Program
| Important notice: Although the challenge is officially scheduled in the morning only, for practical reasons the workshop will be co-organized with the Prostate Segmentation from T2-weighted MRI (PROMISE) challenge and therefore occupy a whole day. |
Practical information
Teams participating in the on-site challenge will receive an email link to the test data (15 real and 15 simulated scans) on Sunday 30 September around noon. Computation of the tumor segmentations can commence immediately. Results should be submitted for evaluation by noon on Monday 1 October (the day of the challenge).
The challenge will be held in Acropolis, Room Gallieni 5. A detailed map of the venue, including also the location of the BRATS poster session, is available from the Program of MICCAI2012 Workshops, challenges and tutorials (pp. 12-13).
Posters have to be mounted on the workshop day from 8:30 AM and will have to be withdrawn before 6 PM on the same day.
Program
| 09:00-09:15 | Introduction |
| 09:15-10:30 | Short oral presentation of the participants |
| 10:30-11:00 | Coffee break |
| 11:00-12:00 | Invited talks |
| Automated tumor segmentation for radiotherapy planning. | |
| Prof. Jan Unkelbach, Massachusetts General Hospital, Harvard Medical School | |
| Multimodal image analysis for glioma characterization. | |
| Dr. Andras Jakab, University of Debrecen Medical and Health Science Center | |
| 12:00-13:00 | Joint PROMISE and BRATS clinical talks |
| Imaging workflow in daily clinical routine - benefits and pitfalls of | |
| advanced brain tumor imaging. | |
| Prof. Roland Wiest, Bern University Hospital | |
| PROMISE talk (12:30): Clinical aspects of prostate imaging. | |
| Francois Cornud MD. Paris | |
| 13:00-15:00 | Lunch and poster session |
| 15:00-15:30 | Challenge results and discussion |
| 15:30-16:00 | Coffee break |
| 16:00-17:00 | Joint PROMISE and BRATS discussion on metrics and clinical relevance |
List of accepted papers
| Context-sensitive Classification Forests for Segmentation of Brain Tumor Tissues |
| D. Zikic, B. Glocker, E. Konukoglu, J. Shotton, A. Criminisi, D. H. Ye, C. Demiralp, O. M. Thomas, T. Das, R. Jena, and S. J. Price |
| Segmentation of Brain Tumor Images Based on Integrated Hierarchical Classification and Regularization |
| S. Bauer, T. Fejes, J. Slotboom, R. Wiest, L.-P. Nolte, and M. Reyes |
| Spatial Decision Forests for Glioma Segmentation in Multi-Channel MR Images |
| E. Geremia, B. H. Menze, and N. Ayache |
| Multimodal Brain Tumor Segmentation Using the "Tumor-cut" Method on the BraTS Dataset |
| A. Hamamci and G. Unal |
| Brain Tumor Segmentation Based on GMM and Active Contour Method with a Model-aware Edge Map |
| L. Zhao, W. Wu, and J.J. Corso |
| Probabilistic Gabor and Markov Random Fields Segmentation of Brain Tumours in MRI Volumes |
| N. K. Subbanna and T. Arbel |
| Hybrid Clustering and Logistic Regression for Multi-Modal Brain Tumor Segmentation |
| H.-C. Shin |
| Hierarchical Random Walker for Multimodal Brain Tumor Segmentation |
| Y. Xiao and J. Hu |
| Automatic Brain Tumor Segmentation Based on a Coupled Global-Local Intensity Bayesian Model |
| X. Tomas-Fernandez, and S. K. Warfield |
| Segmenting Glioma in Multi-Modal Images Using a Generative Model for Brain Lesion Segmentation |
| B. H. Menze, K. Van Leemput, D. Lashkari, M.-A. Weber, N. Ayache, and P. Golland |
| Segmenting Glioma in Multi-Modal Images Using a Generative-Discriminative Model for Brain Lesion Segmentation |
| B. H. Menze, E. Geremia, N. Ayache, and G. Szekely |
| Multi-modal Brain Tumor Segmentation via Latent Atlases |
| T. Riklin Raviv, K. Van Leemput, and B. H. Menze |
