Simultaneous automatic segmentation of multiple needles using 3D ultrasound for high-dose-rate prostate brachytherapy

Hrinivich, William T.; Hoover, Douglas A.; Surry, Kathleen; Edirisinghe, Chandima; Montreuil, Jacques; D’Souza, David; Fenster, Aaron; Wong, Eugene

doi:10.1002/mp.12148

Cited by 16 publications

(23 citation statements)

References 39 publications

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“…This has improved the results given by an automatic segmentation algorithm. 7 Figure 6(c) shows the 2D relationship between residuals in x and y directions, where the red ellipse in the plot is the 95% confidence ellipse, which means 95% of the data are included inside. The performance of the tip network is presented in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Hough transform (HT), 13 which converts the problem of finding a straight line into an optimization problem, is robust and is the basis of most subsequent algorithms. 14,15,7 However, the standard HT is really computationally demanding. Ding et al 16,17 developed a real-time needle segmentation algorithm using the Hough transform and it was further extended to analyze three-dimesional (3D) US images.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning applications in automatic needle segmentation in ultrasound‐guided prostate brachytherapy

Wang

Bagshaw

et al. 2020

Medical Physics

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High-Dose-Rate (HDR) brachytherapy is one of the most effective ways to treat the prostate cancer, which is the second most common cancer in men worldwide. This treatment delivers highly conformal dose through the transperineal needle implants and is guided by a real time ultrasound (US) imaging system. Currently, the brachytherapy needles in the US images are manually segmented by physicists during the treatment, which is time consuming and error prone. In this study, we propose a set of deep learning-based algorithms to accurately segment the brachytherapy needles and locate the needle tips from the US images. Methods: Two deep neural networks are developed to address this problem. First, a modified deep U-Net is used to segment the pixels belonging to the brachytherapy needles from the US images. Second, an additional VGG-16-based deep convolutional network is combined with the segmentation network to predict the locations of the needle tips. The networks are trained and evaluated on a clinical US images dataset with labeled needle trajectories collected in our hospital (Institutional Review Board approval (IRB 41755)). Results: The evaluation results show that our method can accurately extract the trajectories of the needles with a resolution of 0.668 mm and 0.319 mm in x and y direction, respectively. 95.4% of the x direction and 99.2% of the y direction have error ≤ 2 mm. Moreover, the position resolutions of the tips are 0.721, 0.369, and 1.877 mm in x, y, and z directions, respectively, while 94.2%, 98.3%, and 67.5% of the data have error ≤ 2 mm. Conclusions: This paper proposed a neural network-based algorithm to segment the brachytherapy needles from the US images and locate the needle tip. It can be used in the HDR brachytherapy to help improve the efficiency and quality of the treatments.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep learning applications in automatic needle segmentation in ultrasound‐guided prostate brachytherapy

Wang

Bagshaw

et al. 2020

Medical Physics

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“…However, generalizing the method for use with ultrasound and fluoroscopic images, extremely important clinical workhorse modalities for percutaneous needle-based interventions, will involve incorporation of modality-specific image-coupling objective functions to capture the grayscale appearance of needles in each case (Hrinivich et al, 2017; Wang et al, 2010). …”

Section: Discussionmentioning

confidence: 99%

“…Some of the various tracking methods surveyed here are much more flexible, may account for bifurcations and thus would probably go astray by allowing much more and catheter-atypical curvature changes than we do. Some methods focus on segmentation of interventional hardware such as guidewires in angiographic images, which are also tortuous because they are inserted into blood vessels (Vandini et al, 2017; Honnorat, 2013; Honnorat et al, 2010), and other methods focus on segmentation of less flexible intervention hardware such as biopsy needles in ultrasound images (Hrinivich et al, 2017; Pourtaherian et al, 2016; Daoud et al, 2015; Qiu et al, 2013; Aboofazeli et al, 2009; Okazawa et al, 2006; Czerwinski et al, 1999). Compelling results for segmentation of elongated structures – blood vessels and surgical hardware-using artificial neural networks have also been recently reported 3 , including for the segmentation of vessels from retinal images (Fu et al, 2016) and segmentation of surgical instruments from endoscopic images (Pakhomov et al, 2017).…”

Section: State Of the Artmentioning

confidence: 99%

Accurate model-based segmentation of gynecologic brachytherapy catheter collections in MRI-images

Mastmeyer

Pernelle

et al. 2017

Medical Image Analysis

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The gynecological cancer mortality rate, including cervical, ovarian, vaginal and vulvar cancers, is more than 20,000 annually in the US alone. In many countries, including the US, external-beam radiotherapy followed by high dose rate brachytherapy is the standard-of-care. The superior ability of MR to visualize soft tissue has led to an increase in its usage in planning and delivering brachytherapy treatment. A technical challenge associated with the use of MRI imaging for brachytherapy, in contrast to that of CT imaging, is the visualization of catheters that are used to place radiation sources into cancerous tissue. We describe here a precise, accurate method for achieving catheter segmentation and visualization. The algorithm, with the assistance of manually provided tip locations, performs segmentation using image-features, and is guided by a catheter-specific, estimated mechanical model. A final quality control step removes outliers or conflicting catheter trajectories. The mean Hausdorff error on a 54 patient, 760 catheter reference database was 1.49 mm; 51 of the outliers deviated more than two catheter widths (3.4 mm) from the gold standard, corresponding to catheter identification accuracy of 93% in a Syed-Neblett template. In a multi-user simulation experiment for evaluating RMS precision by simulating varying manually-provided superior tip positions, 3σ maximum errors were 2.44 mm. The average segmentation time for a single catheter was 3 seconds on a standard PC. The segmentation time, accuracy and precision, are promising indicators of the value of this method for clinical translation of MR-guidance in gynecologic brachytherapy and other catheter-based interventional procedures.

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“…Applications for 3D US needle-like segmentation have been focused on procedures in prostate, [24][25][26][27][28] breast, [29][30][31] heart, 32,33 and anesthetic administration, 34 but these approaches do not readily translate to liver interventions. This is primarily due to deep insertions into the liver that require needle applicators up to 30 cm in length and large angles relative to the transducer face resulting in poor specular reflections back to the transducer.…”

Section: Introductionmentioning

confidence: 99%

Three‐dimensional therapy needle applicator segmentation for ultrasound‐guided focal liver ablation

et al. 2019

Self Cite

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Purpose Minimally invasive procedures, such as microwave ablation, are becoming first‐line treatment options for early‐stage liver cancer due to lower complication rates and shorter recovery times than conventional surgical techniques. Although these procedures are promising, one reason preventing widespread adoption is inadequate local tumor ablation leading to observations of higher local cancer recurrence compared to conventional procedures. Poor ablation coverage has been associated with two‐dimensional (2D) ultrasound (US) guidance of the therapy needle applicators and has stimulated investigation into the use of three‐dimensional (3D) US imaging for these procedures. We have developed a supervised 3D US needle applicator segmentation algorithm using a single user input to augment the addition of 3D US to the current focal liver tumor ablation workflow with the goals of identifying and improving needle applicator localization efficiency. Methods The algorithm is initialized by creating a spherical search space of line segments around a manually chosen seed point that is selected by a user on the needle applicator visualized in a 3D US image. The most probable trajectory is chosen by maximizing the count and intensity of threshold voxels along a line segment and is filtered using the Otsu method to determine the tip location. Homogeneous tissue mimicking phantom images containing needle applicators were used to optimize the parameters of the algorithm prior to a four‐user investigation on retrospective 3D US images of patients who underwent microwave ablation for liver cancer. Trajectory, axis localization, and tip errors were computed based on comparisons to manual segmentations in 3D US images. Results Segmentation of needle applicators in ten phantom 3D US images was optimized to median (Q1, Q3) trajectory, axis, and tip errors of 2.1 (1.1, 3.6)°, 1.3 (0.8, 2.1) mm, and 1.3 (0.7, 2.5) mm, respectively, with a mean ± SD segmentation computation time of 0.246 ± 0.007 s. Use of the segmentation method with a 16 in vivo 3D US patient dataset resulted in median (Q1, Q3) trajectory, axis, and tip errors of 4.5 (2.4, 5.2)°, 1.9 (1.7, 2.1) mm, and 5.1 (2.2, 5.9) mm based on all users. Conclusions Segmentation of needle applicators in 3D US images during minimally invasive liver cancer therapeutic procedures could provide a utility that enables enhanced needle applicator guidance, placement verification, and improved clinical workflow. A semi‐automated 3D US needle applicator segmentation algorithm used in vivo demonstrated localization of the visualized trajectory and tip with less than 5° and 5.2 mm errors, respectively, in less than 0.31 s. This offers the ability to assess and adjust needle applicator placements intraoperatively to potentially decrease the observed liver cancer recurrence rates associated with current ablation procedures. Although optimized for deep and oblique angle needle applicator insertions, this proposed workflow has the potential to be altered for a variety of image‐guided minimally inva...

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Simultaneous automatic segmentation of multiple needles using 3D ultrasound for high-dose-rate prostate brachytherapy

Cited by 16 publications

References 39 publications

Deep learning applications in automatic needle segmentation in ultrasound‐guided prostate brachytherapy

Deep learning applications in automatic needle segmentation in ultrasound‐guided prostate brachytherapy

Accurate model-based segmentation of gynecologic brachytherapy catheter collections in MRI-images

Three‐dimensional therapy needle applicator segmentation for ultrasound‐guided focal liver ablation

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