The 9th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2006, was held in Copenhagen, Denmark, at the Tivoli Concert Hall with satellite workshops and tutorials at the IT University of Copenhagen, October 1–6, 2006. The conference has become the premier international conference with in-depth full-length articles in the multidisciplinary fields of medical image computing, computer-assisted intervention, and medical robotics. The conference brings together clinicians, computer scientists, engineers, physicists, and other researchers and offers a forum for exchange of ideas in a multidisciplinary setting.
Selected papers from MICCAI 2005 ( ) were published in a previous issue of Academic Radiology and were well received by the readers. MICCAI 2007 ( www.miccai2007.org ) will be held in Brisbane, Australia, October 29–November 2, 2007.
For the MICCAI 2006 conference, a careful review and selection process was executed to secure the best possible program. We received 578 scientific papers, from which 39 articles were selected for the presentation in the single track oral program and 193 articles for the poster program. The authors of 16 of these articles addressing technical as well as clinical issues were invited to submit extended versions of their articles for this special issue. These articles went through a new review process and finally eight articles ( ) were accepted for publication. Additional articles from MICCAI 2006 were selected for special issues of Medical Image Analysis and Computer-Aided Surgery.
The selected articles cover a series of topics of interest for Academic Radiology readers, such as medical image computing, computer-assisted interventional systems and robotics, new applications for specific imaging systems, bioscience applications and computer-aided diagnosis, and visualization and feedback.
In the article by Wong et al ( ), a cardiac physiome model comprising an electrical wave propagation model, an electromechanical coupling model, and a biomechanical model is applied as a prior constraint for extracting cardiac deformation data from magnetic resonance image sequences of the heart. This is an extension from previous work on constraining models based on biomechanics only. The present work leads to more physiologic meaningful results.
Deformation modeling is also an important factor in the article by Murgasova et al ( ). Their work addresses the use of brain atlases for segmentation of brain structures in magnetic resonance imaging of young children. A probabilistic atlas for different brain structures is constructed by nonlinear deformation of a set of training examples and a single manually segmented example in to a common frame of reference. The probabilistic atlas is then used as a prior for subsequent voxel classification. The results show an improvement over previous work using the expectation-maximization algorithm for brain segmentation.
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References
1. Wong K.C., Liu H., et. al.: Physiome-model-based statespace framework for cardiac deformation recovery. Acad Radiol 2007; 14: pp. 11.
2. Murgasova M., Dyet L., Edwards D., et. al.: Segmentation of brain MRI in young children. Acad Radiol 2007; 14: pp. 11.
3. Groher M., Jakobs T.F., Padoy N., et. al.: Planning and intraoperative visualization of liver catheterizations: new CTA protocol and 2D-3D registration method. Acad Radiol 2007; 14: pp. 11.
4. Zhan Y., Ou Y., Feldman M., et. al.: Registering histological and MR images of prostate for image-based cancer detection. Acad Radiol 2007; 14: pp. 11.
5. Ross J.C., Miller J.V., Turner W.D., et. al.: An analysis of early studies released by the lung imaging database consortium (LIDC). Acad Radiol 2007; 14: pp. 11.
6. Altrogge I., Preusser T., Kröger T., et. al.: Multi-scale optimization of probe placement for radio-frequency ablation. Acad Radiol 2007; 14: pp. 11.
7. Linguraru M.G., Kabla A., Marx G.R., et. al.: Real-time tracking and shape analysis of atrial septal defects in 3D echocardiography. Acad Radiol 2007; 14: pp. 11.
8. Dornheim J., Seim H., Preim B., et. al.: Segmentation of neck lymph nodes in CT datasets with stable 3D mass-spring models. Acad Radiol 2007; 14: pp. 11.
9. Rousseau R., Glenn O., Iordanova B., et. al.: Registration-based approach for reconstruction of high-resolution in utero fetal MR brain images. Acad Radiol 2006; 13: pp. 1072-1081. 2006
10. Xu S., Taylor R., Fichtinger G., et. al.: Lung deformation estimation and four-dimensional CT lung reconstruction. Acad Radiol 2006; 13: pp. 1082-1092.
11. Buonaccorsi G., Roberts C., Cheung S., et. al.: Comparison of the performance of tracer kinetic model-driven registration for dynamic contrast enhanced MRI using different models of contrast enhancement. Acad Radiol 2006; 13: pp. 1112-1123.
12. Wang F., Vemuri B., Eisenschenk S.: Joint registration and segmentation of neuroanatomic structures from brain MRI. Acad Radiol 2006; 13: pp. 1104-1111.
13. Dold C., Zaitsev M., Speck O., et. al.: Advantages and limitations of prospective head motion compensation for MRI using an optical motion tracking device. Acad Radiol 2006; 13: pp. 1093-1103.
14. Kiss G., Drisis S., Bielen D., et. al.: Computer-aided detection of colonic polyps using low-dose CT acquisitions. Acad Radiol 2006; 13: pp. 1062-1071.
15. Littmann A., Guehring J., Buechel C., et. al.: Acquisition-related morphological variability in structural MRI. Acad Radiol 2006; 13: pp. 1055-1061.
16. Huang H., Shen L., Zhang R., et. al.: Cardiac motion analysis to improve pacing site selection in CRT. Acad Radiol 2006; 13: pp. 1124-1134.