Three-dimensional CTLiver Image Segmentation Based on Hierarchical Contextual Active Contour_Journal of Biomedical Engineering

Authors：

 JIHongwei , HEJiangping , YANGXin

Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China;

Corresponding?author：

JIHongwei, Email: hongweiji@sjtu.edu.cn

Keywords：

liver segmentation; active contour; context feature; hierarchical contextual active contour; shape model

DOI：

10.7507/1001-5515.20140076

Video：

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Abstract Full text Figures/Tables Video References Cited by

In this paper, we propose a new active contour algorithm, i.e. hierarchical contextual active contour (HCAC), and apply it to automatic liver segmentation from three-dimensional CT (3D-CT) images. HCAC is a learning-based method and can be divided into two stages. At the first stage, i.e. the training stage, given a set of abdominal 3D-CT training images and the corresponding manual liver labels, we tried to establish a mapping between automatic segmentations (in each round) and manual reference segmentations via context features, and obtained a series of self-correcting classifiers. At the second stage, i.e. the segmentation stage, we firstly used the basic active contour to segment the image and subsequently used the contextual active contour (CAC) iteratively, which combines the image information and the current shape model, to improve the segmentation result. The current shape model is produced by the corresponding self-correcting classifier (the input is the previous automatic segmentation result). The proposed method was evaluated on the datasets of MICCAI 2007 liver segmentation challenge. The experimental results showed that we would get more and more accurate segmentation results by the iterative steps and the satisfied results would be obtained after about six rounds of iterations.

Citation： JIHongwei, HEJiangping, YANGXin. Three-dimensional CTLiver Image Segmentation Based on Hierarchical Contextual Active Contour. Journal of Biomedical Engineering, 2014, 31(2): 405-412. doi: 10.7507/1001-5515.20140076 Copy

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1. MEINZER H P,THORN M,CáRDENAS C E.Computerized planning of liver surgery——an overview[J].Comput Graph,2002,26(4):569-576.
2. RADTKE A,NADALIN S,SOTIROPOULOS G C,et al.Computer-assisted operative planning in adult living donor liver transplantation:A new way to resolve the dilemma of the middle hepatic vein[J].World J Surg,2007,31(1):175-185.
3. CHEN E L,CHUNG P C,CHEN C L,et al.An automatic diagnostic system for CT liver image classification[J].IEEE Trans Biomed Eng,1998,45(6):783-794.
4. GLETSOS M,MOUGIAKAKOU S G,MATSOPOULOS G K,et al.A computer-aided diagnostic system to characterize CT focal liver lesions:design and optimization of a neural network classifier[J].IEEE Trans Inf Technol Biomed,2003,7(3):153-162.
5. HEIMANN T,THORN M,KUNERT T,et al.New method for leak detection and contour correction in seeded region growing segmentation[C]//Proceedings of 20th ISPRS Congress.Istanbul,Turkey:2004,XXXV:317-322.
6. CHANEY E,IBBOTT G.Methods for image segmentation should be standardized and calibrated.For the proposition[J].Med Phys,2005,32(12):3507-3508.
7. STYNER M A,CHARLES H C,PARK J,et al.Multi-site validation of image analysis methods-assessing intra and inter-site variability[C]//Proc SPIE Med Imag.San Diego:2002,4684:278-286.
8. CLARKE L P,VELTHUIZEN R P,CAMACHO M A,et al.MRI segmentation:methods and applications[J].Magn Reson Imaging,1995,13(3):343-368.
9. YUSHKEVICH P A,PIVEN J,HAZLETT H C,et al.User-guided 3D active contour segmentation of anatomical structures:significantly improved efficiency and reliability[J].Neuroimage,2006,31(3):1116-1128.
10. RUSKóL,BEKES G,NéMETH G,et al.Fully automatic liver segmentation for contrast-enhanced CT images[C]//Proc MICCAI Workshop 3-D Segmentat Clinic:a Grand Challenge.Brisbane,Australia:2007:143-150.
11. SCHMIDT G,ATHELOGOU M A,SCHONMEYER R,et al.Cognition network technology for a fully automated 3-dsegmentation of liver[C]//Proc MICCAI Workshop 3-D Segmentat Clinic:a Grand Challenge.Brisbane,Australia:2007:125-133.
12. HEIMANN T,MEINZER H P,WOLF I.A statistical deformable model for the segmentation of liver CT volumes[C]//Proc MICCAI Workshop 3-D Segmentat Clinic:a Grand Challenge.Brisbane,Australia:2007:161-166.
13. COOTES T F,HILL A,TAYLOR C J,et al.The use of active shape models for locating structures in medical images[J].Image Vis Comput,1994,12(6):355-365.
14. ZHANG X,TIAN J,DENG K,et al.Automatic liver segmentation using a statistical shape model with optimal surface detection[J].IEEE Trans Biomed Eng,2010,57(10):2622-2626.
15. SADDI K A,ROUSSON M,CHEFDHOTEL C,et al.Global-to-local shape matching for liver segmentation in CT imaging[C]//Proc MICCAI Workshop 3-D Segmentat Clinic:a Grand Challenge.Brisbane,Australia:2007:207-214.
16. BADAKHSHANNOORY H,SAEEDI P.A model-based validation scheme for organ segmentation in CT scan volumes[J].IEEE Trans Biomed Eng,2011,58(9):2681-2693.
17. BEICHEL R,BAUER C,BORNIK A,et al.Live segmentation in CT data:a segmentation refinement approach[C]//Proc MICCAI Workshop 3-D Segmentat Clinic:a Grand Challenge.Brisbane,Australia:2007:235-245.
18. BOYKOV Y,FUNKA-LEA G.Graph cuts and efficient n-d image segmentation[J].Int J Comput Vis,2006,70(2):109-131.
19. BECK A,AURICH V.Hepatux-a semiautomatic liver segmentation system[C]//Proc MICCAI Workshop 3-D Segmentat Clinic:a Grand Challenge.Brisbane,Australia:2007:225-234.
20. CHAN T F,VESE L A.Active contours without edges[J].IEEE Trans Image Process,2001,10(2):266-277.
21. TU Z,BAI X.Auto-context and its application to high-level vision tasks and 3D brain image segmentation[J].IEEE Trans Pattern Anal Mach Intell,2010,32(10):1744-1757.

Journal of Biomedical Engineering

Three-dimensional CTLiver Image Segmentation Based on Hierarchical Contextual Active Contour

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