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)
T2 - 14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011
Y2 - 18 September 2011 through 22 September 2011
ER -
