利用群体CT计划图像的多任务前列腺自动分割

doi:10.3969/j.issn.0255-8297.2017.01.011

Abstract

Abstract:

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.

Key words: group images, CT planning images, multiple training task, radiation treatment, prostate segmentation

CLC Number:

TP391

DAI Xiu-bin, Deng Huang-jian, LIU Dai-fu, LIU Ke, ZHOU Qing-rong. Multi-task Automatic Prostate Segmentation with Group CT Planning Images[J]. Journal of Applied Sciences, 2017, 35(1): 99-106.

References

[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.

[1]	YAO Rujing, YANG Lei, YANG Tao, HU Yingxin, TIAN Qiang, WU Ou. Analysis for Psychological Scale Big Data Based on Improved Ising Model [J]. Journal of Applied Sciences, 2020, 38(3): 339-352.
[2]	JIANG Qinyin, ZHANG Xi. Topic-Specific Assessment Approach for Social Network Influence Evaluation [J]. Journal of Applied Sciences, 2020, 38(3): 353-366.
[3]	ZHANG Tongtong, DONG Junyu, ZHAO Haoran, LI Qiong, SUN Xin. Lightweight Phytoplankton Detection Network Based on Knowledge Distillation [J]. Journal of Applied Sciences, 2020, 38(3): 367-376.
[4]	WEI Ziyang, ZHAO Zhihong, ZHAO Jingjiao. Improved Faster R-CNN Algorithm and Its Application on Vehicle Detection [J]. Journal of Applied Sciences, 2020, 38(3): 377-387.
[5]	SUN Ronghai, SHI Linfu, YU Chunqiang, LAO Huan, TANG Zhenjun. Random Interpolation Method for Data Hiding in Encrypted Images [J]. Journal of Applied Sciences, 2020, 38(3): 419-430.
[6]	WANG Daqian, CUI Rongyi, JIN Jingxuan. Text Detection in Natural Scene Based on Visual Attention Model and Multi-scale MSER [J]. Journal of Applied Sciences, 2020, 38(3): 496-506.
[7]	YANG Gaoqiang, LI Sheng, ZHANG Xinpeng. Altered Fingerprint Detection Based on Orientation Field [J]. Journal of Applied Sciences, 2019, 37(6): 775-782.
[8]	YU Shuzhen, OU Qiaofeng, XIONG Bangshu, CHEN Yaofeng. A Calibration Method Based on Defocused Image for Stereo Vision Sensor with Large FOV [J]. Journal of Applied Sciences, 2019, 37(6): 795-805.
[9]	CUI Sanshuai, MAO Maoyu, LIN Xiaodan, KANG Xiangui. Survey on Analysis and Applications of Electric Network Frequency (ENF) Signals in Image and Video [J]. Journal of Applied Sciences, 2019, 37(5): 573-589.
[10]	LIU Wei, ZHANG Wanling, XIANG Shijun. Face Anti-spoofing Based on LBP, Multilayer DCT and CNN [J]. Journal of Applied Sciences, 2019, 37(5): 609-617.
[11]	QU Lingfeng, CHEN fan, HE Hongjie, YUAN yuan. Security Analysis of Image Encryption Algorithm Based on Bit Plane-Pixel Block Scrambling [J]. Journal of Applied Sciences, 2019, 37(5): 631-642.
[12]	ZHAO Yan, ZHOU Xiaowei, SHEN Qi. Compact Image Hashing Algorithm Based on Opposite Color and Salient Region [J]. Journal of Applied Sciences, 2019, 37(5): 691-703.
[13]	ZHANG Yongsheng, TIAN Huawei, XIAO Yanhui, HAO Xinze, ZHANG Mingwang. PRNU Extraction Algorithm Based on Trilateral Weighted Sparse Coding Model [J]. Journal of Applied Sciences, 2019, 37(5): 704-710.
[14]	ZHU Yiming, CHEN Fan, HE Hongjie, CHEN Hongyou. Orthogonal GAN Information Hiding Model Based on Secret Information Driven [J]. Journal of Applied Sciences, 2019, 37(5): 721-732.
[15]	WANG Junxiang, MAO Ningxiong, ZHAO Yi, WANG Chuntao. Large Color Image Based High-Performance Reversible Data Hiding Hcheme for Various Capacities [J]. Journal of Applied Sciences, 2019, 37(5): 744-760.

Multi-task Automatic Prostate Segmentation with Group CT Planning Images

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments