Robust motion tracking in liver from 2D ultrasound images using supporters
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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.
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.
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 min. 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.
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.
KeywordsTracking liver in ultrasound Respiratory motion compensation Image-guided radiation therapy Supporters
This work is supported by the Swiss National Science Foundation.
Compliance with ethical standards
Conflict of interest
All the authors declare that they have no conflict of interest.
- 4.Vijayan S, Klein S, Hofstad EF, Lindseth F, Ystgaard B, Langø T (2013) Validation of a non-rigid registration method for motion compensation in 4D ultrasound of the liver. In: 2013 IEEE 10th international symposium on biomedical imaging, pp 792–795Google Scholar
- 5.De Luca V, Tschannen M, Székely G, Tanner C (2013) A learning-based approach for fast and robust vessel tracking in long ultrasound sequences. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 518–525Google Scholar
- 6.Makhinya M, Goksel O (2015) Motion tracking in 2D ultrasound using vessel models and robust optic-flow. In: MICCAI 2015 challenge on liver ultrasound trackingGoogle Scholar
- 7.Grabner H, Matas J, Van Gool L, Cattin P (2010) Tracking the invisible: learning where the object might be. In: International conference on computer vision and pattern recognition (CVPR), pp 1285–1292Google Scholar
- 8.Yanagawa Y, Echigo T, Vu H, Okazaki H, Fujiwara Y, Arakawa T, Yagi Y (2012) Tracking abnormalities in video capsule endoscopy using surrounding features with a triangular constraint. In: International symposium on biomedical imaging (ISBI)Google Scholar
- 11.Sun Z, Yao H, Zhang S, Sun X (2011) Robust visual tracking via context objects computing. In: 18th IEEE international conference on image processing, pp 509–512Google Scholar
- 12.Xiong F, Camps OI, Sznaier M (2012) Dynamic context for tracking behind occlusions. In: European conference on computer vision (ECCV), pp 580–593Google Scholar
- 13.Meng L, Jia Q (2013) Multi-target tracking based on level set segmentation and contextual information. Int J Signal Process Image Process Pattern Recognit 6(4):287–296Google Scholar
- 16.Samei G, Chlebus G, Sz ekely G, Tanner C (2013) Adaptive confidence regions of motion predictions from population exemplar models. In: MICCAI workshop on computational and clinical challenges in abdominal imaging, pp 231–240Google Scholar
- 17.De Luca T, annd Benz V, Kondo S, Knig L, Lbke D, Rothlbbers S, Somphone O, Allaire S, Lediju Bell M, Chung D, Cifor A, Grozea C, Gnther M, Jenne J, Kipshagen T, Kowarschik M, Navab N, Rhaak J, Schwaab J, Tanner C (2015) The 2014 liver ultrasound tracking benchmark. Phys Med Biol 60(14):5571CrossRefPubMedGoogle Scholar
- 19.Mattausch O, Goksel O (2016) Monte-carlo ray-tracing for realistic interactive ultrasound simulation. In: Eurographics workshop on visual computing for biology and medicineGoogle Scholar
- 20.Lucas B, Kanade T (1981) An iterative image registration technique with an application to stereo vision. In: Proceedings of imaging understanding workshop, pp 121–130Google Scholar