title: Discriminative dictionary learning for abdominal multi-organ segmentation.
abstract: 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.
authors.affiliation: Biomedical Image Analysis Group, Department of Computing, Imperial College London, 180 Queen's Gate, London SW7 2AZ, UK. Electronic address: t.tong11@imperial.ac.uk.
authors.forename: Tong
authors.initials: T
authors.lastname: Tong
authors.affiliation: Biomedical Image Analysis Group, Department of Computing, Imperial College London, 180 Queen's Gate, London SW7 2AZ, UK.
authors.forename: Robin
authors.initials: R
authors.lastname: Wolz
authors.affiliation: Biomedical Image Analysis Group, Department of Computing, Imperial College London, 180 Queen's Gate, London SW7 2AZ, UK.
authors.forename: Zehan
authors.initials: Z
authors.lastname: Wang
authors.affiliation: Biomedical Image Analysis Group, Department of Computing, Imperial College London, 180 Queen's Gate, London SW7 2AZ, UK.
authors.forename: Qinquan
authors.initials: Q
authors.lastname: Gao
authors.affiliation: Aichi Cancer Center, Nagoya 464-8681, Japan.
authors.forename: Kazunari
authors.initials: K
authors.lastname: Misawa
authors.affiliation: Nagoya University Hospital, Nagoya 466-0065, Japan.
authors.forename: Michitaka
authors.initials: M
authors.lastname: Fujiwara
authors.affiliation: Information and Communications, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan.
authors.forename: Kensaku
authors.initials: K
authors.lastname: Mori
authors.affiliation: Center for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas Hospital, London SE1 7EH, UK.
authors.forename: Joseph V
authors.initials: JV
authors.lastname: Hajnal
authors.affiliation: Biomedical Image Analysis Group, Department of Computing, Imperial College London, 180 Queen's Gate, London SW7 2AZ, UK.
authors.forename: Daniel
authors.initials: D
authors.lastname: Rueckert
mesh_terms: D000465:Algorithms; D006801:Humans; D007668:Kidney / Q000000981:diagnostic imaging; D008099:Liver / Q000000981:diagnostic imaging; D010179:Pancreas / Q000000981:diagnostic imaging; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011860:Radiography, Abdominal / Q000379:methods*; D013154:Spleen / Q000000981:diagnostic imaging; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Abdominal multi-organ segmentation; Discriminative dictionary learning; Local atlas selection; Patch based