Learning of atlas forest hierarchy for automatic labeling of MR brain images

Lichi Zhang, Qian Wang, Yaozong Gao, Guorong Wu, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

We propose a multi-atlas-based framework to label brain anatomies in magnetic resonance (MR) images, by constructing a hierarchical structure of atlas forests. We start by training the atlas forests in accordance to individual atlases, and then cluster atlas forests with similar labeling performances into several groups. For each group, a new representative forest is re-trained, based on all atlas images associated with the atlas forests in the group, as well as the tentative label maps output by the clustered atlas forests. This clustering and re-training procedure is conducted iteratively to obtain a hierarchical structure of atlas forests. When applied to an unlabeled image for testing, only the suitable trained atlas forests will be selected from the hierarchical structure. Hence the labeling result of the test image is fused from the outputs of selected atlas forests. Experimental results show that the proposed framework can significantly improve the labeling performance compared to the state-of-the-art method.

Original languageEnglish
Pages (from-to)323-330
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8679
DOIs
Publication statusPublished - 2014
Externally publishedYes

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Learning of atlas forest hierarchy for automatic labeling of MR brain images'. Together they form a unique fingerprint.

Cite this