Optical Flow-Based Tracking of Needles and Needle-Tip Localization Using Circular Hough Transform in Ultrasound Images

Ayvali, Elif; Desai, Jaydev P.

doi:10.1007/s10439-014-1208-0

Cited by 36 publications

(18 citation statements)

References 29 publications

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“…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. Although some are time‐consuming, they detect the needle tip with high accuracy . Interestingly, the same algorithms can be trained to detect other linear structures such as nerves and blood vessels, which allows for the ideal needle trajectory of entry to be calculated for median or femoral nerve blocks .…”

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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“…Image-based approaches for tool guidance, avoiding the requirement for additional equipment, signal processing, and set-up, have been proposed for general tool segmentation in US images, leveraging techniques based on image properties, including projections, [8][9][10][11] random sample consensus (RANSAC), [12][13][14] filtering, 13,[15][16][17] and Hough or Radon transforms, [18][19][20][21][22] or physical properties, such as analyses of motion, [22][23][24][25][26] beam steering, 27 and circular wave generation. 28 Many of these algorithms were developed for 3D US images; [8][9][10][11][12][13][14][15]20,21,23 however, real-time 2D US is the clinical standard for guiding tools during image-guided minimally invasive interventions at most institutions 1,2 and therefore is the focus of our work presented in this study.…”

Section: Introductionmentioning

confidence: 99%

“…28 Many of these algorithms were developed for 3D US images; [8][9][10][11][12][13][14][15]20,21,23 however, real-time 2D US is the clinical standard for guiding tools during image-guided minimally invasive interventions at most institutions 1,2 and therefore is the focus of our work presented in this study. Many of the published approaches were tested only on phantom or ex vivo tissue images, [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][26][27][28] in vivo on an anesthetized porcine model, 20,25 or, in some cases, US images from one clinical application, such as breast biopsy 10,12,18 or nerve block imaging. 27 Therefore, the generalizability of the algorithms to multiple applications, particularly given the idealized nature of phantom conditions, requires further investigation.…”

Section: Introductionmentioning

confidence: 99%

Deep learning segmentation of general interventional tools in two‐dimensional ultrasound images

et al. 2020

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Purpose: Many interventional procedures require the precise placement of needles or therapy applicators (tools) to correctly achieve planned targets for optimal diagnosis or treatment of cancer, typically leveraging the temporal resolution of ultrasound (US) to provide real-time feedback. Identifying tools in two-dimensional (2D) images can often be time-consuming with the precise position difficult to distinguish. We have developed and implemented a deep learning method to segment tools in 2D US images in near real-time for multiple anatomical sites, despite the widely varying appearances across interventional applications. Methods: A U-Net architecture with a Dice similarity coefficient (DSC) loss function was used to perform segmentation on input images resized to 256 × 256 pixels. The U-Net was modified by adding 50% dropouts and the use of transpose convolutions in the decoder section of the network. The proposed approach was trained with 917 images and manual segmentations from prostate/gynecologic brachytherapy, liver ablation, and kidney biopsy/ablation procedures, as well as phantom experiments. Real-time data augmentation was applied to improve generalizability and doubled the dataset for each epoch. Postprocessing to identify the tool tip and trajectory was performed using two different approaches, comparing the largest island with a linear fit to random sample consensus (RAN-SAC) fitting. Results: Comparing predictions from 315 unseen test images to manual segmentations, the overall median [first quartile, third quartile] tip error, angular error, and DSC were 3.5 [1.3, 13.5] mm, 0.8 [0.3, 1.7]°, and 73.3 [56.2, 82.3]%, respectively, following RANSAC postprocessing. The predictions with the lowest median tip and angular errors were observed in the gynecologic images (median tip error: 0.3 mm; median angular error: 0.4°) with the highest errors in the kidney images (median tip error: 10.1 mm; median angular error: 2.9°). The performance on the kidney images was likely due to a reduction in acoustic signal associated with oblique insertions relative to the US probe and the increased number of anatomical interfaces with similar echogenicity. Unprocessed segmentations were performed with a mean time of approximately 50 ms per image. Conclusions: We have demonstrated that our proposed approach can accurately segment tools in 2D US images from multiple anatomical locations and a variety of clinical interventional procedures in near real-time, providing the potential to improve image guidance during a broad range of diagnostic and therapeutic cancer interventions.

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“…Many studies for the guidance of the needle during the insertion operation have been conducted with the help of ultrasound devices, and 2D ultrasound images are quite general to use, especially for the sagittal one (shown in Figure 1). Elif et al proposed to use circular Hough transform to locate the needle tip accurately, even when the imaging is out-of-plane [13]. Kaya et al localized the needle axis and estimated the needle tip by using a Gabor Filter in sagittal US images [14].…”

Section: Introductionmentioning

confidence: 99%

Tip Estimation Method in Phantoms for Curved Needle Using 2D Transverse Ultrasound Images

Song

Liu

et al. 2019

Applied Sciences

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Flexible needles have been widely used in minimally invasive surgeries, especially in percutaneous interventions. Among the interventions, tip position of the curved needle is very important, since it directly affects the success of the surgeries. In this paper, we present a method to estimate the tip position of a long-curved needle by using 2D transverse ultrasound images from a robotic ultrasound system. Ultrasound is first used to detect the cross section of long-flexible needle. A new imaging approach is proposed based on the selection of numbers of pixels with a higher gray level, which can directly remove the lower gray level to highlight the needle. After that, the needle shape tracking method is proposed by combining the image processing with the Kalman filter by using 3D needle positions, which develop a robust needle tracking procedure from 1 mm to 8 mm scan intervals. Shape reconstruction is then achieved using the curve fitting method. Finally, the needle tip position is estimated based on the curve fitting result. Experimental results showed that the estimation error of tip position is less than 1 mm within 4 mm scan intervals. The advantage of the proposed method is that the shape and tip position can be estimated through scanning the needle’s cross sections at intervals along the direction of needle insertion without detecting the tip.

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Optical Flow-Based Tracking of Needles and Needle-Tip Localization Using Circular Hough Transform in Ultrasound Images

Cited by 36 publications

References 29 publications

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

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

Deep learning segmentation of general interventional tools in two‐dimensional ultrasound images

Tip Estimation Method in Phantoms for Curved Needle Using 2D Transverse Ultrasound Images

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