Abstract
In recent years, there has been a great interest in prostate segmentation, which is an important and challenging task for CT image guided radiotherapy. In this paper, a learning-based segmentation method via joint transductive feature selection and transductive regression is presented, which incorporates the physician's simple manual specification (only taking a few seconds), to aid accurate segmentation, especially for the case with large irregular prostate motion. More specifically, for the current treatment image, experienced physician is first allowed to manually assign the labels for a small subset of prostate and non-prostate voxels, especially in the first and last slices of the prostate regions. Then, the proposed method follows the two step: in prostate-likelihood estimation step, two novel algorithms, tLasso and wLapRLS, will be sequentially employed for transductive feature selection and transductive regression, respectively, aiming to generate the prostate-likelihood map. In multi-atlases based label fusion step, the final segmentation result will be obtained according to the corresponding prostate-likelihood map and the previous images of the same patient. The proposed method has been substantially evaluated on a real prostate CT dataset including 24 patients with 330 CT images, and compared with several state-of-the-art methods. Experimental results show that the proposed method outperforms the state-of-the-arts in terms of higher Dice ratio, higher true positive fraction, and lower centroid distances. Also, the results demonstrate that simple manual specification can help improve the segmentation performance, which is clinically feasible in real practice.
| Original language | English |
|---|---|
| Pages (from-to) | 317-331 |
| Number of pages | 15 |
| Journal | Neurocomputing |
| Volume | 173 |
| DOIs | |
| Publication status | Published - 2016 Jan 15 |
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Keywords
- Feature selection
- Prostate segmentation
- Transductive learning
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Cognitive Neuroscience
Cite this
A learning-based CT prostate segmentation method via joint transductive feature selection and regression. / Shi, Yinghuan; Gao, Yaozong; Liao, Shu; Zhang, Daoqiang; Gao, Yang; Shen, Dinggang.
In: Neurocomputing, Vol. 173, 15.01.2016, p. 317-331.Research output: Contribution to journal › Article
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TY - JOUR
T1 - A learning-based CT prostate segmentation method via joint transductive feature selection and regression
AU - Shi, Yinghuan
AU - Gao, Yaozong
AU - Liao, Shu
AU - Zhang, Daoqiang
AU - Gao, Yang
AU - Shen, Dinggang
PY - 2016/1/15
Y1 - 2016/1/15
N2 - In recent years, there has been a great interest in prostate segmentation, which is an important and challenging task for CT image guided radiotherapy. In this paper, a learning-based segmentation method via joint transductive feature selection and transductive regression is presented, which incorporates the physician's simple manual specification (only taking a few seconds), to aid accurate segmentation, especially for the case with large irregular prostate motion. More specifically, for the current treatment image, experienced physician is first allowed to manually assign the labels for a small subset of prostate and non-prostate voxels, especially in the first and last slices of the prostate regions. Then, the proposed method follows the two step: in prostate-likelihood estimation step, two novel algorithms, tLasso and wLapRLS, will be sequentially employed for transductive feature selection and transductive regression, respectively, aiming to generate the prostate-likelihood map. In multi-atlases based label fusion step, the final segmentation result will be obtained according to the corresponding prostate-likelihood map and the previous images of the same patient. The proposed method has been substantially evaluated on a real prostate CT dataset including 24 patients with 330 CT images, and compared with several state-of-the-art methods. Experimental results show that the proposed method outperforms the state-of-the-arts in terms of higher Dice ratio, higher true positive fraction, and lower centroid distances. Also, the results demonstrate that simple manual specification can help improve the segmentation performance, which is clinically feasible in real practice.
AB - In recent years, there has been a great interest in prostate segmentation, which is an important and challenging task for CT image guided radiotherapy. In this paper, a learning-based segmentation method via joint transductive feature selection and transductive regression is presented, which incorporates the physician's simple manual specification (only taking a few seconds), to aid accurate segmentation, especially for the case with large irregular prostate motion. More specifically, for the current treatment image, experienced physician is first allowed to manually assign the labels for a small subset of prostate and non-prostate voxels, especially in the first and last slices of the prostate regions. Then, the proposed method follows the two step: in prostate-likelihood estimation step, two novel algorithms, tLasso and wLapRLS, will be sequentially employed for transductive feature selection and transductive regression, respectively, aiming to generate the prostate-likelihood map. In multi-atlases based label fusion step, the final segmentation result will be obtained according to the corresponding prostate-likelihood map and the previous images of the same patient. The proposed method has been substantially evaluated on a real prostate CT dataset including 24 patients with 330 CT images, and compared with several state-of-the-art methods. Experimental results show that the proposed method outperforms the state-of-the-arts in terms of higher Dice ratio, higher true positive fraction, and lower centroid distances. Also, the results demonstrate that simple manual specification can help improve the segmentation performance, which is clinically feasible in real practice.
KW - Feature selection
KW - Prostate segmentation
KW - Transductive learning
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U2 - 10.1016/j.neucom.2014.11.098
DO - 10.1016/j.neucom.2014.11.098
M3 - Article
AN - SCOPUS:84948665052
VL - 173
SP - 317
EP - 331
JO - Neurocomputing
JF - Neurocomputing
SN - 0925-2312
ER -