Learning-based needle tip tracking in 2D ultrasound by fusing visual tracking and motion prediction

Yan, Wanquan; Ding, Qingpeng; Chen, Jianghua; Yan, Kim; Tang, Raymond; Cheng, Shing Shin

doi:10.1016/j.media.2023.102847

Cited by 9 publications

(1 citation statement)

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“…Yan et al [ 93 ] utilized a transformer-based network for motion prediction in their needle tip tracking system. This approach helped them estimate the target’s current position from its past position data, addressing the issue of the target’s temporary disappearance.…”

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%