CT prostate deformable segmentation by boundary regression

Yeqin Shao; Yaozong Gao; Xin Yang; Dinggang Shen

doi:10.1007/978-3-319-13972-2_12

CT prostate deformable segmentation by boundary regression

Yeqin Shao, Yaozong Gao, Xin Yang, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

Automatic and accurate prostate segmentation from CT images is challenging due to low image contrast, uncertain organ motion, and variable organ appearance in different patient images. To deal with these challenges, we propose a new prostate boundary detection method with a boundary regression strategy for prostate deformable segmentation. Different from the previous regression-based segmentation methods, which train one regression forest for each specific point (e.g., each point on a shape model), our method learns a single global regression forest to predict the nearest boundary points from each voxel for enhancing the entire prostate boundary. The experimental results show that our proposed boundary regression method outperforms the conventional prostate classification method. Compared with other state-of-the-art methods, our method also shows a competitive performance.

Original language	English
Title of host publication	Medical Computer Vision
Subtitle of host publication	Algorithms for Big Data - International Workshop, MCV 2014 held in Conjunction with MICCAI 2014, Revised Selected Papers
Editors	Henning Müller, Bjoern Menze, Shaoting Zhang, Weidong (Tom) Cai, Bjoern Menze, Georg Langs, Dimitris Metaxas, Georg Langs, Henning Müller, Michael Kelm, Albert Montillo, Weidong (Tom) Cai
Publisher	Springer Verlag
Pages	127-136
Number of pages	10
ISBN (Electronic)	9783319139715
DOIs	https://doi.org/10.1007/978-3-319-13972-2_12
Publication status	Published - 2014
Event	International Workshop on Medical Computer Vision: Algorithms for Big Data was held in conjunction with 17th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI-bigMCV 2014 - Cambridge, United States Duration: 2014 Sept 18 → 2014 Sept 18

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8848
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	International Workshop on Medical Computer Vision: Algorithms for Big Data was held in conjunction with 17th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI-bigMCV 2014
Country/Territory	United States
City	Cambridge
Period	14/9/18 → 14/9/18

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2014.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-13972-2_12

Cite this

Shao, Y., Gao, Y., Yang, X., & Shen, D. (2014). CT prostate deformable segmentation by boundary regression. In H. Müller, B. Menze, S. Zhang, W. Cai, B. Menze, G. Langs, D. Metaxas, G. Langs, H. Müller, M. Kelm, A. Montillo, & W. Cai (Eds.), Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2014 held in Conjunction with MICCAI 2014, Revised Selected Papers (pp. 127-136). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8848). Springer Verlag. https://doi.org/10.1007/978-3-319-13972-2_12

CT prostate deformable segmentation by boundary regression. / Shao, Yeqin; Gao, Yaozong; Yang, Xin et al.
Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2014 held in Conjunction with MICCAI 2014, Revised Selected Papers. ed. / Henning Müller; Bjoern Menze; Shaoting Zhang; Weidong (Tom) Cai; Bjoern Menze; Georg Langs; Dimitris Metaxas; Georg Langs; Henning Müller; Michael Kelm; Albert Montillo; Weidong (Tom) Cai. Springer Verlag, 2014. p. 127-136 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8848).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Shao, Y, Gao, Y, Yang, X & Shen, D 2014, CT prostate deformable segmentation by boundary regression. in H Müller, B Menze, S Zhang, W Cai, B Menze, G Langs, D Metaxas, G Langs, H Müller, M Kelm, A Montillo & W Cai (eds), Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2014 held in Conjunction with MICCAI 2014, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8848, Springer Verlag, pp. 127-136, International Workshop on Medical Computer Vision: Algorithms for Big Data was held in conjunction with 17th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI-bigMCV 2014, Cambridge, United States, 14/9/18. https://doi.org/10.1007/978-3-319-13972-2_12

Shao Y, Gao Y, Yang X, Shen D. CT prostate deformable segmentation by boundary regression. In Müller H, Menze B, Zhang S, Cai W, Menze B, Langs G, Metaxas D, Langs G, Müller H, Kelm M, Montillo A, Cai W, editors, Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2014 held in Conjunction with MICCAI 2014, Revised Selected Papers. Springer Verlag. 2014. p. 127-136. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-13972-2_12

Shao, Yeqin ; Gao, Yaozong ; Yang, Xin et al. / CT prostate deformable segmentation by boundary regression. Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2014 held in Conjunction with MICCAI 2014, Revised Selected Papers. editor / Henning Müller ; Bjoern Menze ; Shaoting Zhang ; Weidong (Tom) Cai ; Bjoern Menze ; Georg Langs ; Dimitris Metaxas ; Georg Langs ; Henning Müller ; Michael Kelm ; Albert Montillo ; Weidong (Tom) Cai. Springer Verlag, 2014. pp. 127-136 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "CT prostate deformable segmentation by boundary regression",

abstract = "Automatic and accurate prostate segmentation from CT images is challenging due to low image contrast, uncertain organ motion, and variable organ appearance in different patient images. To deal with these challenges, we propose a new prostate boundary detection method with a boundary regression strategy for prostate deformable segmentation. Different from the previous regression-based segmentation methods, which train one regression forest for each specific point (e.g., each point on a shape model), our method learns a single global regression forest to predict the nearest boundary points from each voxel for enhancing the entire prostate boundary. The experimental results show that our proposed boundary regression method outperforms the conventional prostate classification method. Compared with other state-of-the-art methods, our method also shows a competitive performance.",

author = "Yeqin Shao and Yaozong Gao and Xin Yang and Dinggang Shen",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.; International Workshop on Medical Computer Vision: Algorithms for Big Data was held in conjunction with 17th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI-bigMCV 2014 ; Conference date: 18-09-2014 Through 18-09-2014",

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N2 - Automatic and accurate prostate segmentation from CT images is challenging due to low image contrast, uncertain organ motion, and variable organ appearance in different patient images. To deal with these challenges, we propose a new prostate boundary detection method with a boundary regression strategy for prostate deformable segmentation. Different from the previous regression-based segmentation methods, which train one regression forest for each specific point (e.g., each point on a shape model), our method learns a single global regression forest to predict the nearest boundary points from each voxel for enhancing the entire prostate boundary. The experimental results show that our proposed boundary regression method outperforms the conventional prostate classification method. Compared with other state-of-the-art methods, our method also shows a competitive performance.

AB - Automatic and accurate prostate segmentation from CT images is challenging due to low image contrast, uncertain organ motion, and variable organ appearance in different patient images. To deal with these challenges, we propose a new prostate boundary detection method with a boundary regression strategy for prostate deformable segmentation. Different from the previous regression-based segmentation methods, which train one regression forest for each specific point (e.g., each point on a shape model), our method learns a single global regression forest to predict the nearest boundary points from each voxel for enhancing the entire prostate boundary. The experimental results show that our proposed boundary regression method outperforms the conventional prostate classification method. Compared with other state-of-the-art methods, our method also shows a competitive performance.

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CT prostate deformable segmentation by boundary regression

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