Discriminative dictionary learning for abdominal multi-organ segmentation

Tong Tong, Robin Wolz, Zehan Wang, Qinquan Gao, Kazunari Misawa, Michitaka Fujiwara, Kensaku Mori, Joseph V. Hajnal, Daniel Rueckert

Research output: Contribution to journalArticlepeer-review

130 Scopus citations


An automated segmentation method is presented for multi-organ segmentation in abdominal CT images. Dictionary learning and sparse coding techniques are used in the proposed method to generate target specific priors for segmentation. The method simultaneously learns dictionaries which have reconstructive power and classifiers which have discriminative ability from a set of selected atlases. Based on the learnt dictionaries and classifiers, probabilistic atlases are then generated to provide priors for the segmentation of unseen target images. The final segmentation is obtained by applying a post-processing step based on a graph-cuts method. In addition, this paper proposes a voxel-wise local atlas selection strategy to deal with high inter-subject variation in abdominal CT images. The segmentation performance of the proposed method with different atlas selection strategies are also compared. Our proposed method has been evaluated on a database of 150 abdominal CT images and achieves a promising segmentation performance with Dice overlap values of 94.9%, 93.6%, 71.1%, and 92.5% for liver, kidneys, pancreas, and spleen, respectively.

Original languageEnglish
Pages (from-to)92-104
Number of pages13
JournalMedical Image Analysis
Issue number1
StatePublished - 1 Jul 2015
Externally publishedYes


  • Abdominal multi-organ segmentation
  • Discriminative dictionary learning
  • Local atlas selection
  • Patch based


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