MICCAI

AcronymDefinition
MICCAIMedical Image Computing and Computer-Assisted Intervention
References in periodicals archive ?
The Midas Journal--2008 MICCAI Workshop Grand Challenge Coronary Artery Tracking.
The Centers for Medical Robotics and Computer Assisted Surgery (MRCAS) at Carnegie Mellon University and UPMC Shadyside Hospital, as well as the Western Pennsylvania Institute for Computer Assisted Surgery (ICAS) are sponsoring MICCAI 2000 October 11-14, 2000 at the Hilton Pittsburgh and Towers Hotel in Pittsburgh, PA, USA.
The theme for MICCAI 2000 theme is "Tools and Technology for Clinical Practice.
MICCAI 2000 attendees will learn about new computer assisted tools, robotic technologies, image guided intervention, and medical imaging techniques that will become the next generation of surgical tools.
MICCAI 2000's exhibition area will feature companies demonstrating the latest diagnostic equipment, surgical guidance systems, prosthetic devices, computer hardware, image display and archive systems, image processing software, books, journals, and other materials.
DiGioia adds, "The upcoming MICCAI conference will be an excellent opportunity to bring together physicians from all sub-specialties, scientists, and commercial representatives from all over the world.
For MICCAI Sliver07 datasets, the score of 100 indicated a perfect segmentation when all the five measures were zero [4].
Lamecker, "Shape constrained automatic segmentation of the liver based on a heuristic intensity model," in Proceedings of the MICCAI Workshop on 3D Segmentation in the Clinic: A Grand Challenge, pp.
Table 1: Overview of liver segmentation methods for CT images: auto = automatic; semi = semiautomatic; VOE = volumetric overlap error; RVD = relative absolute volume difference; MaxD = maximum symmetric surface distance; DSC = dice similarity coefficient; RG = region growing; DM = deformable model; SSM = statistical shape model; PA = probabilistic atlas; GC = graph cuts; local = from local hospitals; Sliver07 = MICCAI 2007 grand challenge in segmentation of liver datasets.