Automated Liver Segmentation for Cone Beam CT Dataset by Probabilistic Atlas Construction

Cone beam CT based image guided radiation therapy can be used to measure and correct positional errors for target and critical structures immediately prior to or during the treatment delivery. Data correlation between Planning CT images and daily CBCT images is the key issue for adaptive radiation therapy, including image registration and segmentation processing. In this paper, aiming for getting accurate liver contour structures automatically in daily CBCT images which is very low-contrast comparing the planning CT, probabilistic atlas is constructed from 50 high contrast planning CT images with manual delineation by oncologist. The incoming CBCT images are registered with the atlas using the deformable registration algorithm, and the liver contour structures are generated automatically by using the deformation map. The experiment results demonstrate the efficiency of our algorithm.

[1]  Gabriele Lombardi,et al.  Automatic liver segmentation from abdominal CT scans , 2007, 14th International Conference on Image Analysis and Processing (ICIAP 2007).

[2]  Martin Styner,et al.  Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets , 2009, IEEE Transactions on Medical Imaging.

[3]  Lei Xing,et al.  Multiscale registration of planning CT and daily cone beam CT images for adaptive radiation therapy. , 2009, Medical physics.

[4]  Xiangrong Zhou,et al.  Construction of a probabilistic atlas for automated liver segmentation in non-contrast torso CT images , 2005 .

[5]  Terry E. Weymouth,et al.  Multiple organ definition in CT using a Bayesian approach for 3D model fitting , 1995, Optics & Photonics.

[6]  F. X. Bosch,et al.  The epidemiology of primary liver cancer: global epidemiology , 2002 .

[7]  Guido Gerig,et al.  Valmet: A New Validation Tool for Assessing and Improving 3D Object Segmentation , 2001, MICCAI.

[8]  Nelson Lam,et al.  Radiation dose from cone beam computed tomography for image-guided radiation therapy. , 2008, International Journal of Radiation Oncology, Biology, Physics.

[9]  Yong Yin,et al.  Multiscale registration for noisy medical images , 2010, 2010 International Conference On Computer Design and Applications.

[10]  Yong Yin,et al.  A new multiscale registration method for medical image , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[11]  Elena Casiraghi,et al.  Liver Segmentation from CT Scans: A Survey , 2007, WILF.

[12]  Yen-Wei Chen,et al.  Automated segmentation of the liver from 3D CT images using probabilistic atlas and multilevel statistical shape model. , 2008, Academic radiology.

[13]  Hyunjin Park,et al.  Construction of an abdominal probabilistic atlas and its application in segmentation , 2003, IEEE Transactions on Medical Imaging.

[14]  Yong Yin,et al.  Fully automated liver segmentation for low- and high- contrast CT volumes based on probabilistic atlases , 2010, 2010 IEEE International Conference on Image Processing.

[15]  Jerry L. Prince,et al.  Snakes, shapes, and gradient vector flow , 1998, IEEE Trans. Image Process..

[16]  W. Eric L. Grimson,et al.  Anatomical guided segmentation with non-stationary tissue class distributions in an expectation-maximization framework , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[17]  Barbara Freeman,et al.  PROBABILISTIC HUMAN BRAIN ATLAS FOR FUNCTIONAL IMAGING: COMPARISON TO SINGLE BRAIN ATLASES , 2007 .

[18]  Jerry L. Prince,et al.  Gradient vector flow: a new external force for snakes , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.