Enhancement of Needle Tip and Shaft from 2D Ultrasound Using Signal Transmission Maps

Mwikirize, Cosmas; Nosher, John L.; Hacihaliloglu, Ilker

doi:10.1007/978-3-319-46720-7_42

Cited by 7 publications

(10 citation statements)

References 10 publications

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“…The resulting images were optimized by a likelihood map with shape measurement. Similarly, automatically optimized Gabor filter methods [39], [40], [41], [42], [43] were used with different image processing steps for needle segmentation in 2D US. Specifically, Kaya et al [39], [40], [41] proposed to employ a two-stage method for needle localization based on Gabor filtering with an optimized insertion angle estimation.…”

Section: ) Parametric Space Methodsmentioning

confidence: 99%

“…The needle is finally detected by a modified Maximum Likelihood Estimation SAmple Consensus (MLESAC) method [44]. Furthermore, Mwikirize et al [43] proposed to localize the needle by introducing signal transmission maps for 2D US, which firstly enhances the visibility of the needle in noisy US images. Then, the needle is localized by applying the algorithm from Hacihaliloglu et al [42].…”

Section: ) Parametric Space Methodsmentioning

confidence: 99%

“…To visualize the needle during the US-guided regional anesthesia or blockade, an essential constraint should be satisfied in conventional 2D US: the needle should be positioned inplane in 2D images, where the needle is visualized as a bright line, requiring a perfect alignment between instrument and ultrasound plane [43]. However, this 2D US-guided therapy is facing the challenges of the instrument being invisible [64], or the instrument being out of the plane [58].…”

Section: A Us-guided Anesthesia-based Interventionmentioning

confidence: 99%

“…Needle biopsy/anesthesia/therapy ex-vivo Kaya et al [41] 2015 2D+t Needle biopsy/drug delivery in-vitro Pourtaherian et al [37] 2016 3D Needle anesthesia/ablation ex-vivo Beigi et al [45] 2016 2D+t Needle biopsy/nerve block/anesthesias in-vitro/in-vivo Mwikirize et al [43] 2016 2D Needle biopsy/ablation/anesthesia ex-vivo Beigi et al [48] 2016 2D+t Needle biopsy/nerve block/anesthesia in-vivo Kaya et al [46] 2016 2D+t Needle biopsy/drug delivery in-vitro Daoud et al [49] 2018 3D Needle intervention ex-vivo Daoud et al [24] 2018 2D Needle intervention ex-vivo Agarwal et al [35] 2019 2D+t Anesthesia/biopsy/brachytherapy in-vitro Needle biopsy/ablation/anesthesia ex-vivo Yang et al [61] 2019 3D Cardiac catheterization in-vitro/ex-vivo/in-vivo Yang et al [70] 2019 3D Cardiac catheterization ex-vivo Yang et al [82] 2019 3D Cardiac catheterization ex-vivo Yang et al [83] 2019 3D Cardiac catheterization ex-vivo Mwikirize et al [89] 2019 2D+t Needle biopsy/anesthesia in-vitro/ex-vivo Mwikirize et al [88] 2019 2D+t Needle biopsy/anesthesia ex-vivo Arif et al [87] 2019 3D Needle biopsy in-vitro/in-vivo Yang et al [85] 2019 3D Cardiac catheterization ex-vivo/in-vivo Min et al [76] 2020 3D Cardiac catheterization ex-vivo Rodgers et al [80] 2020 2D/3D Interstitial gynecologic brachytherapy in-vitro/in-vivo Zhang et al [68], [67] 2020 3D prostate brachytherapy in-vivo Zhang et al [86] 2020 3D Prostate brachytherapy in-vivo Zhang et al [90] 2020 3D Prostate brachytherapy in-vivo Lee et al [79] 2020 2D Needle biopsy in-vivo With the above summaries, there are remaining some challenges and limitations for this area, despite the current methods obtain satisfactory results. We discuss these challenges below.…”

Section: Referencementioning

confidence: 99%

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Medical Instrument Detection in Ultrasound-Guided Interventions: A Review

Yang¹,

Shan²,

Kolen³

2020

Preprint

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Medical instrument detection is essential for computer assisted interventions, since it would facilitate the surgeons to find the instrument efficiently with a better interpretation, which leads to a better outcome. This article reviews medical instrument detection methods in ultrasound-guided intervention. First, we present a comprehensive review for instrument detection methodologies, which include traditional non-datadriven methods and data-driven methods. The non-data-driven methods were extensive studied prior to the era of machine learning, i.e. data-driven approaches. We discuss the main clinical applications of medical instrument detection in ultrasound, including anesthesia, biopsy, prostate brachytherapy and cardiac catheterization, which were validated on clinical datasets. Finally, we selected several principal publications to summarize the key issues and potential research directions for the computer assisted intervention community.

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Section: ) Parametric Space Methodsmentioning

confidence: 99%

Section: ) Parametric Space Methodsmentioning

confidence: 99%

Section: A Us-guided Anesthesia-based Interventionmentioning

confidence: 99%

Section: Referencementioning

confidence: 99%

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Medical Instrument Detection in Ultrasound-Guided Interventions: A Review

Yang¹,

Shan²,

Kolen³

2020

Preprint

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“…In static analysis, the frames are investigated independently of time, assuming that the needle shaft is seen as a line-like brighter intensity in the images. There are several traditional methods to extract lines in US frames using statistical analysis of image intensity [12], hough transform [13]- [16], projections [17]- [21], random sample consensus(RANSAC) [22]- [24] [25], filtering [10], [26]- [28] and radon transform [29], [30]. Log-Gabor filters have also been used to extract phase images of needle projections.…”

mentioning

confidence: 99%

Spatiotemporal analysis of speckle dynamics to track invisible needle in ultrasound sequences using Convolutional Neural Networks

Zade

Aziz

Majedi

et al. 2022

Preprint

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ObjectiveAccurate needle placement to the target point is critical for ultrasound interventions like biopsies and epidural injections. However, aligning the needle to the thin plane of the transducer is a challenging issue as it leads to the decay of visibility by the naked eye. Therefore, we have developed a CNN-based framework to track the needle using the spatiotemporal features of speckle dynamics.MethodsThere are three key techniques to optimize the network for our application. First, we proposed a motion field estimation network (RMF) to extract spatiotemporal features from the stack of consecutive frames. We also designed an efficient network based on the state-of-the-art Yolo framework (nYolo). Lastly, the Assisted Excitation (AE) module was added at the neck of the network to handle imbalance problem.ResultsTen freehand ultrasound sequences are collected by inserting an injection needle steeply into the Ultrasound Compatible Lumbar Epidural Simulator and Femoral Vascular Access Ezono test phantoms. We divided the dataset into two sub-categories. In the second category, in which the situation is more challenging and the needle is totally invisible statically, the angle and tip localization error were 2.43±1.14° and 2.3±1.76 mm using Yolov3+RMF+AE and 2.08±1.18° and 2.12±1.43 mm using nYolo+RMF+AE.Conclusion and significanceThe proposed method has the potential to track the needle in a more reliable operation compared to other state-of-the-art methods and can accurately localize it in 2D B-mode US images in real-time, allowing it to be used in in current ultrasound intervention procedures.

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