Enhanced needle localization in ultrasound using beam steering and learning-based segmentation

Hatt, Charles R.; Ng, Gary C.; Parthasarathy, V.

doi:10.1016/j.compmedimag.2014.06.016

Cited by 46 publications

(45 citation statements)

References 34 publications

(42 reference statements)

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“…Most assume that the needle exhibits the brightest pixels in the image; furthermore, they identify linear structures (Radon and Hough transform), filter‐specific transitions of bright and dark pixels (Gabor filter) and can eliminate outliers based on statistical probability (Kalman and RANSAC). In 2D scanning, detection of the needle angle allows the US beam to be perfectly steered for needle visibility enhancement . If combined with 3D scanning, automated needle detection algorithms do not require perfect alignment of needle and probe, addressing the problem of unintentional probe manipulation during interventions.…”

Section: Resultsmentioning

confidence: 99%

Improving needle tip identification during ultrasound-guided procedures in anaesthetic practice

et al. 2017

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Ultrasound guidance is becoming standard practice for needle-based interventions in anaesthetic practice, such as vascular access and peripheral nerve blocks. However, difficulties in aligning the needle and the transducer can lead to incorrect identification of the needle tip, possibly damaging structures not visible on the ultrasound screen. Additional techniques specifically developed to aid alignment of needle and probe or identification of the needle tip are now available. In this scoping review, advantages and limitations of the following categories of those solutions are presented: needle guides; alterations to needle or needle tip; three- and four-dimensional ultrasound; magnetism, electromagnetic or GPS systems; optical tracking; augmented (virtual) reality; robotic assistance; and automated (computerised) needle detection. Most evidence originates from phantom studies, case reports and series, with few randomised clinical trials. Improved first-pass success and reduced performance time are the most frequently cited benefits, whereas the need for additional and often expensive hardware is the greatest limitation to widespread adoption. Novice ultrasound users seem to benefit most and great potential lies in education. Future research should focus on reporting relevant clinical parameters to learn which technique will benefit patients most in terms of success and safety.

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Section: Resultsmentioning

confidence: 99%

Improving needle tip identification during ultrasound-guided procedures in anaesthetic practice

et al. 2017

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“…In recent work, Hatt et al introduced a machine-learning approach to segment the needle in beamsteered B-mode images with the needle orientation known a priori. It uses a classifier to segment pixels as needle or background, followed by a Radon transform for localization [9]. With these approaches however, steering to large angles remains a challenge, especially for curvilinear array transducers, and tissue boundaries are also enhanced.…”

Section: Introductionmentioning

confidence: 99%

Spectral analysis of the tremor motion for needle detection in curvilinear ultrasound via spatiotemporal linear sampling

et al. 2016

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“…Transform (e.g., see [18]), Gabor filter (e.g. see [19]), Monogenic Decomposition (e.g. see [20]), or Fractual Analysis (e.g.…”

Section: Introductionmentioning

confidence: 99%

Classification for Liver Diseases Based on Ultrasound Image Texture Features

Xu¹,

Su²,

Chang³

et al. 2018

Preprint

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This paper discusses the computer-aided (CAD) classification between Hepatocellular Carcinoma (HCC), i.e., the most common type of liver cancer, and Liver Abscess, based on ultrasound image texture features and Support Vector Machine (SVM) classifier. Among 79 cases of liver diseases, with 44 cases of HCC and 35 cases of liver abscess, this research extracts 96 features of Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Run-Length Matrix (GLRLM) from the region of interests (ROIs) in ultrasound images. Three feature selection models, i) Sequential Forward Selection, ii) Sequential Backward Selection, and iii) F-score, are adopted to determine the identification of these liver diseases. Finally, the developed system can classify HCC and liver abscess by SVM with the accuracy of 88.875%. The proposed methods can provide diagnostic assistance while distinguishing two kinds of liver diseases by using a CAD system.

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Enhanced needle localization in ultrasound using beam steering and learning-based segmentation

Cited by 46 publications

References 34 publications

Improving needle tip identification during ultrasound-guided procedures in anaesthetic practice

Improving needle tip identification during ultrasound-guided procedures in anaesthetic practice

Spectral analysis of the tremor motion for needle detection in curvilinear ultrasound via spatiotemporal linear sampling

Classification for Liver Diseases Based on Ultrasound Image Texture Features

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