Learning image context for segmentation of prostate in CT-guided radiotherapy

Wei Li, Shu Liao, Qianjin Feng, Wufan Chen, Dinggang Shen

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

Abstract

Segmentation of prostate is highly important in the external beam radiotherapy of prostate cancer. However, it is challenging to localize prostate in the CT images due to low image contrast, prostate motion, and both intensity and shape changes of bladder and rectum around the prostate. In this paper, an online learning and patient-specific classification method based on location-adaptive image context is proposed to precisely segment prostate in the CT image. Specifically, two sets of position-adaptive classifiers are respectively placed along the two coordinate directions, and further trained with the previous segmented treatment images to jointly perform the prostate segmentation. In particular, each location-adaptive classifier is recursively trained with different image context collected at different scales and orientations for better identification of each prostate region. The proposed learning-based prostate segmentation method has been extensively evaluated on a large set of patients, achieving very promising results.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages570-578
Number of pages9
Volume6893 LNCS
EditionPART 3
DOIs
Publication statusPublished - 2011 Oct 11
Externally publishedYes
Event14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011 - Toronto, ON, Canada
Duration: 2011 Sep 182011 Sep 22

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume6893 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011
CountryCanada
CityToronto, ON
Period11/9/1811/9/22

Fingerprint

Radiotherapy
Classifiers
Segmentation
CT Image
Classifier
Prostate Cancer
Online Learning
Large Set
Motion
Context
Learning

Keywords

  • Classification
  • Image context
  • Prostate segmentation
  • Radiotherapy

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Li, W., Liao, S., Feng, Q., Chen, W., & Shen, D. (2011). Learning image context for segmentation of prostate in CT-guided radiotherapy. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 3 ed., Vol. 6893 LNCS, pp. 570-578). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6893 LNCS, No. PART 3). https://doi.org/10.1007/978-3-642-23626-6_70

Learning image context for segmentation of prostate in CT-guided radiotherapy. / Li, Wei; Liao, Shu; Feng, Qianjin; Chen, Wufan; Shen, Dinggang.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6893 LNCS PART 3. ed. 2011. p. 570-578 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6893 LNCS, No. PART 3).

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

Li, W, Liao, S, Feng, Q, Chen, W & Shen, D 2011, Learning image context for segmentation of prostate in CT-guided radiotherapy. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 3 edn, vol. 6893 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 6893 LNCS, pp. 570-578, 14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011, Toronto, ON, Canada, 11/9/18. https://doi.org/10.1007/978-3-642-23626-6_70
Li W, Liao S, Feng Q, Chen W, Shen D. Learning image context for segmentation of prostate in CT-guided radiotherapy. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 3 ed. Vol. 6893 LNCS. 2011. p. 570-578. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). https://doi.org/10.1007/978-3-642-23626-6_70
Li, Wei ; Liao, Shu ; Feng, Qianjin ; Chen, Wufan ; Shen, Dinggang. / Learning image context for segmentation of prostate in CT-guided radiotherapy. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6893 LNCS PART 3. ed. 2011. pp. 570-578 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).
@inproceedings{6c09d75dd0af460ea40a7836d8c80132,
title = "Learning image context for segmentation of prostate in CT-guided radiotherapy",
abstract = "Segmentation of prostate is highly important in the external beam radiotherapy of prostate cancer. However, it is challenging to localize prostate in the CT images due to low image contrast, prostate motion, and both intensity and shape changes of bladder and rectum around the prostate. In this paper, an online learning and patient-specific classification method based on location-adaptive image context is proposed to precisely segment prostate in the CT image. Specifically, two sets of position-adaptive classifiers are respectively placed along the two coordinate directions, and further trained with the previous segmented treatment images to jointly perform the prostate segmentation. In particular, each location-adaptive classifier is recursively trained with different image context collected at different scales and orientations for better identification of each prostate region. The proposed learning-based prostate segmentation method has been extensively evaluated on a large set of patients, achieving very promising results.",
keywords = "Classification, Image context, Prostate segmentation, Radiotherapy",
author = "Wei Li and Shu Liao and Qianjin Feng and Wufan Chen and Dinggang Shen",
year = "2011",
month = "10",
day = "11",
doi = "10.1007/978-3-642-23626-6_70",
language = "English",
isbn = "9783642236259",
volume = "6893 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 3",
pages = "570--578",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
edition = "PART 3",

}

TY - GEN

T1 - Learning image context for segmentation of prostate in CT-guided radiotherapy

AU - Li, Wei

AU - Liao, Shu

AU - Feng, Qianjin

AU - Chen, Wufan

AU - Shen, Dinggang

PY - 2011/10/11

Y1 - 2011/10/11

N2 - Segmentation of prostate is highly important in the external beam radiotherapy of prostate cancer. However, it is challenging to localize prostate in the CT images due to low image contrast, prostate motion, and both intensity and shape changes of bladder and rectum around the prostate. In this paper, an online learning and patient-specific classification method based on location-adaptive image context is proposed to precisely segment prostate in the CT image. Specifically, two sets of position-adaptive classifiers are respectively placed along the two coordinate directions, and further trained with the previous segmented treatment images to jointly perform the prostate segmentation. In particular, each location-adaptive classifier is recursively trained with different image context collected at different scales and orientations for better identification of each prostate region. The proposed learning-based prostate segmentation method has been extensively evaluated on a large set of patients, achieving very promising results.

AB - Segmentation of prostate is highly important in the external beam radiotherapy of prostate cancer. However, it is challenging to localize prostate in the CT images due to low image contrast, prostate motion, and both intensity and shape changes of bladder and rectum around the prostate. In this paper, an online learning and patient-specific classification method based on location-adaptive image context is proposed to precisely segment prostate in the CT image. Specifically, two sets of position-adaptive classifiers are respectively placed along the two coordinate directions, and further trained with the previous segmented treatment images to jointly perform the prostate segmentation. In particular, each location-adaptive classifier is recursively trained with different image context collected at different scales and orientations for better identification of each prostate region. The proposed learning-based prostate segmentation method has been extensively evaluated on a large set of patients, achieving very promising results.

KW - Classification

KW - Image context

KW - Prostate segmentation

KW - Radiotherapy

UR - http://www.scopus.com/inward/record.url?scp=80053542451&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80053542451&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-23626-6_70

DO - 10.1007/978-3-642-23626-6_70

M3 - Conference contribution

SN - 9783642236259

VL - 6893 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 570

EP - 578

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -