Sparse patch based prostate segmentation in CT images

Shu Liao, Yaozong Gao, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Citations (Scopus)

Abstract

Automatic prostate segmentation plays an important role in image guided radiation therapy. However, accurate prostate segmentation in CT images remains as a challenging problem mainly due to three issues: Low image contrast, large prostate motions, and image appearance variations caused by bowel gas. In this paper, a new patient-specific prostate segmentation method is proposed to address these three issues. The main contributions of our method lie in the following aspects: (1) A new patch based representation is designed in the discriminative feature space to effectively distinguish voxels belonging to the prostate and non-prostate regions. (2) The new patch based representation is integrated with a new sparse label propagation framework to segment the prostate, where candidate voxels with low patch similarity can be effectively removed based on sparse representation. (3) An online update mechanism is adopted to capture more patient-specific information from treatment images scanned in previous treatment days. The proposed method has been extensively evaluated on a prostate CT image dataset consisting of 24 patients with 330 images in total. It is also compared with several state-of-the-art prostate segmentation approaches, and experimental results demonstrate that our proposed method can achieve higher segmentation accuracy than other methods under comparison.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings
EditorsNicholas Ayache, Herve Delingette, Polina Golland, Kensaku Mori
PublisherSpringer Verlag
Pages385-392
Number of pages8
ISBN (Print)9783642334535
DOIs
Publication statusPublished - 2012
Event15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012 - Nice, France
Duration: 2012 Oct 12012 Oct 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7512 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012
CountryFrance
CityNice
Period12/10/112/10/5

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Liao, S., Gao, Y., & Shen, D. (2012). Sparse patch based prostate segmentation in CT images. In N. Ayache, H. Delingette, P. Golland, & K. Mori (Eds.), Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings (pp. 385-392). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7512 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-642-33454-2_48