TY - JOUR
T1 - Interactive prostate segmentation based on adaptive feature selection and manifold regularization
AU - Park, Sang Hyun
AU - Gao, Yaozong
AU - Shi, Yinghuan
AU - Shen, Dinggang
N1 - Funding Information:
This work was supported in part by NIH grant CA140413.
PY - 2014
Y1 - 2014
N2 - In this paper, we propose a new learning-based interactive editing method for prostate segmentation. Although many automatic methods have been proposed to segment the prostate, laborious manual correction is still required for many clinical applications due to the limited performance of automatic segmentation. The proposed method is able to flexibly correct wrong parts of the segmentation within a short time, even few scribbles or dots are provided. In order to obtain the robust correction with a few interactions, the discriminative features that can represent mid-level cues beyond image intensity or gradient are adaptively extracted from a local region of interest according to both the training set and the interaction. Then, the labeling problem is formulated as a semi-supervised learning task, which is aimed to preserve the manifold configuration between the labeled and unlabeled voxels. The proposed method is evaluated on a challenging prostate CT image data set with large shape and appearance variations. The automatic segmentation results originally with the average Dice of 0.766 were improved to the average Dice 0.866 after conducting totally 22 interactions for the 12 test images by using our proposed method.
AB - In this paper, we propose a new learning-based interactive editing method for prostate segmentation. Although many automatic methods have been proposed to segment the prostate, laborious manual correction is still required for many clinical applications due to the limited performance of automatic segmentation. The proposed method is able to flexibly correct wrong parts of the segmentation within a short time, even few scribbles or dots are provided. In order to obtain the robust correction with a few interactions, the discriminative features that can represent mid-level cues beyond image intensity or gradient are adaptively extracted from a local region of interest according to both the training set and the interaction. Then, the labeling problem is formulated as a semi-supervised learning task, which is aimed to preserve the manifold configuration between the labeled and unlabeled voxels. The proposed method is evaluated on a challenging prostate CT image data set with large shape and appearance variations. The automatic segmentation results originally with the average Dice of 0.766 were improved to the average Dice 0.866 after conducting totally 22 interactions for the 12 test images by using our proposed method.
KW - Feature selection
KW - Interactive segmentation
KW - Manifold regularization
KW - Prostate
KW - Semisupervised learning
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U2 - 10.1007/978-3-319-10581-9_33
DO - 10.1007/978-3-319-10581-9_33
M3 - Article
AN - SCOPUS:84921759769
SN - 0302-9743
VL - 8679
SP - 264
EP - 271
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -