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Automatic Prostate MR Image Segmentation with Sparse Label Propagation and Domain-Specific Manifold Regularization
| Content Provider | Semantic Scholar |
|---|---|
| Author | Liao, Shu Gao, Yaozong Shi, Yinghuan Yousuf, Ambereen Karademir, Ibrahim Oto, Aytekin Shen, Dinggang |
| Copyright Year | 2013 |
| Abstract | 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. |
| Starting Page | 139 |
| Ending Page | 170 |
| Page Count | 32 |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.med.unc.edu/bric/ideagroup/Publications/articles/IPMI2013_Liao_Auto.pdf |
| Alternate Webpage(s) | https://www.med.unc.edu/bric/ideagroup/Publications/articles/IPMI2013_Liao_Auto.pdf |
| Alternate Webpage(s) | https://www.med.unc.edu/bric/ideagroup/Publications/articles/IPMI2013_Liao_Auto.pdf/ |
| Alternate Webpage(s) | https://www.wikidata.org/entity/Q38601488 |
| PubMed reference number | 24683995v1 |
| Alternate Webpage(s) | https://doi.org/10.1007/978-3-642-38868-2_43 |
| DOI | 10.1007/978-3-642-38868-2_43 |
| Journal | IPMI |
| Volume Number | 23 |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Atlases Domain-specific language Feature vector Image segmentation Linear discriminant analysis Manifold regularization Matrix regularization Numerous Patients Projections and Predictions Prostatic Neoplasms Segmentation action Semi-supervised learning Semiconductor industry Silo (dataset) Software propagation Sparse matrix Strand Displacement Amplification Telling untruths Tracer Type signature Voxel biologic segmentation subclass |
| Content Type | Text |
| Resource Type | Article |
