Titlebook: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015; 18th International C Nassir Navab,Joachim Hornegger,Alejandro F.

可觸知

Segmentation of Infant Hippocampus Using Common Feature Representations Learned for Multimodal Longiar, neuropsychiatric disorders, such as attention deficit hyperactivity disorder (ADHD), have been linked with abnormal early development of the hippocampus. Despite its known importance, studying the hippocampus in infant subjects is very challenging due to the significantly smaller brain size, dyn

Insul島

悄悄移動(dòng)

LAY

Automatic 3D US Brain Ventricle Segmentation in Pre-Term Neonates Using Multi-phase Geodesic Level-Saging has been used to quantitatively monitor the ventricular volume in IVH neonates, instead of typical 2D US used clinically, which relies on linear measurements from a single slice and visually estimates to determine ventricular dilation. To translate 3D US imaging into clinical setting, an accur

Cardiac-Output

Multiple Surface Segmentation Using Truncated Convex Priors fast multiple surface segmentation approach with truncated convex priors for a segmentation problem, in which there exist abrupt surface distance changes between mutually interacting surface pairs. A 3-D graph theoretic framework based on . is employed. The use of truncated convex priors enables to

施魔法

amphibian

Acupressure

Corpus Callosum Segmentation in MS Studies Using Normal Atlases and Optimal Hybridization of Extrinsltiple sclerosis (MS). A number of automatic methods have been proposed for CC segmentation in magnetic resonance images (MRIs) that can be broadly classified as intensity-based and template-based. Imaging artifacts and signal changes due to pathology often cause errors in intensity-based methods. T

flaggy

Brain Tissue Segmentation Based on Diffusion MRI Using ?0 Sparse-Group Representation Classificationand facilitates fusion of information between the two imaging modalities. Unlike existing segmentation approaches that are based on diffusion tensor imaging (DTI), our method explicitly models the coexistence of various diffusion compartments within each voxel owing to different tissue types and dif

善辯

A Latent Source Model for Patch-Based Image Segmentationere has been no theoretical development on when, why, and how well these nonparametric methods work. We bridge this gap by providing a theoretical performance guarantee for nearest-neighbor and weighted majority voting segmentation under a new probabilistic model for patch-based image segmentation.