TY - GEN
T1 - Automatic prostate MR image segmentation with sparse label propagation and domain-specific manifold regularization
AU - Liao, Shu
AU - Gao, Yaozong
AU - Shi, Yinghuan
AU - Yousuf, Ambereen
AU - Karademir, Ibrahim
AU - Oto, Aytekin
AU - Shen, Dinggang
PY - 2013/7/12
Y1 - 2013/7/12
N2 - Automatic prostate segmentation in MR images plays an important role in prostate cancer diagnosis. However, there are two main challenges: (1) Large inter-subject prostate shape variations; (2) Inhomogeneous prostate appearance. To address these challenges, we propose a new hierarchical prostate MR segmentation method, with the main contributions lying in the following aspects: First, the most salient features are learnt from atlases based on a subclass discriminant analysis (SDA) method, which aims to find a discriminant feature subspace by simultaneously maximizing the inter-class distance and minimizing the intra-class variations. The projected features, instead of only voxel-wise intensity, will be served as anatomical signature of each voxel. Second, based on the projected features, a new multi-atlases sparse label fusion framework is proposed to estimate the prostate likelihood of each voxel in the target image from the coarse level. Third, a domain-specific semi-supervised manifold regularization method is proposed to incorporate the most reliable patient-specific information identified by the prostate likelihood map to refine the segmentation result from the fine level. Our method is evaluated on a T2 weighted prostate MR image dataset consisting of 66 patients and compared with two state-of-the-art segmentation methods. Experimental results show that our method consistently achieves the highest segmentation accuracies than other methods under comparison.
AB - Automatic prostate segmentation in MR images plays an important role in prostate cancer diagnosis. However, there are two main challenges: (1) Large inter-subject prostate shape variations; (2) Inhomogeneous prostate appearance. To address these challenges, we propose a new hierarchical prostate MR segmentation method, with the main contributions lying in the following aspects: First, the most salient features are learnt from atlases based on a subclass discriminant analysis (SDA) method, which aims to find a discriminant feature subspace by simultaneously maximizing the inter-class distance and minimizing the intra-class variations. The projected features, instead of only voxel-wise intensity, will be served as anatomical signature of each voxel. Second, based on the projected features, a new multi-atlases sparse label fusion framework is proposed to estimate the prostate likelihood of each voxel in the target image from the coarse level. Third, a domain-specific semi-supervised manifold regularization method is proposed to incorporate the most reliable patient-specific information identified by the prostate likelihood map to refine the segmentation result from the fine level. Our method is evaluated on a T2 weighted prostate MR image dataset consisting of 66 patients and compared with two state-of-the-art segmentation methods. Experimental results show that our method consistently achieves the highest segmentation accuracies than other methods under comparison.
UR - http://www.scopus.com/inward/record.url?scp=84901279625&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84901279625&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-38868-2_43
DO - 10.1007/978-3-642-38868-2_43
M3 - Conference contribution
C2 - 24683995
AN - SCOPUS:84901279625
SN - 9783642388675
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 511
EP - 523
BT - Information Processing in Medical Imaging - 23rd International Conference, IPMI 2013, Proceedings
T2 - 23rd International Conference on Information Processing in Medical Imaging, IPMI 2013
Y2 - 28 June 2013 through 3 July 2013
ER -