Improving Whole-Heart CT Image Segmentation by Attention Mechanism

Wang, Wei; Ye, Chengqin; Zhang, Shanzhuo; Xu, Yong; Wang, Kuanquan

doi:10.1109/access.2019.2961410

Cited by 13 publications

(6 citation statements)

References 28 publications

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“…Ye et al [82] incorporated multi-depth fusion with 3D U-Net to better extract context information, reaching a 96.7% DSC. Wang et al [83] introduced different modified attention mechanisms to lead 3D U-Nets to focus on more salient information. The joint attention gate (AG) and U-CliqueNet (UCNet) modules showed the best performance for aorta segmentation, with a 96.8% DSC.…”

Section: Aorta Segmentation Using Deep Learningmentioning

confidence: 99%

Latest Developments in Adapting Deep Learning for Assessing TAVR Procedures and Outcomes

Tahir,

Mutlu,

Bensaali

et al. 2023

JCM

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Aortic valve defects are among the most prevalent clinical conditions. A severely damaged or non-functioning aortic valve is commonly replaced with a bioprosthetic heart valve (BHV) via the transcatheter aortic valve replacement (TAVR) procedure. Accurate pre-operative planning is crucial for a successful TAVR outcome. Assessment of computational fluid dynamics (CFD), finite element analysis (FEA), and fluid–solid interaction (FSI) analysis offer a solution that has been increasingly utilized to evaluate BHV mechanics and dynamics. However, the high computational costs and the complex operation of computational modeling hinder its application. Recent advancements in the deep learning (DL) domain can offer a real-time surrogate that can render hemodynamic parameters in a few seconds, thus guiding clinicians to select the optimal treatment option. Herein, we provide a comprehensive review of classical computational modeling approaches, medical imaging, and DL approaches for planning and outcome assessment of TAVR. Particularly, we focus on DL approaches in previous studies, highlighting the utilized datasets, deployed DL models, and achieved results. We emphasize the critical challenges and recommend several future directions for innovative researchers to tackle. Finally, an end-to-end smart DL framework is outlined for real-time assessment and recommendation of the best BHV design for TAVR. Ultimately, deploying such a framework in future studies will support clinicians in minimizing risks during TAVR therapy planning and will help in improving patient care.

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Section: Aorta Segmentation Using Deep Learningmentioning

confidence: 99%

Latest Developments in Adapting Deep Learning for Assessing TAVR Procedures and Outcomes

Tahir,

Mutlu,

Bensaali

et al. 2023

JCM

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“…A negative mining technique is used in this model [ 35 ] to suppress the uninterested area. First, the number of negative sample examples N s for each training sample was estimated using Equation (19).…”

Section: Application Of Modified Unetmentioning

confidence: 99%

Review of Semantic Segmentation of Medical Images Using Modified Architectures of UNET

AnbuDevi

Suganthi

2022

Diagnostics

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In biomedical image analysis, information about the location and appearance of tumors and lesions is indispensable to aid doctors in treating and identifying the severity of diseases. Therefore, it is essential to segment the tumors and lesions. MRI, CT, PET, ultrasound, and X-ray are the different imaging systems to obtain this information. The well-known semantic segmentation technique is used in medical image analysis to identify and label regions of images. The semantic segmentation aims to divide the images into regions with comparable characteristics, including intensity, homogeneity, and texture. UNET is the deep learning network that segments the critical features. However, UNETs basic architecture cannot accurately segment complex MRI images. This review introduces the modified and improved models of UNET suitable for increasing segmentation accuracy.

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“…To enhance model accuracy, we propose the integration of effective feature extraction methods [ 3 ], attention mechanisms [ 4 ], and multi-scale fusion strategies [ 5 ]. Additionally, we introduce a novel polarized self-attention strategy to further improve spatial and channel features, enhancing the segmentation model’s performance.…”

Section: Introductionmentioning

confidence: 99%

Optimizing cardiovascular image segmentation through integrated hierarchical features and attention mechanisms

Liao,

Wang,

Lin

2024

THC

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BACKGROUND: Cardiovascular diseases are the top cause of death in China. Manual segmentation of cardiovascular images, prone to errors, demands an automated, rapid, and precise solution for clinical diagnosis. OBJECTIVE: The paper highlights deep learning in automatic cardiovascular image segmentation, efficiently identifying pixel regions of interest for auxiliary diagnosis and research in cardiovascular diseases. METHODS: In our study, we introduce innovative Region Weighted Fusion (RWF) and Shape Feature Refinement (SFR) modules, utilizing polarized self-attention for significant performance improvement in multiscale feature integration and shape fine-tuning. The RWF module includes reshaping, weight computation, and feature fusion, enhancing high-resolution attention computation and reducing information loss. Model optimization through loss functions offers a more reliable solution for cardiovascular medical image processing. RESULTS: Our method excels in segmentation accuracy, emphasizing the vital role of the RWF module. It demonstrates outstanding performance in cardiovascular image segmentation, potentially raising clinical practice standards. CONCLUSIONS: Our method ensures reliable medical image processing, guiding cardiovascular segmentation for future advancements in practical healthcare and contributing scientifically to enhanced disease diagnosis and treatment.

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Improving Whole-Heart CT Image Segmentation by Attention Mechanism

Cited by 13 publications

References 28 publications

Latest Developments in Adapting Deep Learning for Assessing TAVR Procedures and Outcomes

Latest Developments in Adapting Deep Learning for Assessing TAVR Procedures and Outcomes

Review of Semantic Segmentation of Medical Images Using Modified Architectures of UNET

Optimizing cardiovascular image segmentation through integrated hierarchical features and attention mechanisms

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