Multi-atlas and Gaussian Mixture Modeling Based Perirectal Fat Segmentation from CT Images

Ghose, Soumya, Denham, Jim, Ebert, Martin, Kennedy, Angel, Mitra, Jhimli, Rose, Stephen and Dowling, Jason (2013). Multi-atlas and Gaussian Mixture Modeling Based Perirectal Fat Segmentation from CT Images. In: Yoshida, H, Warfield, S and Vannier, MW, Abdominal Imaging: Computation and Clinical Applications. 5th International Workshop on Abdominal Imaging - Computational and Clinical Applications, Nagoya, Japan, (194-202). 22 September 2013. doi:10.1007/978-3-642-41083-3_22


Author Ghose, Soumya
Denham, Jim
Ebert, Martin
Kennedy, Angel
Mitra, Jhimli
Rose, Stephen
Dowling, Jason
Title of paper Multi-atlas and Gaussian Mixture Modeling Based Perirectal Fat Segmentation from CT Images
Conference name 5th International Workshop on Abdominal Imaging - Computational and Clinical Applications
Conference location Nagoya, Japan
Conference dates 22 September 2013
Proceedings title Abdominal Imaging: Computation and Clinical Applications   Check publisher's open access policy
Journal name Abdominal Imaging: Computation and Clinical Applications   Check publisher's open access policy
Place of Publication New York, NY, United States
Publisher Springer
Publication Year 2013
Year available 2013
Sub-type Fully published paper
DOI 10.1007/978-3-642-41083-3_22
Open Access Status Not yet assessed
ISBN 9783642410833
ISSN 0302-9743
Editor Yoshida, H
Warfield, S
Vannier, MW
Volume 8198
Start page 194
End page 202
Total pages 9
Language eng
Abstract/Summary Accurate perirectal fat segmentation in CT images aids in estimating radiation dose delivered to the region of fat around the rectum during radiation therapy treatment of prostate cancer. Such a process is important in determining the resulting toxicity of the neighboring tissues. However automatic or semi-automatic segmentation of the perirectal fat in CT images is a challenging task due to inter patient anatomical variability, contrast variability and imaging artifacts. We propose a combined schema of multi-atlas and multi parametric Gaussian mixture modeling for perirectal fat segmentation in CT images. Multi-atlas based soft segmentation and multi parametric Gaussian mixture modeling aids in identifying the volume of interest (VOI). Thereafter expectation maximization (EM) based soft clustering of the intensities of the VOI refined with positional probabilities of the perirectal fat provides the segmentation of the perirectal fat. The proposed method achieves a mean sensitivity value of 0.88 +/- 0.07 and a mean specificity value of 0.998 +/- 0.001 with 5 patient datasets in a leave-one-patient-out validation framework. Qualitative results show a good approximation of the perirectal fat volume compared to the ground truth.
Keyword Multi-atlas
Gaussian mixture modeling
Computed tomography
Mr-Images
Prostate
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Conference Paper
Sub-type: Abdominal Imaging: Computation and Clinical Applications
Collection: UQ Centre for Clinical Research Publications
 
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