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Medical Computer Vision: Recognition Techniques and Applications in Medical Imaging

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The Workshop on Medical Computer Vision (MICCAI-MCV 2010) was held in conjunction with the 13th International Conference on Medical Image Computing and Computer – Assisted Intervention (MICCAI 2010) on September 20, 2010 in Beijing, China. The one-day workshop focused on recognition techniques and applications in medical imaging. The participants discussed principled approaches that go beyond the limits of current model-driven image analysis, which are provably efficient and scalable, and which generalize well to previously unseen images. It included emerging applications that go beyond the analysis of individual clinical studies and specific diagnostic tasks – a current focus of many computational methods in medical imaging.

The workshop fostered discussions among researchers working on novel computational approaches at the interface of computer vision, machine learning, and medical image analysis. It targeted an emerging community interested in pushing the boundaries of what current medical software applications can deliver in both clinical and research medical settings. Our call for papers resulted in 38 submissions of up to 12 pages each. Each paper received at least three reviews. Based on these peer reviews, we selected 10 submissions for oral and 11 for poster presentation.

The Best Scientific Paper Award was given to Michael Kelm and co-authors for their contribution “Detection of 3D Spinal Geometry Using Iterated Marginal Space Learning.” The runners-up were Peter Maday and co-authors for their contribution “Imaging as a Surrogate for the Early Prediction and Assessment of Treatment Response Through the Analysis of 4-D Texture Ensembles,” and Dongfeng Han and co-authors for their contribution “Motion Artifact Reduction in 4D Helical CT: Graph-Based Structure Alignment.”

During the workshop two distinguished invited speakers offered their perspective on the development of the field: Dorin Comanicu of Siemens Corporate Research, and Yiqiang Zhan of Siemens Medical Solutions.

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