Segmenting images by combining selected atlases on manifold

Yihui Cao, Yuan Yuan, Xuelong Li, Baris Turkbey, Peter L. Choyke, Pingkun Yan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

35 Scopus citations

Abstract

Atlas selection and combination are two critical factors affecting the performance of atlas-based segmentation methods. In the existing works, those tasks are completed in the original image space. However, the intrinsic similarity between the images may not be accurately reflected by the Euclidean distance in this high-dimensional space. Thus, the selected atlases may be away from the input image and the generated template by combining those atlases for segmentation can be misleading. In this paper, we propose to select and combine atlases by projecting the images onto a low-dimensional manifold. With this approach, atlases can be selected according to their intrinsic similarity to the patient image. A novel method is also proposed to compute the weights for more efficiently combining the selected atlases to achieve better segmentation performance. The experimental results demonstrated that our proposed method is robust and accurate, especially when the number of training samples becomes large.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - 14th International Conference, Proceedings
Pages272-279
Number of pages8
EditionPART 3
DOIs
StatePublished - 2011
Externally publishedYes
Event14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011 - Toronto, ON, Canada
Duration: 18 Sep 201122 Sep 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume6893 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011
Country/TerritoryCanada
CityToronto, ON
Period18/09/1122/09/11

Fingerprint

Dive into the research topics of 'Segmenting images by combining selected atlases on manifold'. Together they form a unique fingerprint.

Cite this