Real-time Needle Tip Localization in 2D Ultrasound Images using Kalman Filter

Agarwal, Nisha; Yadav, Ankur; Gupta, Arjun; Orlando, M. Felix

doi:10.1109/aim.2019.8868799

Cited by 12 publications

(10 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although these methods may include different pre-/post-processing steps, the core idea of them is to extract tubular-like structures by Hessian matrix analysis for local intensity distributions. Further processing steps range from a simple thresholding [31], [32], [35] to Random sample consensus (RANSAC) model-fitting [34] with Kalman filtering in time sequence-based US datasets [36], [33]. Moreover, the local Hessian matrix is also applied to detect the shadow of steep needles in 3D US [37], which automatically extracts the 2D slice containing the needle for in-plane visualization.…”

Section: ) Parametric Space Methodsmentioning

confidence: 99%

Medical Instrument Detection in Ultrasound-Guided Interventions: A Review

Yang¹,

Shan²,

Kolen³

2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: ) Parametric Space Methodsmentioning

confidence: 99%

Medical Instrument Detection in Ultrasound-Guided Interventions: A Review

Yang¹,

Shan²,

Kolen³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Therefore the next step of image processing involves the computation of its trajectory. A variety of methods have been proposed from a simple selection of the maximal segmented object 17 to more advanced techniques as Kalman filter, 13,22 log-Gabor filter, 18 Gabor filter, 15 or Radon transform. 16 Other needle segmentation approaches and the datasets, accompanied by the obtained segmentation results, are described in Tables 3 and 4 for 2D and 3D images, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…No artificial or phantom images were used in the workflow. 13 Median filtering; Otsu thresholding Canny edge detection Kalman filtering Groves et al 14 Resizing; depth normalization CNN -Mwikirize et al 18 Top-hat filter Fully convolutional network R-CNN; 2D Log-Gabor filter bank Wijata et al 15 Hough transform Shock filter Gabor filter Czajkowska et al 23 Gaussian smoothing Histogram of Gradients Kernelized Weighted C-Means (KWCM) Hatt et al 16 Log-Gabor wavelets Adaboost Radon transform; second Gaussian derivative filter…”

Section: Introductionmentioning

confidence: 99%

“…They are summarized in Tables 1 and 2 for 2D and 3D images, respectively. To enhance bright linear structures, these methods employ mainly the following pre-processing: Otsu tresholding, 13 Top-hat filter, 14 Hough transform, 15 or Log-Gabor wavelets. 16 The machine-learning methods are widely used for needle segmentation process.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Automatic Biopsy Needle Detection and Segmentation on Ultrasound Images Using a Convolutional Neural Network

2021

View full text Add to dashboard Cite

Needle visualization in the ultrasound image is essential to successfully perform the ultrasound-guided core needle biopsy. Automatic needle detection can significantly reduce the procedure time, false-negative rate, and highly improve the diagnosis. In this paper, we present a CNN-based, fully automatic method for detection of core needle in 2D ultrasound images. Adaptive moment estimation optimizer is proposed as CNN architecture. Radon transform is applied to locate the needle. The network’s model was trained and tested on the total of 619 2D images from 91 cases of breast cancer. The model has achieved an average weighted intersection over union (the weighted Jaccard Index) of 0.986, F1 Score of 0.768, and angle RMSE of 3.73°. The obtained results exceed the other solutions by at least 0.27 and 7° in case of F1 score and angle RMSE, respectively. Finally, the needle is detected in a single frame averagely in 21.6 ms on a modern PC.

show abstract