收稿日期: 2016-08-07
修回日期: 2016-10-08
网络出版日期: 2017-01-30
基金资助
国家自然科学基金（No.31671006）资助
Multi-task Automatic Prostate Segmentation with Group CT Planning Images
Received date: 2016-08-07
Revised date: 2016-10-08
Online published: 2017-01-30
戴修斌, 邓黄健, 刘代富, 刘可, 周青蓉 . 利用群体CT计划图像的多任务前列腺自动分割[J]. 应用科学学报, 2017 , 35(1) : 99 -106 . DOI: 10.3969/j.issn.0255-8297.2017.01.011
To automatically and accurately segment prostates in CT planning images, a multi-task CT prostate segmentation method is proposed based on group images.The group images with those from other patients are frst mapped to various spaces of reference images to form a multiple training task.The random forest method and the automatic context model are used to train a series of classifers.The trained classifers are then iteratively applied to CT images to be segmented.Multiple classifcation probability maps are thus produced.The fnal segmentation result is obtained using a majority voting method.Experimental results show that, compared with single-task segmentation, proposed multi-task segmentation based on group images can effectively improve accuracy of prostate segmentation for CT planning images.
[1] Shoag J, Halpern J, Lee D J, Mittal S, Ballman K V, Barbieri C E, Hu J C. Decline in prostate cancer screening by primary care physicians:an analysis of trends in the use of digital rectal examination and prostate specifc antigen testing[J]. Journal of Urology Home, 2016, 196(4):1047-1052.
[2] Sharp G, Fritscher K D, Pekar V, Peroni M, Shussharina N, Veeraraghavan H, Yang J. Vision 20/20:Perspectives on automated image segmentation for radiotherapy[J]. Medical Physics, 2014, 41(5):050902-050902.
[3] Chen S, Lovelock D M, Radke R J. Segmenting the prostate and rectum in CT imagery using anatomical constraints[J]. Medical Image Analysis, 2011, 15(1):1-11.
[4] Feng Q J, Foskey M, Chen W F, Shen D G. Segmenting CT prostate images using population and patient-specifc statistics for radiotherapy[J]. Medical Physics, 2010, 37(8):4121-4132.
[5] Davis B C, Foskey M, Rosenman J, Goyal L, Chang S, Joshi S. Automatic segmentation of intra-treatment CT images for adaptive radiation therapy of the prostate[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention, 2005:442-450.
[6] Liao S, Shen D G. A feature-based learning framework for accurate prostate localization in CT images[J]. IEEE Transactions on Image Processing, 2012, 21(8):3546-3559.
[7] Li W, Liao S, Feng Q J, Chen W F, Shen D G. Learning image context for segmentation of the prostate in CT-guided radiotherapy[J]. Physics in Medicine and Biology, 2012, 57(5):1283-1308.
[8] Gao Y Z, Liao S, Shen D G. Prostate segmentation by sparse representation based classifcation[J]. Medical Physics, 2012, 39(10):6372-6387.
[9] 邵叶秦,杨新. 基于随机森林的CT前列腺分割[J]. CT理论与应用研究, 2015, 24(5):647-655. Shao Y Q, Yang X. CT prostate segmentation based on random forest[J]. CT Theory and Applications, 2015, 24(5):647-655. (in Chinese)
[10] Zhang L, Suganthan P N. Random forests with ensemble of feature spaces[J]. Pattern Recognition, 2014, 47(10):3429-3437.
[11] Tu Z W, Bai X. Auto-context and its application to high-level vision tasks and 3d brain image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(10):1744-1757.
[12] Kim M J, Wu G R, Li W, Wang L, Son Y D, Cho Z H, Shen D G. Automatic hippocampus segmentation of 7.0 Tesla MR images by combining multiple atlases and auto-context models[J]. Neuroimage, 2013, 83(12):335-345.
[13] Foskey M, Davis B, Goyal L, Chang S, Chaney E, Strehl N, Tomei S, Rosenman J, Joshi S. Large deformation three-dimensional image registration in image-guided radiation therapy[J]. Physics in Medicine and Biology, 2005, 50(24):5869-5892.
/
| 〈 |
|
〉 |
