CT prostate deformable segmentation by boundary regression

Yeqin Shao, Yaozong Gao, Xin Yang, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages127-136
Number of pages10
Volume8848
ISBN (Print)9783319139715
DOIs
Publication statusPublished - 2014 Jan 1
Externally publishedYes
EventInternational 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 Sep 182014 Sep 18

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8848
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

OtherInternational 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
CountryUnited States
CityCambridge
Period14/9/1814/9/18

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Shao, Y., Gao, Y., Yang, X., & Shen, D. (2014). CT prostate deformable segmentation by boundary regression. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8848, 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