Co-attention spatial transformer network for unsupervised motion tracking and cardiac strain analysis in 3D echocardiography

Ahn, Shawn S.; Ta, Kevinminh; Thorn, Stephanie; Onofrey, John A.; Melvinsdottir, Inga; Lee, Supum; Langdon, Jonathan; Sinusas, Albert J.; Duncan, James S.

doi:10.1016/j.media.2022.102711

Cited by 10 publications

(3 citation statements)

References 35 publications

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“…A Co-Attention Spatial Transformer Network (STN) that exploits interframe correlations to improve left-ventricle motion tracking between ED and ES frames and strain analysis in noisy 3D echocardiography was introduced by Ahn et al [ 67 ]. This method enhances feature extraction through the utilization of feature cross-correlations, drawing inspiration from speckle tracking techniques.…”

Section: Organsmentioning

confidence: 99%

Ultrasound Image Analysis with Vision Transformers—Review

Vafaeezadeh,

Behnam,

Gifani

2024

Diagnostics

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Ultrasound (US) has become a widely used imaging modality in clinical practice, characterized by its rapidly evolving technology, advantages, and unique challenges, such as a low imaging quality and high variability. There is a need to develop advanced automatic US image analysis methods to enhance its diagnostic accuracy and objectivity. Vision transformers, a recent innovation in machine learning, have demonstrated significant potential in various research fields, including general image analysis and computer vision, due to their capacity to process large datasets and learn complex patterns. Their suitability for automatic US image analysis tasks, such as classification, detection, and segmentation, has been recognized. This review provides an introduction to vision transformers and discusses their applications in specific US image analysis tasks, while also addressing the open challenges and potential future trends in their application in medical US image analysis. Vision transformers have shown promise in enhancing the accuracy and efficiency of ultrasound image analysis and are expected to play an increasingly important role in the diagnosis and treatment of medical conditions using ultrasound imaging as technology progresses.

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Section: Organsmentioning

confidence: 99%

Ultrasound Image Analysis with Vision Transformers—Review

Vafaeezadeh,

Behnam,

Gifani

2024

Diagnostics

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“…This represents a significant advancement in medical imaging, particularly in cardiac echocardiography, demonstrating the powerful applications of TNNs in healthcare technology. Going further, Ahn et al [196] introduced the Co-Attention Spatial Transformer Network (CA-STN) for unsupervised motion tracking in 3D echocardiography.…”

Section: Transformersmentioning

confidence: 99%

Cardiac Healthcare Digital Twins Supported by Artificial Intelligence-Based Algorithms and Extended Reality—A Systematic Review

Rudnicka,

Proniewska,

Perkins

et al. 2024

Electronics

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Recently, significant efforts have been made to create Health Digital Twins (HDTs), Digital Twins for clinical applications. Heart modeling is one of the fastest-growing fields, which favors the effective application of HDTs. The clinical application of HDTs will be increasingly widespread in the future of healthcare services and has huge potential to form part of mainstream medicine. However, it requires the development of both models and algorithms for the analysis of medical data, and advances in Artificial Intelligence (AI)-based algorithms have already revolutionized image segmentation processes. Precise segmentation of lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapy. In this systematic review, a brief overview of recent achievements in HDT technologies in the field of cardiology, including interventional cardiology, was conducted. HDTs were studied taking into account the application of Extended Reality (XR) and AI, as well as data security, technical risks, and ethics-related issues. Special emphasis was put on automatic segmentation issues. In this study, 253 literature sources were taken into account. It appears that improvements in data processing will focus on automatic segmentation of medical imaging in addition to three-dimensional (3D) pictures to reconstruct the anatomy of the heart and torso that can be displayed in XR-based devices. This will contribute to the development of effective heart diagnostics. The combination of AI, XR, and an HDT-based solution will help to avoid technical errors and serve as a universal methodology in the development of personalized cardiology. Additionally, we describe potential applications, limitations, and further research directions.

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“…Ta et al [16] introduced a semi-supervised joint network that simultaneously performs LV tracking and segmentation. Additionally, Ahn et al [17] presented a framework based on the attention mechanism for unsupervised motion tracking between end-diastole and end-systole frames and presented a novel temporal constraint for regularization of the deformation field. Cardiac motion's deformation field possesses crucial characteristics like incompressibility, smoothness, and invertibility.…”

Section: Introductionmentioning

confidence: 99%

Learning Fast Diffeomorphic Registration for Cardiac Motion Estimation in 3D Echocardiography

Ghadim,

Azarnoush

2023

Preprint

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Echocardiography is a well-established technique for diagnosing and monitoring cardiovascular diseases. Myocardial regional motion analysis has the potential to offer a comprehensive understanding of cardiac health. Accurate and efficient quantification of cardiac motion is crucial for clinical tasks related to cardiac diagnosis and prognosis. Although various methods have been proposed for cardiac motion tracking, they often suffer from long inference times or require numerous adjustable parameters. In this study, we propose a diffeomorphic registration network (DRN) to take advantage of deep neural networks’ capability for registration purposes: their ability to learn complex representations and predict the desired output in a single step. Diffeomorphic image registration offers distinct advantages, such as generating invertible and topology-preserving deformation fields, which are key attributes in the context of cardiac motion. This approach enhances the accuracy and realism of deformation field estimation. The DRN framework employs a neural network to estimate a stationary velocity field, from which the deformation field is derived through the Scaling and Squaring method. Evaluation of methods on a synthetic echocardiography dataset with ground truth displacement vectors demonstrates the superiority of our approach in terms of accuracy. Importantly, it maintains a practical inference time of 316.63 ms per frame, making it suitable for clinical applications. Notably, DRN trained with segmentation labels achieves the best results, with a mean error of 0.81 mm for global tracking error.

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Co-attention spatial transformer network for unsupervised motion tracking and cardiac strain analysis in 3D echocardiography

Cited by 10 publications

References 35 publications

Ultrasound Image Analysis with Vision Transformers—Review

Ultrasound Image Analysis with Vision Transformers—Review

Cardiac Healthcare Digital Twins Supported by Artificial Intelligence-Based Algorithms and Extended Reality—A Systematic Review

Learning Fast Diffeomorphic Registration for Cardiac Motion Estimation in 3D Echocardiography

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