Automatic prostate MR image segmentation with sparse label propagation and domain-specific manifold regularization

Shu Liao, Yaozong Gao, Yinghuan Shi, Ambereen Yousuf, Ibrahim Karademir, Aytekin Oto, Dinggang Shen

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

Abstract

Automatic prostate segmentation in MR images plays an important role in prostate cancer diagnosis. However, there are two main challenges: (1) Large inter-subject prostate shape variations; (2) Inhomogeneous prostate appearance. To address these challenges, we propose a new hierarchical prostate MR segmentation method, with the main contributions lying in the following aspects: First, the most salient features are learnt from atlases based on a subclass discriminant analysis (SDA) method, which aims to find a discriminant feature subspace by simultaneously maximizing the inter-class distance and minimizing the intra-class variations. The projected features, instead of only voxel-wise intensity, will be served as anatomical signature of each voxel. Second, based on the projected features, a new multi-atlases sparse label fusion framework is proposed to estimate the prostate likelihood of each voxel in the target image from the coarse level. Third, a domain-specific semi-supervised manifold regularization method is proposed to incorporate the most reliable patient-specific information identified by the prostate likelihood map to refine the segmentation result from the fine level. Our method is evaluated on a T2 weighted prostate MR image dataset consisting of 66 patients and compared with two state-of-the-art segmentation methods. Experimental results show that our method consistently achieves the highest segmentation accuracies than other methods under comparison.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages511-523
Number of pages13
Volume7917 LNCS
DOIs
Publication statusPublished - 2013 Jul 12
Externally publishedYes
Event23rd International Conference on Information Processing in Medical Imaging, IPMI 2013 - Asilomar, CA, United States
Duration: 2013 Jun 282013 Jul 3

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7917 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other23rd International Conference on Information Processing in Medical Imaging, IPMI 2013
CountryUnited States
CityAsilomar, CA
Period13/6/2813/7/3

Fingerprint

Discriminant analysis
Image segmentation
Image Segmentation
Labels
Regularization
Fusion reactions
Propagation
Segmentation
Voxel
Atlas
Likelihood
Prostate Cancer
Regularization Method
Discriminant Analysis
Discriminant
Fusion
Signature
Subspace
Target
Experimental Results

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Liao, S., Gao, Y., Shi, Y., Yousuf, A., Karademir, I., Oto, A., & Shen, D. (2013). Automatic prostate MR image segmentation with sparse label propagation and domain-specific manifold regularization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7917 LNCS, pp. 511-523). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7917 LNCS). https://doi.org/10.1007/978-3-642-38868-2_43

Automatic prostate MR image segmentation with sparse label propagation and domain-specific manifold regularization. / Liao, Shu; Gao, Yaozong; Shi, Yinghuan; Yousuf, Ambereen; Karademir, Ibrahim; Oto, Aytekin; Shen, Dinggang.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7917 LNCS 2013. p. 511-523 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7917 LNCS).

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

Liao, S, Gao, Y, Shi, Y, Yousuf, A, Karademir, I, Oto, A & Shen, D 2013, Automatic prostate MR image segmentation with sparse label propagation and domain-specific manifold regularization. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 7917 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7917 LNCS, pp. 511-523, 23rd International Conference on Information Processing in Medical Imaging, IPMI 2013, Asilomar, CA, United States, 13/6/28. https://doi.org/10.1007/978-3-642-38868-2_43
Liao S, Gao Y, Shi Y, Yousuf A, Karademir I, Oto A et al. Automatic prostate MR image segmentation with sparse label propagation and domain-specific manifold regularization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7917 LNCS. 2013. p. 511-523. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-38868-2_43
Liao, Shu ; Gao, Yaozong ; Shi, Yinghuan ; Yousuf, Ambereen ; Karademir, Ibrahim ; Oto, Aytekin ; Shen, Dinggang. / Automatic prostate MR image segmentation with sparse label propagation and domain-specific manifold regularization. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7917 LNCS 2013. pp. 511-523 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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