Sparse patch-based label propagation for accurate prostate localization in CT images

Shu Liao, Yaozong Gao, Jun Lian, Dinggang Shen

Research output: Contribution to journalArticle

58 Citations (Scopus)

Abstract

In this paper, we propose a new prostate computed tomography (CT) segmentation method for image guided radiation therapy. The main contributions of our method lie in the following aspects. 1) Instead of using voxel intensity information alone, patch-based representation in the discriminative feature space with logistic sparse LASSO is used as anatomical signature to deal with low contrast problem in prostate CT images. 2) Based on the proposed patch-based signature, a new multi-atlases label fusion method formulated under sparse representation framework is designed to segment prostate in the new treatment images, with guidance from the previous segmented images of the same patient. This method estimates the prostate likelihood of each voxel in the new treatment image from its nearby candidate voxels in the previous segmented images, based on the nonlocal mean principle and sparsity constraint. 3) A hierarchical labeling strategy is further designed to perform label fusion, where voxels with high confidence are first labeled for providing useful context information in the same image for aiding the labeling of the remaining voxels. 4) An online update mechanism is finally adopted to progressively collect more patient-specific information from newly segmented treatment images of the same patient, for adaptive and more accurate segmentation. The proposed method has been extensively evaluated on a prostate CT image database consisting of 24 patients where each patient has more than 10 treatment images, and further compared with several state-of-the-art prostate CT segmentation algorithms using various evaluation metrics. Experimental results demonstrate that the proposed method consistently achieves higher segmentation accuracy than any other methods under comparison.

Original languageEnglish
Article number6362228
Pages (from-to)419-434
Number of pages16
JournalIEEE Transactions on Medical Imaging
Volume32
Issue number2
DOIs
Publication statusPublished - 2013 Feb 8
Externally publishedYes

Fingerprint

Tomography
Labels
Prostate
Labeling
Fusion reactions
Radiotherapy
Logistics
Image-Guided Radiotherapy
Atlases
Therapeutics
Databases

Keywords

  • Image guided radiation therapy (IGRT)
  • online update mechanism
  • patch-based representation
  • prostate segmentation
  • sparse label propagation

ASJC Scopus subject areas

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

Cite this

Sparse patch-based label propagation for accurate prostate localization in CT images. / Liao, Shu; Gao, Yaozong; Lian, Jun; Shen, Dinggang.

In: IEEE Transactions on Medical Imaging, Vol. 32, No. 2, 6362228, 08.02.2013, p. 419-434.

Research output: Contribution to journalArticle

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