Multi-atlas labeling with population-specific template and non-local patch-based label fusion
نویسندگان
چکیده
We propose a new method combining a population-specific nonlinear template atlas approach with non-local patch-based structure segmentation for whole brain segmentation into individual structures. This way, we benefit from the efficient intensity-driven segmentation of the non-local means framework and from the global shape constraints imposed by the nonlinear template matching.
منابع مشابه
Robust Patch-Based Multi-Atlas Labeling by Joint Sparsity Regularization
Automated labeling of anatomical structures on MR brain images is widely investigated in many neuroscience and clinic studies to quantitatively measure either individual or group structural/functional difference. To address the issue of registration errors that affect multi-atlas based labeling, patch-based label fusion methods have been recently proposed by inspecting the similarity of each pa...
متن کاملA generative probability model of joint label fusion for multi-atlas based brain segmentation
Automated labeling of anatomical structures in medical images is very important in many neuroscience studies. Recently, patch-based labeling has been widely investigated to alleviate the possible mis-alignment when registering atlases to the target image. However, the weights used for label fusion from the registered atlases are generally computed independently and thus lack the capability of p...
متن کاملRobust multi-atlas label propagation by deep sparse representation
Recently, multi-atlas patch-based label fusion has achieved many successes in medical imaging area. The basic assumption in the current state-of-the-art approaches is that the image patch at the target image point can be represented by a patch dictionary consisting of atlas patches from registered atlas images. Therefore, the label at the target image point can be determined by fusing labels of...
متن کاملSparse Patch-Based Label Fusion for Multi-Atlas Segmentation
Patch-based label fusion methods have shown great potential in multi-atlas segmentation. It is crucial for patch-based labeling methods to determine appropriate graphs and corresponding weights to better link patches in the input image with those in atlas images. Currently, two independent steps are performed, i.e., first constructing graphs based on the fixed image neighborhood and then comput...
متن کاملMulti-atlas segmentation with augmented features for cardiac MR images
Multi-atlas segmentation infers the target image segmentation by combining prior anatomical knowledge encoded in multiple atlases. It has been quite successfully applied to medical image segmentation in the recent years, resulting in highly accurate and robust segmentation for many anatomical structures. However, to guide the label fusion process, most existing multi-atlas segmentation methods ...
متن کامل