Výsledky vyhledávání - "cardiac segmentation"

Akademický článek

3D augmentation for volumetric whole heart segmentation

Autor: Krittanat Sutassananon, Worapan Kusakunniran, Mehmet Orgun, Thanongchai Siriapisith

Publikováno v: Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)

Abstract Data augmentation is a technique usually deployed to mitigate the possible performance limitation from training a neural network model on a limited dataset, especially in the medical domain. This paper presents a study on effects of applying

Externí odkaz: https://doaj.org/article/f51f7ad046d54b8ca0649cd095fddd8e

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Akademický článek

DAFNet: A dual attention-guided fuzzy network for cardiac MRI segmentation

Autor: Yuxin Luo, Yu Fang, Guofei Zeng, Yibin Lu, Li Du, Lisha Nie, Pu-Yeh Wu, Dechuan Zhang, Longling Fan

Publikováno v: AIMS Mathematics, Vol 9, Iss 4, Pp 8814-8833 (2024)

Background: In clinical diagnostics, magnetic resonance imaging (MRI) technology plays a crucial role in the recognition of cardiac regions, serving as a pivotal tool to assist physicians in diagnosing cardiac diseases. Despite the notable success o

Externí odkaz: https://doaj.org/article/a05e4d0e950241ff9fe69222dc08ad3c

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Akademický článek

Toward Robust Cardiac Segmentation Using Graph Convolutional Networks

Autor: Gilles Van De Vyver, Sarina Thomas, Guy Ben-Yosef, Sindre Hellum Olaisen, Havard Dalen, Lasse Lovstakken, Erik Smistad

Publikováno v: IEEE Access, Vol 12, Pp 33876-33888 (2024)

Fully automatic cardiac segmentation can be a fast and reproducible method to extract clinical measurements from an echocardiography examination. The U-Net architecture is the current state-of-the-art deep learning architecture for medical segmentati

Externí odkaz: https://doaj.org/article/95877fa9bb5740bf9e221a3d19d9c19c

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Akademický článek

A Novel Multi-Dimensional Joint Search Method for the Compression of Medical Image Segmentation Models

Autor: Yunhui Zheng, Zhiyong Wu, Fengna Ji, Lei Du, Zhenyu Yang

Publikováno v: Journal of Imaging, Vol 10, Iss 9, p 206 (2024)

Due to the excellent results achieved by transformers in computer vision, more and more scholars have introduced transformers into the field of medical image segmentation. However, the use of transformers will make the model’s parameters very large

Externí odkaz: https://doaj.org/article/612e327dd8b34ad0b622058fead36bb2

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Akademický článek

A Multi-Stage Automatic Method Based on a Combination of Fully Convolutional Networks for Cardiac Segmentation in Short-Axis MRI

Autor: Italo Francyles Santos da Silva, Aristófanes Corrêa Silva, Anselmo Cardoso de Paiva, Marcelo Gattass, António Manuel Cunha

Publikováno v: Applied Sciences, Vol 14, Iss 16, p 7352 (2024)

Magnetic resonance imaging (MRI) is a non-invasive technique used in cardiac diagnosis. Using it, specialists can measure the masses and volumes of the right ventricle (RV), left ventricular cavity (LVC), and myocardium (MYO). Segmenting these struct

Externí odkaz: https://doaj.org/article/37e36707c502468cbe7b16c2a677a831

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Akademický článek

MSMHSA-DeepLab V3+: An Effective Multi-Scale, Multi-Head Self-Attention Network for Dual-Modality Cardiac Medical Image Segmentation

Autor: Bo Chen, Yongbo Li, Jiacheng Liu, Fei Yang, Lei Zhang

Publikováno v: Journal of Imaging, Vol 10, Iss 6, p 135 (2024)

The automatic segmentation of cardiac computed tomography (CT) and magnetic resonance imaging (MRI) plays a pivotal role in the prevention and treatment of cardiovascular diseases. In this study, we propose an efficient network based on the multi-sca

Externí odkaz: https://doaj.org/article/147d5893313843d3b593a4adf9687a9d

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Akademický článek

Cardiovascular magnetic resonance images with susceptibility artifacts: artificial intelligence with spatial-attention for ventricular volumes and mass assessment

Autor: Marco Penso, Mario Babbaro, Sara Moccia, Marco Guglielmo, Maria Ludovica Carerj, Carlo Maria Giacari, Mattia Chiesa, Riccardo Maragna, Mark G. Rabbat, Andrea Barison, Nicola Martini, Mauro Pepi, Enrico G. Caiani, Gianluca Pontone

Publikováno v: Journal of Cardiovascular Magnetic Resonance, Vol 24, Iss 1, Pp 1-14 (2022)

Abstract Background Segmentation of cardiovascular magnetic resonance (CMR) images is an essential step for evaluating dimensional and functional ventricular parameters as ejection fraction (EF) but may be limited by artifacts, which represent the ma

Externí odkaz: https://doaj.org/article/9ca7c5b138ee4f09907c31f05b846d13

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Akademický článek

Automated T1 and T2 mapping segmentation on cardiovascular magnetic resonance imaging using deep learning

Autor: András Kalapos, Liliána Szabó, Zsófia Dohy, Máté Kiss, Béla Merkely, Bálint Gyires-Tóth, Hajnalka Vágó

Publikováno v: Frontiers in Cardiovascular Medicine, Vol 10 (2023)

IntroductionStructural and functional heart abnormalities can be examined non-invasively with cardiac magnetic resonance imaging (CMR). Thanks to the development of MR devices, diagnostic scans can capture more and more relevant information about pos

Externí odkaz: https://doaj.org/article/1e85203c93ac4279bef951a2275b3bf0

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