Dataset Information

Tendon-motion tracking in an ultrasound image sequence using optical-flow-based block matching.

ABSTRACT:

Background

Tendon motion, which is commonly observed using ultrasound imaging, is one of the most important features used in tendinopathy diagnosis. However, speckle noise and out-of-plane issues make the tracking process difficult. Manual tracking is usually time consuming and often yields inconsistent results between users.

Methods

To automatically track tendon motion in ultrasound images, we developed a new method that combines the advantages of optical flow and multi-kernel block matching. For every pair of adjacent image frames, the optical flow is computed and used to estimate the accumulated displacement. The proposed method selects the frame interval adaptively based on this displacement. Multi-kernel block matching is then computed on the two selected frames, and, to reduce tracking errors, the detailed displacements of the frames in between are interpolated based on the optical flow results.

Results

In the experiments, cadaver data were used to evaluate the tracking results. The mean absolute error was less than 0.05 mm. The proposed method also tracked the motion of tendons in vivo, which provides useful information for clinical diagnosis.

Conclusion

The proposed method provides a new index for adaptively determining the frame interval. Compared with other methods, the proposed method yields tracking results that are significantly more accurate.

SUBMITTER: Chuang BI

PROVIDER: S-EPMC5399340 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Publications

Tendon-motion tracking in an ultrasound image sequence using optical-flow-based block matching.

Chuang Bo-I BI Hsu Jian-Han JH Kuo Li-Chieh LC Jou I-Ming IM Su Fong-Chin FC Sun Yung-Nien YN

Biomedical engineering online 20170420 1

<h4>Background</h4>Tendon motion, which is commonly observed using ultrasound imaging, is one of the most important features used in tendinopathy diagnosis. However, speckle noise and out-of-plane issues make the tracking process difficult. Manual tracking is usually time consuming and often yields inconsistent results between users.<h4>Methods</h4>To automatically track tendon motion in ultrasound images, we developed a new method that combines the advantages of optical flow and multi-kernel bl ...[more]

PMID: 28427411

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Project description:PURPOSE:The implementation of motion management techniques in radiation therapy can aid in mitigating uncertainties and reducing margins. For motion management to be effective, it is necessary to track key structures both accurately and at a real-time speed. Therefore, the focus of this work was to develop a 2D algorithm for the real-time tracking of ultrasound features to aid in radiation therapy motion management. MATERIALS AND METHODS:The developed algorithm utilized a similarity measure-based block matching algorithm incorporating training methods and multiple simultaneous templates. The algorithm is broken down into three primary components, all of which use normalized cross-correlation (NCC) as a similarity metric. First, a global feature shift to account for gross displacements from the previous frame is determined using large block sizes which encompass the entirety of the feature. Second, the most similar reference frame is chosen from a series of training images that are accumulated during the first K frames of tracking to aid in contour consistency and provide a starting point for the localized template initialization. Finally, localized block matching is performed through the simultaneous use of both a training frame and the previous frame. The localized block matching utilizes a series of templates positioned at the boundary points of the training and previous contours. The weighted final boundary points from both the previous and the training frame are ultimately combined and used to determine an affine transformation from the previous frame to the current frame. RESULTS:A mean tracking error of 0.72 ± 1.25 mm was observed for 85 point-landmarks across 39 ultrasound sequences relative to manual ground truth annotations. The image processing speed per landmark with the GPU implementation was between 41 and 165 frames per second (fps) during the training set accumulation, and between 73 and 234 fps after training set accumulation. Relative to a comparable multithreaded CPU approach using OpenMP, the GPU implementation resulted in speedups between -30% and 355% during training set accumulation, and between -37% and 639% postaccumulation. CONCLUSIONS:Initial implementations indicated an accuracy that was comparable to or exceeding those achieved by alternative 2D tracking methods, with a computational speed that is more than sufficient for real-time applications in a radiation therapy environment. While the overall performance reached levels suitable for implementation in radiation therapy, the observed increase in failures for smaller features, as well as the algorithm's inability to be applied to nonconvex features warrants additional investigation to address the shortcomings observed.

Dataset Information

Tendon-motion tracking in an ultrasound image sequence using optical-flow-based block matching.

Background

Methods

Results

Conclusion

Publications

Tendon-motion tracking in an ultrasound image sequence using optical-flow-based block matching.

Similar Datasets

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