Transductive prostate segmentation for CT image guided radiotherapy

Yinghuan Shi, Shu Liao, Yaozong Gao, Daoqiang Zhang, Yang Gao, Dinggang Shen

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

5 Citations (Scopus)

Abstract

Accurate 3-D prostate segmentation is a significant and challenging issue for CT image guided radiotherapy. In this paper, a novel transductive method for 3-D prostate segmentation is proposed, which incorporates the physician's interactive labeling information, to aid accurate segmentation, especially when large irregular prostate motion occurs. More specifically, for the current treatment image, the physician is first asked to manually assign the labels for a small subset of prostate and non-prostate (background) voxels, especially in the first and last slices of the prostate regions. Then, transductive Lasso (tLasso) is proposed to select the most discriminative features slice-by-slice. With the selected features, our proposed weighted Laplacian regularized least squares (wLapRLS) is adopted to predict the prostate-likelihood for each remaining unlabeled voxel in the current treatment image. The final segmentation result is obtained by aligning the manually segmented prostate regions of the planning and previous treatment images, onto the estimated prostate-likelihood map of the current treatment image for majority voting. The proposed method has been evaluated on a real prostate CT dataset including 11 patients with more than 160 images, and compared with several state-of-the-art methods. Experimental results indicate that the promising results can be achieved by our proposed method.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages1-9
Number of pages9
Volume7588 LNCS
DOIs
Publication statusPublished - 2012 Nov 30
Externally publishedYes
Event3rd International Workshop on Machine Learning in Medical Imaging, MLMI 2012, Held in conjunction with the 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012 - Nice, France
Duration: 2012 Oct 12012 Oct 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7588 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other3rd International Workshop on Machine Learning in Medical Imaging, MLMI 2012, Held in conjunction with the 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012
CountryFrance
CityNice
Period12/10/112/10/1

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ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Shi, Y., Liao, S., Gao, Y., Zhang, D., Gao, Y., & Shen, D. (2012). Transductive prostate segmentation for CT image guided radiotherapy. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7588 LNCS, pp. 1-9). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7588 LNCS). https://doi.org/10.1007/978-3-642-35428-1_1