlining the coronary artery is difficult when using X-ray coronary angiography (XCA) because of the low signal-to-noise ratio and the presence of interfering background structures. In this paper, a new approach for segmenting vessels in angiograms is presented, specifically designed to tackle the difficulties arising from non-uniform illumination, artifacts, and noise present in angiographic images. The proposed method employs an edge-based tracking tool to generate an initial probability map for segmentation. A segmentation method based on coronary vessel tracking is presented for finding the border and centerline of the vessel. The proposed method is designed based on two main components: preprocessing and tracking. In the preprocessing stage, a guided filter and edge-sharpening algorithms are used to enhance the features of the original image. In the tracking stage, an initial point is selected, and using the Gaussian property, a semi-circle operator is applied to track the line perpendicular to the vessel. The proposed method demonstrated remarkable performance in terms of sensitivity and specificity, achieving values of 86.93 and 99.61, respectively. Additionally, the method achieved an accuracy rate of 97.81. Notably, the proposed method outperformed existing state-of-the-art segmentation methods, as indicated by its higher dice score. These impressive results signify a significant advancement in the field of vessel segmentation, highlighting the effectiveness and superiority of the proposed approach.

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Deep Learning Segmentation in 2D X-ray Images and Non-Rigid Registration in Multi-Modality Images of Coronary Arteries

Cited by 13 publications

References 37 publications

A Review of Modern Approaches for Coronary Angiography Imaging Analysis

A Review of Modern Approaches for Coronary Angiography Imaging Analysis

Reconstruction of Partially Broken Vascular Structures in X-Ray Images via Vesselness-Loss-Based Multi-Scale Generative Adversarial Networks

Coronary Vessel Segmentation in X-ray Angiography Images Using Edge-Based Tracking Method

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