Medical image computing and computer-assisted intervention - MICCAI 2004 : 7th International Conference, Saint-Malo, France, September 26-29, 2004 : proceedings

TL;DR: The results show that the proposed algorithm generates diffeomorphic transformations while providing similar levels of performance as the existing FFD registration algorithm in terms of registration accuracy.

TL;DR: Six prominent categories of resting-state fMRI data analysis are reviewed: seed-based functional connectivity, independent component analysis, clustering, pattern classification, graph theory, and two “local” methods.

TL;DR: Recent progress on the study of Alzheimer's disease (AD) based on resting-state fMRI is reviewed, including alterations in regional homogeneity (ReHo) of the fMRI signal in the resting state, and altered patterns of functional connectivity from regions of interest and whole brain analyses.

TL;DR: DaVinci Canvas as discussed by the authors is a telerobotic surgical system with integrated robot-assisted laparoscopic ultrasound capability, which consists of the integration of a rigid laminar ultrasound probe with the daVincenci robot, video tracking of ultrasound probe motions, endoscope and ultrasound calibration and registration, autonomous robot motions, and display of registered 2D and 3D ultrasound images.

TL;DR: A novel scheme for the detection of object boundaries based on active contours evolving in time according to intrinsic geometric measures of the image, allowing stable boundary detection when their gradients suffer from large variations, including gaps.

TL;DR: A novel approach to correcting for intensity nonuniformity in magnetic resonance (MR) data is described that achieves high performance without requiring a model of the tissue classes present, and is applied at an early stage in an automated data analysis, before a tissue model is available.

TL;DR: The authors present a realistic, high-resolution, digital, volumetric phantom of the human brain, which can be used to simulate tomographic images of the head and is the ideal tool to test intermodality registration algorithms.

TL;DR: A parametric model for an implicit representation of the segmenting curve is derived by applying principal component analysis to a collection of signed distance representations of the training data to minimize an objective function for segmentation.