CLUST Publications


This webpage lists publications which are using CLUST data. Please let us know of any omission.

Inside CLUST Challenge

2014

    Benz, T., Kowarschik, M. and Navab, N., Kernel-based Tracking in Ultrasound Sequences of Liver. In Proc. MICCAI workshop: Challenge on Liver Ultrasound Tracking, pp. 21-28 (2014)
    Kondo, S., Liver Ultrasound Tracking Using Long-term and Short-term Template Matching. In Proc. MICCAI workshop: Challenge on Liver Ultrasound Tracking, pp. 13-20 (2014)
    König, L., Kipshagen, T. and Rühaak, J., A non-linear image registration scheme for real-time liver ultrasound tracking using normalized gradient fields. In Proc. MICCAI workshop: Challenge on Liver Ultrasound Trackin, pp. 29-36 (2014)
    Luebke, D., and Grozea, C., MICCAI CLUST 2014 - Bayesian Real-Time Liver Feature Ultrasound Tracking In Proc. MICCAI workshop: Challenge on Liver Ultrasound Trackin, pp. 37-44 (2014)
    O’Shea, T., Bamber, J., and Harris, E. Liver Feature Motion Estimation in Long High Frame Rate 2D Ultrasound Sequences In Proc. MICCAI workshop: Challenge on Liver Ultrasound Tracking, pp. 5-12 (2015)
    Rothlübbers, S., Schwaab, J., Jenne, J. and Günther, M., MICCAI CLUST 2014 - Bayesian real-time liver feature ultrasound tracking. In Proc. MICCAI workshop: Challenge on Liver Ultrasound Tracking, pp. 45-52 (2014)
    Somphone, O., Allaire, S., Mory, B. and Dufour, C., Live Feature Tracking in Ultrasound Liver Sequences with Sparse Demons. In Proc. MICCAI workshop: Challenge on Liver Ultrasound Tracking, pp. 53-60 (2014)
2015

    Banerjee, J., Klink, C., Vast, E., Niessen, W.J., Moelker, A. and van Walsum, T., A combined tracking and registration approach for tracking anatomical landmarks in 4D ultrasound of the liver. In Proc. MICCAI workshop: Challenge on Liver Ultrasound Tracking, pp. 43-50 (2015)
    De Luca, V., Benz, T., Kondo, S., König, L., Lübke, D., Rothlübbers, S., Somphone, O., Allaire, S., Bell, M.L., Chung, D.Y.F. and Cifor, A., The 2014 liver ultrasound tracking benchmark. Physics in medicine and biology, 60(14), p.5571 (2015)
    Hallack, A., Papiez, W. B., Cifor, A., Gooding, M.J., Schnabel, J.A., Robust Liver Ultrasound Tracking using Dense Distinctive Image Features. In Proc. MICCAI workshop: Challenge on Liver Ultrasound Tracking, pp. 28-35 (2015)
    Kondo, S., Liver Ultrasound Tracking Using Kernelized Correlation Filter With Adaptive Window Size Selection. In Proc. MICCAI workshop: Challenge on Liver Ultrasound Tracking, pp. 13-19 (2015)
    Makhinya, M. and Goksel, O., Motion Tracking in 2D Ultrasound Using Vessel Models and Robust Optic-Flow. In Proc. MICCAI workshop: Challenge on Liver Ultrasound Tracking, pp. 20-27 (2015)
    Nouri, D. and Rothberg, A., Liver Ultrasound Tracking using a Learned Distance Metric. In Proc. MICCAI workshop: Challenge on Liver Ultrasound Tracking, pp. 5-12 (2015)
    Royer, L., Dardenne, G., Le Bras, A., Marchal, M. and Krupa, A., Tracking of Non-rigid Targets in 3D US Images: Results on CLUST 2015. In Proc. MICCAI workshop: Challenge on Liver Ultrasound Tracking, pp. 36-42 (2015)
2016

    Chen, X., Tanner, C., Göksel, O., Székely, G. and De Luca, V., Temporal Prediction of Respiratory Motion Using a Trained Ensemble of Forecasting Methods. In Int. Conference on Medical Imaging and Virtual Reality, pp. 383-391 (2016)
2017

    Royer, L., Krupa, A., Dardenne, G., Le Bras, A., Marchand, E. and Marchal, M., Real-time Target Tracking of Soft Tissues in 3D Ultrasound Images Based on Robust Visual Information and Mechanical Simulation. Medical Image Analysis, 35, pp. 582-598 (2017)
    Ozkan, E., Tanner, C., Kastelic, M., Mattausch, O., Makhinya, M. and Goksel, O., Robust Motion Tracking in Liver from 2D Ultrasound Images using Supporters. Int. Journal of Computer Assisted Radiology and Surgery, pp.1-10 (2017)

Outside CLUST Challenge

The CLUST challenge enables the comparison of different methods. The manual annotations are withheld for the test set to avoid potential overfitting of methods to the data. Instead we provide the service of calculating the tracking performance for submitted results. However, publications do appear which are purely based on the CLUST training data. These should not be compared with official CLUST challenge data. To facilitate making this discrimination, we list and comment here published work, which uses CLUST data without participating in the challenge.

2015

    Cao, K., Bednarz, B., Smith, L.S., Foo, T.K. and Patwardhan, K.A., Respiration induced fiducial motion tracking in ultrasound using an extended SFA approach. In Proc. SPIE Medical Imaging, 94190S-94190S, (2015)
    • Subset of CLUST14 training dataset
    • Subset of frames assumed to be annotated pre-therapy images (2D: 110 s, 3D: 12 s) for sequence-specific learning. Learned relationship tested on short (4-12 s) subset of same sequence.
2016

    Carletti, M., Dall’Alba, D., Cristani, M. and Fiorini, P., A Robust Particle Filtering Approach with Spatially-dependent Template Selection for Medical Ultrasound Tracking Applications. In Proc. 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016), 524-533 (2016)
    • CLUST15 training dataset
    • Not comparable to the included CLUST14 challenge results
    Shepard, A. and Bednarz, B., SU-G-BRA-02: Development of a Learning Based Block Matching Algorithm for Ultrasound Tracking in Radiotherapy. Medical Physics, 43(6), 3635-3635 (2016)
    • Subset of CLUST14 training dataset
    • First 200 frames assumed to be manually segmented pre-therapy images, and used for sequence-specific training of method
    Wilms, M., Ha, I.Y., Handels, H. and Heinrich, M.P., Model-Based Regularisation for Respiratory Motion Estimation with Sparse Features in Image-Guided Interventions. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 89-97, (October 2016)
    • Subset of CLUST15 training dataset
    Zhao Y, Shen Y, Li J, Jin J, and Wang Q. A new motion estimation method for long-term ultrasound free respiration sequences based on multi-scale blobness enhancement filter and level set method. In IEEE 13th International Conference Signal Processing (ICSP), pp. 57-61 (Nov 6 2016)
    • Subset of CLUST15 training dataset

ETH Zurich

CVL ETH

ICR

LCSR Johns Hopkins