SU‐E‐T‐607: Accurate Prostate Localization in CT Images with a Learning Based Hierarchical Framework

S. Liao, Dinggang Shen

Research output: Contribution to journalArticle

Abstract

Purpose: Accurate localization of prostate in CT images plays an important role in image guided radiation therapy. However, it has two main challenges. The first challenge is due to the low contrast in CT images to localize the prostate. The other challenge is due to the uncertainty of the existence of bowel gas. In this work an automatic prostate localization method is proposed to address these two challenges. Methods: The proposed method extracts anatomical features are from input images, and the best features at distinctive image regions are learnt to localize the prostate. An online update mechanism is adopted to adaptively combine both the inter‐ patient and patient‐specific information during the learning process. An explicit similarity measure function is built based on the learnt features to align the planning image to the treatment images. The prostate in the treatment image thus can be localized by transforming the segmented prostate in the training image to the space of the treatment image. Results: We evaluate the proposed method on a 3D CT prostate dataset consisting of 10 patients. Each patient has more than 10 CT scans. The manual segmentation results provided by clinical experts are also available. The segmentation accuracy is evaluated based on two quantitative measures: The centroid distance and the Dice ratio between the estimated prostate and the manual segmented prostate. for all the patients, the 25th and 75th percentiles of the centroid distances are within 1 mm error and the average dice ratio can reach 89%. Conclusions: The proposed method can achieve high prostate segmentation accuracies in CT images. Most importantly, the proposed method is highly flexible in clinical application as high segmentation accuracies can be achieved even in the case that only the planning image of the underlying patient is available.

Original languageEnglish
Pages (from-to)3629
Number of pages1
JournalMedical Physics
Volume38
Issue number6
DOIs
Publication statusPublished - 2011
Externally publishedYes

Fingerprint

Prostate
Learning
Image-Guided Radiotherapy
Uncertainty
Therapeutics
Gases

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging

Cite this

SU‐E‐T‐607 : Accurate Prostate Localization in CT Images with a Learning Based Hierarchical Framework. / Liao, S.; Shen, Dinggang.

In: Medical Physics, Vol. 38, No. 6, 2011, p. 3629.

Research output: Contribution to journalArticle

@article{cf29a27c4a74419a84880e0204da5533,
title = "SU‐E‐T‐607: Accurate Prostate Localization in CT Images with a Learning Based Hierarchical Framework",
abstract = "Purpose: Accurate localization of prostate in CT images plays an important role in image guided radiation therapy. However, it has two main challenges. The first challenge is due to the low contrast in CT images to localize the prostate. The other challenge is due to the uncertainty of the existence of bowel gas. In this work an automatic prostate localization method is proposed to address these two challenges. Methods: The proposed method extracts anatomical features are from input images, and the best features at distinctive image regions are learnt to localize the prostate. An online update mechanism is adopted to adaptively combine both the inter‐ patient and patient‐specific information during the learning process. An explicit similarity measure function is built based on the learnt features to align the planning image to the treatment images. The prostate in the treatment image thus can be localized by transforming the segmented prostate in the training image to the space of the treatment image. Results: We evaluate the proposed method on a 3D CT prostate dataset consisting of 10 patients. Each patient has more than 10 CT scans. The manual segmentation results provided by clinical experts are also available. The segmentation accuracy is evaluated based on two quantitative measures: The centroid distance and the Dice ratio between the estimated prostate and the manual segmented prostate. for all the patients, the 25th and 75th percentiles of the centroid distances are within 1 mm error and the average dice ratio can reach 89{\%}. Conclusions: The proposed method can achieve high prostate segmentation accuracies in CT images. Most importantly, the proposed method is highly flexible in clinical application as high segmentation accuracies can be achieved even in the case that only the planning image of the underlying patient is available.",
author = "S. Liao and Dinggang Shen",
year = "2011",
doi = "10.1118/1.3612570",
language = "English",
volume = "38",
pages = "3629",
journal = "Medical Physics",
issn = "0094-2405",
publisher = "AAPM - American Association of Physicists in Medicine",
number = "6",

}

TY - JOUR

T1 - SU‐E‐T‐607

T2 - Accurate Prostate Localization in CT Images with a Learning Based Hierarchical Framework

AU - Liao, S.

AU - Shen, Dinggang

PY - 2011

Y1 - 2011

N2 - Purpose: Accurate localization of prostate in CT images plays an important role in image guided radiation therapy. However, it has two main challenges. The first challenge is due to the low contrast in CT images to localize the prostate. The other challenge is due to the uncertainty of the existence of bowel gas. In this work an automatic prostate localization method is proposed to address these two challenges. Methods: The proposed method extracts anatomical features are from input images, and the best features at distinctive image regions are learnt to localize the prostate. An online update mechanism is adopted to adaptively combine both the inter‐ patient and patient‐specific information during the learning process. An explicit similarity measure function is built based on the learnt features to align the planning image to the treatment images. The prostate in the treatment image thus can be localized by transforming the segmented prostate in the training image to the space of the treatment image. Results: We evaluate the proposed method on a 3D CT prostate dataset consisting of 10 patients. Each patient has more than 10 CT scans. The manual segmentation results provided by clinical experts are also available. The segmentation accuracy is evaluated based on two quantitative measures: The centroid distance and the Dice ratio between the estimated prostate and the manual segmented prostate. for all the patients, the 25th and 75th percentiles of the centroid distances are within 1 mm error and the average dice ratio can reach 89%. Conclusions: The proposed method can achieve high prostate segmentation accuracies in CT images. Most importantly, the proposed method is highly flexible in clinical application as high segmentation accuracies can be achieved even in the case that only the planning image of the underlying patient is available.

AB - Purpose: Accurate localization of prostate in CT images plays an important role in image guided radiation therapy. However, it has two main challenges. The first challenge is due to the low contrast in CT images to localize the prostate. The other challenge is due to the uncertainty of the existence of bowel gas. In this work an automatic prostate localization method is proposed to address these two challenges. Methods: The proposed method extracts anatomical features are from input images, and the best features at distinctive image regions are learnt to localize the prostate. An online update mechanism is adopted to adaptively combine both the inter‐ patient and patient‐specific information during the learning process. An explicit similarity measure function is built based on the learnt features to align the planning image to the treatment images. The prostate in the treatment image thus can be localized by transforming the segmented prostate in the training image to the space of the treatment image. Results: We evaluate the proposed method on a 3D CT prostate dataset consisting of 10 patients. Each patient has more than 10 CT scans. The manual segmentation results provided by clinical experts are also available. The segmentation accuracy is evaluated based on two quantitative measures: The centroid distance and the Dice ratio between the estimated prostate and the manual segmented prostate. for all the patients, the 25th and 75th percentiles of the centroid distances are within 1 mm error and the average dice ratio can reach 89%. Conclusions: The proposed method can achieve high prostate segmentation accuracies in CT images. Most importantly, the proposed method is highly flexible in clinical application as high segmentation accuracies can be achieved even in the case that only the planning image of the underlying patient is available.

UR - http://www.scopus.com/inward/record.url?scp=85024801783&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85024801783&partnerID=8YFLogxK

U2 - 10.1118/1.3612570

DO - 10.1118/1.3612570

M3 - Article

AN - SCOPUS:85024801783

VL - 38

SP - 3629

JO - Medical Physics

JF - Medical Physics

SN - 0094-2405

IS - 6

ER -