Iterative multi-atlas-based multi-image segmentation with tree-based registration

Iterative multi-atlas-based multi-image segmentation with tree-based registration

In this paper, we present a multi-atlas-based framework for accurate, consistent and simultaneous segmentation of a group of target images. Multi-atlas-based segmentation algorithms consider concurrently complementary information from multiple atlases to produce optimal segmentation outcomes. However, the accuracy of these algorithms relies heavily on the precise alignment of the atlases with t...

Shape-constrained multi-atlas based segmentation with multichannel registration

Multi-atlas based segmentation methods have recently attracted much attention in medical image segmentation. The multi-atlas based segmentation methods typically consist of three steps, including image registration, label propagation, and label fusion. Most of the recent studies devote to improving the label fusion step and adopt a typical image registration method for registering atlases to th...

Multi-Modal Image Registration and Atlas Formation

Multi-Atlas based Segmentation of Multi-Modal Brain Images

Brain image analysis is playing a fundamental role in clinical and population-based epidemiological studies. Several brain disorder studies involve quantitative interpretation of brain scans and particularly require accurate measurement and delineation of tissue volumes in the scans. Automatic segmentation methods have been proposed to provide reliability and accuracy of the labelling as well a...

Multi-Atlas-based Segmentation with Hierarchical Max-Flow

This study investigates a method for brain tissue segmentation from 3D T1 weighted (T1w) MR images via convex relaxation with a hierarchical ordering constraint. It employs a multi-atlas-based initialization from 5 training images and is tested on 12 T1w MR images provided by the MICCAI 2013 MRBrainS segmentation challenge. The registered atlas images, fully segmented into eight different brain...