TY - GEN
T1 - Sparse patch based prostate segmentation in CT images
AU - Liao, Shu
AU - Gao, Yaozong
AU - Shen, Dinggang
N1 - Funding Information:
Acknowledgment. This work was supported by NIH grant CA140413.
Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2012.
PY - 2012
Y1 - 2012
N2 - Automatic prostate segmentation plays an important role in image guided radiation therapy. However, accurate prostate segmentation in CT images remains as a challenging problem mainly due to three issues: Low image contrast, large prostate motions, and image appearance variations caused by bowel gas. In this paper, a new patient-specific prostate segmentation method is proposed to address these three issues. The main contributions of our method lie in the following aspects: (1) A new patch based representation is designed in the discriminative feature space to effectively distinguish voxels belonging to the prostate and non-prostate regions. (2) The new patch based representation is integrated with a new sparse label propagation framework to segment the prostate, where candidate voxels with low patch similarity can be effectively removed based on sparse representation. (3) An online update mechanism is adopted to capture more patient-specific information from treatment images scanned in previous treatment days. The proposed method has been extensively evaluated on a prostate CT image dataset consisting of 24 patients with 330 images in total. It is also compared with several state-of-the-art prostate segmentation approaches, and experimental results demonstrate that our proposed method can achieve higher segmentation accuracy than other methods under comparison.
AB - Automatic prostate segmentation plays an important role in image guided radiation therapy. However, accurate prostate segmentation in CT images remains as a challenging problem mainly due to three issues: Low image contrast, large prostate motions, and image appearance variations caused by bowel gas. In this paper, a new patient-specific prostate segmentation method is proposed to address these three issues. The main contributions of our method lie in the following aspects: (1) A new patch based representation is designed in the discriminative feature space to effectively distinguish voxels belonging to the prostate and non-prostate regions. (2) The new patch based representation is integrated with a new sparse label propagation framework to segment the prostate, where candidate voxels with low patch similarity can be effectively removed based on sparse representation. (3) An online update mechanism is adopted to capture more patient-specific information from treatment images scanned in previous treatment days. The proposed method has been extensively evaluated on a prostate CT image dataset consisting of 24 patients with 330 images in total. It is also compared with several state-of-the-art prostate segmentation approaches, and experimental results demonstrate that our proposed method can achieve higher segmentation accuracy than other methods under comparison.
UR - http://www.scopus.com/inward/record.url?scp=84872943332&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-33454-2_48
DO - 10.1007/978-3-642-33454-2_48
M3 - Conference contribution
C2 - 23286154
AN - SCOPUS:84872943332
SN - 9783642334535
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 385
EP - 392
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings
A2 - Ayache, Nicholas
A2 - Delingette, Herve
A2 - Golland, Polina
A2 - Mori, Kensaku
PB - Springer Verlag
T2 - 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012
Y2 - 1 October 2012 through 5 October 2012
ER -