CLUST Publications
This webpage lists publications which are using CLUST data. Please let us know of any omission. Last updated on 19th September 2019.
Inside CLUST Challenge
2020
Liu, F., Liu, D., Tian, J., Xie, X., Yang, X., Wang, K.,
Cascaded one-shot deformable convolutional neural networks: Developing a deep learning mdoel for respiratory motion estimation in ultrasound sequences.
Medical Image Analysis, 65, 101793 (2020)
2019
Jeungyoon, L., Euisuk, C., Tai-Kyong, S.,
Combination of RCNN and KCF for Landmark Tracking in 2D Ultrasound Sequence of Liver.
IEEE Engineering in Medicine & Biology Society, (2019)
Gomariz, A., Li, W., Ozkan, E., Tanner, C., Goksel, O.,
Siamese Networks with Location Prior for Landmark Tracking in Liver Ultrasound Sequences.
IEEE International Symposium on Biomedical Imaging, (2019)
2018
De Luca, V., Banerjee, J., Hallack, A., Kondo, S., Makhinya, M., Nouri, D., Royer, L., Cifor, A., Dardenne, G., Goksel, O., Gooding, M.J., Klink, C., Krupa, A., Le Bras, A., Marchal, M., Moelker, A., Niessen, W.J., Papiez, B.W., Rothberg, A., Schnabel, J., van Walsum, T., Harris, E., Lediju Bell, M.A., Tanner, C.,
Evaluation of 2D and 3D ultrasound tracking algorithms and impact on ultrasound‐guided liver radiotherapy margins. Medical Physics, 45(11), pp. 4986-5003 (2018)
Williamson, T., Cheung, W., Roberts, S. K., & Chauhan, S.,
Ultrasound-based liver tracking utilizing a hybrid template/optical flow approach.
Int. Journal of Computer Assisted Radiology and Surgery, 13, pp. 1605-1615 (2018)
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, 12, pp. 941-950 (2017)
Shepard, A. J., Wang, B., Foo, T. K., & Bednarz, B. P.,
A block matching based approach with multiple simultaneous templates for the real‐time 2D ultrasound tracking of liver vessels.
Medical Physics, 44, pp. 5889-5900 (2017)
Ihle, F. A.,
Random Forests for Tracking on Ultrasonic Images.
Master-Thesis, University Bremen, April 2017
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)
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)
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)
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 or different annotations of the test 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.
2019
Huang, P., Su, L., Chen, S., Cao, K., Song, Q., Kazanzides, P., Iordachita, I., Bell, M.A.L., Wong, J.W., Li, D. and Ding, K., 2019.
2D ultrasound imaging based intra-fraction respiratory motion tracking for abdominal radiation therapy using machine learning.
Physics in Medicine & Biology, 64 (2019)
- Evaluated for part of CLUST15 training data, but compared in Table 1 to official CLUST15 challenge performances (test data)
Huang, P., Yu, G., Lu, H., Liu, D., Xing, L., Yin, Y., Kovalchuk, N., Xing, L. and Li, D., 2019.
Attention‐aware fully convolutional neural network with convolutional long short‐term memory network for ultrasound‐based motion tracking.
Medical Physics, 46(5), pp.2275-2285 (2019)
- Evaluated on own gold standard annotations for CLUST15 test data
Ha, I.Y., Wilms, M., Handels, H. and Heinrich, M.P.,
Model-based sparse-to-dense image registration for realtime respiratory motion estimation in image-guided interventions.
IEEE Transactions on Biomedical Engineering, 66(2), pp.302-310. (2019)
- Subset of CLUST15 images with few own manual annotations on test data
He, J., Shen, C., Huang, Y. and Wu, J.,
Siamese Spatial Pyramid Matching Network with Location Prior for Anatomical Landmark Tracking with in 3-Dimentional Ultrasound Sequence.
In Chinese Conference on Pattern Recognition and Computer Vision (PRCV) pp. 341-353. (2019)
2018
Shen, C., Shi, H., Sun, T., Huang, Y. and Wu, J., An Online Learning Approach for Robust Motion Tracking in Liver Ultrasound Sequence.
In Chinese Conference on Pattern Recognition and Computer Vision (PRCV) pp. 440-451. (2018)
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
Rangamani, A., Xiong, T., Nair, A., Tran, T. D., & Chin, S. P.
Landmark Detection and Tracking in Ultrasound using a CNN-RNN Framework.
3D Deep Learning Workshop @ NIPS (Dec 9 2016)
- Subset of one sequence from CLUST15 training dataset
- Trained on 116 annotated frames of one sequence, tested on remaining 28 annotated frames of same sequence
- Not comparable with included result for full test set
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.
