Segmenting ct prostate images using population and patient-Specific statistics for radiotherapy

Qianjin Fenga; Mark Foskey; Songyuan Tang; Wufan Chen; Dinggang Shen

doi:10.1109/ISBI.2009.5193039

Segmenting ct prostate images using population and patient-Specific statistics for radiotherapy

Qianjin Fenga^*, Mark Foskey, Songyuan Tang, Wufan Chen, Dinggang Shen

^*Corresponding author for this work

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

11 Citations (Scopus)

Abstract

This paper presents a new deformable model using both population and patient-specific statistics to segment the prostate from CT images. There are two novelties in the proposed method. First, a modified scale invariant feature transform (SIFT) local descriptor, which is more distinctive than general intensity and gradient features, is used to characterize the image features. Second, an online training approach is used to build the shape statistics for accurately capturing intra-patient variation, which is more important than inter-patient variation for prostate segmentation in clinical radiotherapy. Experimental results show that the proposed method is robust and accurate, suitable for clinical application.

Original language	English
Title of host publication	Proceedings - 2009 IEEE International Symposium on Biomedical Imaging
Subtitle of host publication	From Nano to Macro, ISBI 2009
Pages	282-285
Number of pages	4
DOIs	https://doi.org/10.1109/ISBI.2009.5193039
Publication status	Published - 2009
Externally published	Yes
Event	2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009 - Boston, MA, United States Duration: 28 Jun 2009 → 1 Jul 2009

Publication series

Name	Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009

Conference

Conference	2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009
Country/Territory	United States
City	Boston, MA
Period	28/06/09 → 1/07/09

Keywords

Deformable model
Prostate CT images
SIFT
Segmentation
Shape statistics

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10.1109/ISBI.2009.5193039

Cite this

Fenga, Q., Foskey, M., Tang, S., Chen, W., & Shen, D. (2009). Segmenting ct prostate images using population and patient-Specific statistics for radiotherapy. In Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009 (pp. 282-285). Article 5193039 (Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009). https://doi.org/10.1109/ISBI.2009.5193039

Fenga, Qianjin ; Foskey, Mark ; Tang, Songyuan et al. / Segmenting ct prostate images using population and patient-Specific statistics for radiotherapy. Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009. 2009. pp. 282-285 (Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009).

@inproceedings{e303d21b20d64957b694b7219e60b537,

title = "Segmenting ct prostate images using population and patient-Specific statistics for radiotherapy",

abstract = "This paper presents a new deformable model using both population and patient-specific statistics to segment the prostate from CT images. There are two novelties in the proposed method. First, a modified scale invariant feature transform (SIFT) local descriptor, which is more distinctive than general intensity and gradient features, is used to characterize the image features. Second, an online training approach is used to build the shape statistics for accurately capturing intra-patient variation, which is more important than inter-patient variation for prostate segmentation in clinical radiotherapy. Experimental results show that the proposed method is robust and accurate, suitable for clinical application.",

keywords = "Deformable model, Prostate CT images, SIFT, Segmentation, Shape statistics",

author = "Qianjin Fenga and Mark Foskey and Songyuan Tang and Wufan Chen and Dinggang Shen",

year = "2009",

doi = "10.1109/ISBI.2009.5193039",

language = "English",

isbn = "9781424439324",

series = "Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009",

pages = "282--285",

booktitle = "Proceedings - 2009 IEEE International Symposium on Biomedical Imaging",

note = "2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009 ; Conference date: 28-06-2009 Through 01-07-2009",

}

Fenga, Q, Foskey, M, Tang, S, Chen, W & Shen, D 2009, Segmenting ct prostate images using population and patient-Specific statistics for radiotherapy. in Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009., 5193039, Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009, pp. 282-285, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009, Boston, MA, United States, 28/06/09. https://doi.org/10.1109/ISBI.2009.5193039

Segmenting ct prostate images using population and patient-Specific statistics for radiotherapy. / Fenga, Qianjin; Foskey, Mark; Tang, Songyuan et al.
Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009. 2009. p. 282-285 5193039 (Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009).

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

TY - GEN

T1 - Segmenting ct prostate images using population and patient-Specific statistics for radiotherapy

AU - Fenga, Qianjin

AU - Foskey, Mark

AU - Tang, Songyuan

AU - Chen, Wufan

AU - Shen, Dinggang

PY - 2009

Y1 - 2009

N2 - This paper presents a new deformable model using both population and patient-specific statistics to segment the prostate from CT images. There are two novelties in the proposed method. First, a modified scale invariant feature transform (SIFT) local descriptor, which is more distinctive than general intensity and gradient features, is used to characterize the image features. Second, an online training approach is used to build the shape statistics for accurately capturing intra-patient variation, which is more important than inter-patient variation for prostate segmentation in clinical radiotherapy. Experimental results show that the proposed method is robust and accurate, suitable for clinical application.

AB - This paper presents a new deformable model using both population and patient-specific statistics to segment the prostate from CT images. There are two novelties in the proposed method. First, a modified scale invariant feature transform (SIFT) local descriptor, which is more distinctive than general intensity and gradient features, is used to characterize the image features. Second, an online training approach is used to build the shape statistics for accurately capturing intra-patient variation, which is more important than inter-patient variation for prostate segmentation in clinical radiotherapy. Experimental results show that the proposed method is robust and accurate, suitable for clinical application.

KW - Deformable model

KW - Prostate CT images

KW - SIFT

KW - Segmentation

KW - Shape statistics

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U2 - 10.1109/ISBI.2009.5193039

DO - 10.1109/ISBI.2009.5193039

M3 - Conference contribution

AN - SCOPUS:70449334554

SN - 9781424439324

T3 - Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009

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EP - 285

BT - Proceedings - 2009 IEEE International Symposium on Biomedical Imaging

T2 - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009

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Fenga Q, Foskey M, Tang S, Chen W, Shen D. Segmenting ct prostate images using population and patient-Specific statistics for radiotherapy. In Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009. 2009. p. 282-285. 5193039. (Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009). doi: 10.1109/ISBI.2009.5193039

Segmenting ct prostate images using population and patient-Specific statistics for radiotherapy

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Keywords

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