Learning to rank atlases for multiple-atlas segmentation

Gerard Sanroma, Guorong Wu, Yaozong Gao, Dinggang Shen

Research output: Contribution to journalArticle

41 Citations (Scopus)

Abstract

Recently, multiple-atlas segmentation (MAS) has achieved a great success in the medical imaging area. The key assumption is that multiple atlases have greater chances of correctly labeling a target image than a single atlas. However, the problem of atlas selection still remains unexplored. Traditionally, image similarity is used to select a set of atlases. Unfortunately, this heuristic criterion is not necessarily related to the final segmentation performance. To solve this seemingly simple but critical problem, we propose a learning-based atlas selection method to pick up the best atlases that would lead to a more accurate segmentation. Our main idea is to learn the relationship between the pairwise appearance of observed instances (i.e., a pair of atlas and target images) and their final labeling performance (e.g., using the Dice ratio). In this way, we select the best atlases based on their expected labeling accuracy. Our atlas selection method is general enough to be integrated with any existing MAS method. We show the advantages of our atlas selection method in an extensive experimental evaluation in the ADNI, SATA, IXI, and LONI LPBA40 datasets. As shown in the experiments, our method can boost the performance of three widely used MAS methods, outperforming other learning-based and image-similarity-based atlas selection methods.

Original languageEnglish
Article number6823729
Pages (from-to)1939-1953
Number of pages15
JournalIEEE Transactions on Medical Imaging
Volume33
Issue number10
DOIs
Publication statusPublished - 2014 Oct 1

Keywords

  • Atlas selection
  • feature selection
  • multi-atlas based segmentation
  • support vector machine (SVM) rank

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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