MICCAI-MCV 2013 - Large Data in Medical Imaging

MICCAI post-conference workshop:        26 Sept 2013
Submission deadline:                              15 June 2013  
Proceedings will be published in the
Springer Lecture Notes in Computer Science
This workshop aims at exploring the use of modern computer vision technology in tasks such as automatic segmentation and registration, localization of anatomical features and detection of anomalies. It emphasizes questions of harvesting, organizing and learning from large-scale medical imaging data sets and general-purpose automatic understanding of medical images. We are especially interested in modern, scalable and efficient algorithms which generalize well to previously unseen images and which can be applied to large-scale data sets that are arising, for example, from studies with significant populations, through the use of wide-field-of-view imaging sequences at high spatial resolution, or when compiling hospital-scale databases.

We encourage the submission of original papers that propose new methodology strongly motivated by a clinical application. Submissions will be at the interface of computer vision, machine learning and medical imaging analysis. Of particular interest is work that fosters the understanding of the specific challenges, assumptions, and constraints that computer vision approaches can overcome in the medical domain.

Florin Ghesu, Stefan Bauer, Michael Gadermayr, Georg Langs
Best Paper Award:
Bauer et al. Integrated Spatio-Temporal Segmentation of Longitudinal Brain Tumor Imaging Studies
Ghesu et al. Pectoral Muscle Detection in Digital Breast Tomosynthesis and Mammography
Gadermayr et al. Shape Curvature Histogram: A Shape Feature for Celiac Disease Diagnosis
NEW!  The proceedings are published by Springer as LNCS volume 8331

Details on how to submit papers until September 15th for oral presentation during the VISCERAL session are available.
The preliminary workshop program and schedule is available.
The upload of the camera ready manuscripts is open.
Manuscripts have to be registered and submitted until June 15th. They can be updated in the conference management system until June 17th, though.
The best submission will receive the Siemens Best Scientific Paper Award sponsored by Siemens Corporate Technology.
We could win Ron Kikinis and Leo Grady as invited speakers.

We encourage the submission of methodological contributions dealing with:

  • Computer vision approaches that are scalable to large data and efficient in the 2D and 3D domain
  • Methods dealing with incomplete-, weak- or noisy annotation of training examples
  • Data driven and exploratory models for image segmentation and quantitative description
  • Learning approaches for registration, calibration and related image transforms
  • Anatomical structure localization through object recognition and categorization
  • Developing 3D image descriptors and interest points for object localization
  • Generative models of 3D image scenes relying on – or complementing – population atlases of anatomy or function
  • Image acquisition variations (CT scan plan or MR pulse sequence variations, contrast/non contrast)
We also welcome submissions on applications of these technologies, for example:
  • Semantic anatomy parsing, semantic navigation and visualization
  • Applications of web-driven techniques to structure medical data sets
  • Image indexing, data organization, data harvesting
  • Real-time medical image applications
Please see the program of the past workshops to identify other related topics: MICCAI-MCV 2010, CVPR-MCV 2012,
This year's emphasis will be on large data and we invite participants of the VISCERAL whole-body annotation benchmark to attend the workshop and to contribute short papers to the VISCERAL Session. More information about VISCERAL is available from the organizers or from www.visceral.eu. Details on how to contribute to the VISCERAL Session can be found here
Bjoern Menze (ETH Zurich, INRIA), Georg Langs (MU Vienna, MIT), Albert Montillo (GE), Michael Kelm (Siemens),
Henning Mueller (HES-SO), Zhuowen Tu (UCLA).