The 2014 liver ultrasound tracking benchmark

Luca, Valéria De; Benz, Tobias; Kondo, Satoshi; König, Lars; Lübke, Diethard; Rothlübbers, Sven; Somphone, Oudom; Allaire, Stéphane; Bell, Muyinatu A. Lediju; Chung, Daniel Y. F.; Cifor, Amalia; Grozea, Cristian; Günther, Matthias; Jenne, Jürgen; Kipshagen, Till; Kowarschik, Markus; Navab, Nassir; Rühaak, Jan; Schwaab, Julia; Tanner, Christine

doi:10.1088/0031-9155/60/14/5571

Cited by 56 publications

(72 citation statements)

References 38 publications

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“…Harris et al (2010) performed "speckle tracking" to measure in vivo liver displacement in the presence of respiratory motion, and Bell et al (2012) applied the technique with a higher acquisition rate afforded by a 2D matrix array probe. Other potential tracking algorithms are benchmarked by De Luca et al (2015).…”

Section: Introductionmentioning

confidence: 99%

Cooperative Control with Ultrasound Guidance for Radiation Therapy

et al. 2016

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Radiation therapy typically begins with the acquisition of a CT scan of the patient for planning, followed by multiple days where radiation is delivered according to the plan. This requires that the patient be reproducibly positioned (set up) on the radiation therapy device (linear accelerator) such that the radiation beams pass through the target. Modern linear accelerators provide cone-beam computed tomography (CBCT) imaging, but this does not provide sufficient contrast to discriminate many abdominal soft-tissue targets, and therefore patient setup is often done by aligning bony anatomy or implanted fiducials. Ultrasound (US) can be used to both assist with patient setup and to provide real-time monitoring of soft-tissue targets. However, one challenge is that the ultrasound probe contact pressure can deform the target area and cause discrepancies with the treatment plan. Another challenge is that radiation therapists typically do not have ultrasound experience and therefore cannot easily find the target in the US image. We propose cooperative control strategies to address both the challenges. First, we use cooperative control with virtual fixtures (VFs) to enable acquisition of a planning CT that includes the soft-tissue deformation. Then, for the patient setup during the treatment sessions, we propose to use real-time US image feedback to dynamically update the VFs; this co-manipulation strategy provides haptic cues that guide the therapist to correctly place the US probe. A phantom study is performed to demonstrate that the co-manipulation strategy enables inexperienced operators to quickly and accurately place the probe on a phantom to reproduce a desired reference image. This is a necessary step for patient setup and, by reproducing the reference image, creates soft-tissue deformations that are consistent with the treatment plan, thereby enabling real-time monitoring during treatment delivery.

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Section: Introductionmentioning

confidence: 99%

Cooperative Control with Ultrasound Guidance for Radiation Therapy

et al. 2016

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“…The resulting performance is 1.04 mm mean and 2.26 mm 95%ile errors. This 95%ile tracking performance is relevant in liver motion tracking for radiation and focused therapy applications, when compared to 1.23 mm mean inter-observer 95%tile variability reported for a similar dataset in [17].…”

Section: Discussionmentioning

confidence: 61%

“…We evaluated our method using the 2D liver US image sequences provided by the Challenge on Liver Ultrasound Tracking (CLUST)-2015 [17]. A main advantage of supporters is the robustness to feature appearance in tracking, for instance, when a target is occluded by acoustic shadowing.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Robust motion tracking in liver from 2D ultrasound images using supporters

Özkan¹,

Tanner²,

Kastelic³

et al. 2017

Int J CARS

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Purpose: Effectiveness of image-guided radiation therapy with precise dose delivery depends highly on accurate target localization, which may involve motion during treatment due to, e.g., breathing and drift. Therefore, it is important to track the motion and adjust the radiation delivery accordingly. Tracking generally requires reliable target appearance and image features, whereas in ultrasound imaging acoustic shadowing and other artifacts may degrade the visibility of a target, leading to substantial tracking errors. To minimize such errors, we propose a method based on so-called supporters, a computer vision tracking technique. This allows us to leverage information from surrounding motion for improving robustness of motion tracking on 2D ultrasound image sequences of the liver. Methods: Image features, potentially useful for predicting the target positions, are individually tracked and a supporter model capturing the coupling of motion between these features and the target is learned on-line. This model is then applied to predict the target position, when the target cannot be otherwise tracked reliably. Results: The proposed method was evaluated using the Challenge on Liver Ultrasound Tracking (CLUST)-2015 dataset. Leave-one-out cross validation was performed on the training set of 24 2D image sequences of each 1-5 minutes. The method was then applied on the test set (24 2D sequences), where the results were evaluated by the challenge organizers, yielding 1.04 mm mean and 2.26 mm 95%ile tracking error for all targets. We also devised a simulation framework to emulate acoustic shadowing artifacts from the ribs, which showed effective tracking despite the shadows. Conclusions: Results support the feasibility and demonstrate the advantages of using supporters. The proposed method improves its baseline tracker, which uses optic-flow and elliptic vessel models, and yields the state-of-the-art real-time tracking solution for the CLUST challenge.

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“…The aforementioned additions to the tracking challenge presented in this paper, i.e. temporal motion prediction and combined results for motion‐compensated margin calculations, are novel compared to our previous study …”

Section: Introductionmentioning

confidence: 96%

“…Instead, the motion of other visible anatomical structures (e.g. vessels) can be estimated and used as input to 4D liver motion models to spatially predict the tumor position …”

Section: Introductionmentioning

confidence: 99%

Evaluation of 2D and 3D ultrasound tracking algorithms and impact on ultrasound‐guided liver radiotherapy margins

et al. 2018

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Liver motion estimation and prediction during free-breathing from 2D ultrasound images can substantially reduce the in-plane motion uncertainty and hence treatment margins. Employing an accurate tracking method while avoiding non-linear temporal prediction would be favorable. This approach has the potential to shorten treatment time compared to breath-hold and gated approaches, and increase treatment efficiency and safety.

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The 2014 liver ultrasound tracking benchmark

Cited by 56 publications

References 38 publications

Cooperative Control with Ultrasound Guidance for Radiation Therapy

Cooperative Control with Ultrasound Guidance for Radiation Therapy

Robust motion tracking in liver from 2D ultrasound images using supporters

Evaluation of 2D and 3D ultrasound tracking algorithms and impact on ultrasound‐guided liver radiotherapy margins

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