Abstract
Purpose
Propose a fully automatic 3D segmentation framework to segment liver on challenging cases that contain the low contrast of adjacent organs and the presence of pathologies from abdominal CT images.
Methods
First, all of the atlases are weighted in the selected training datasets by calculating the similarities between the atlases and the test image to dynamically generate a subject-specific probabilistic atlas for the test image. The most likely liver region of the test image is further determined based on the generated atlas. A rough segmentation is obtained by a maximum a posteriori classification of probability map, and the final liver segmentation is produced by a shape–intensity prior level set in the most likely liver region. Our method is evaluated and demonstrated on 25 test CT datasets from our partner site, and its results are compared with two state-of-the-art liver segmentation methods. Moreover, our performance results on 10 MICCAI test datasets are submitted to the organizers for comparison with the other automatic algorithms.
Results
Using the 25 test CT datasets, average symmetric surface distance is \(1.09 \pm 0.34\) mm (range 0.62–2.12 mm), root mean square symmetric surface distance error is \(1.72 \pm 0.46\) mm (range 0.97–3.01 mm), and maximum symmetric surface distance error is \(18.04 \pm 3.51\) mm (range 12.73–26.67 mm) by our method. Our method on 10 MICCAI test data sets ranks 10th in all the 47 automatic algorithms on the site as of July 2015. Quantitative results, as well as qualitative comparisons of segmentations, indicate that our method is a promising tool to improve the efficiency of both techniques.
Conclusion
The applicability of the proposed method to some challenging clinical problems and the segmentation of the liver are demonstrated with good results on both quantitative and qualitative experimentations. This study suggests that the proposed framework can be good enough to replace the time-consuming and tedious slice-by-slice manual segmentation approach.
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References
Meinzer HP, Thorn M, Crdenas CE (2002) Computerized planning of liver surgery—an overview. Comput Graph 26(4):569–576
Masumoto J, Hori M, Sato Y, Murakami T, Johkoh T, Nakamura H, Tamura S (2003) Automated liver segmentation using multislice CT images. Syst Comput 34(9):71–82
Shiffman S, Rubin GD, Napel S (2000) Medical image segmentation using analysis of isolable-contour maps. IEEE Trans Med Imaging 19(11):1064–1074
Bae KT, Giger ML, Chen CT, Kahn CE Jr (1993) Automatic segmentation of liver structure in CT images. Med Phys 20(1):71–78
Ruskó L, Bekes G, Fidrich M (2009) Automatic segmentation of the liver from multi- and single-phase contrast-enhanced CT images. Med Image Anal 13(6):871–882
Selver MA, Kocaoǧlu A, Demir GK, Doanǧ H, Dicle O, Güzeliş C (2008) Patient oriented and robust automatic liver segmentation for pre-evaluation of liver transplantation. Comput Biol Med 38(7):765–784
Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277
Okada T, Shimada R, Hori M, Nakamoto M, Chen Y-W, Nakamura H, Sato Y (2008) Automated segmentation of the liver from 3D CT images using probabilistic atlas and multilevel statistical shape model. Acad Radiol 15(11):1390–1403
So R, Chung A (2009) Multi-level non-rigid image registration using graph-cuts. In: IEEE international conference on acoustics, speech and signal processing, 2009. ICASSP 2009. IEEE, pp 397–400
Wimmer A, Soza G, Hornegger J (2009) A generic probabilistic active shape model for organ segmentation. In: MICCAI. Springer, pp 26–33
Chu C, Oda M, Kitasaka T, Misawa K, Fujiwara M, Hayashi Y, Wolz R, Rueckert D, Mori K (2013) Multi-organ segmentation from 3D abdominal CT images using patient-specific weighted-probabilistic atlas. In: SPIE medical imaging. International Society for Optics and Photonics, pp 86693–86697
Linguraru MG, Sandberg JK, Li Z, Pura JA, Summers RM (2009) Atlas-based automated segmentation of spleen and liver using adaptive enhancement estimation. In: MICCAI. Springer, pp 1001–1008
Cootes TF, Taylor CJ, Cooper DH, Graham J (1995) Active shape models-their training and application. Comput Vis Image Underst 61(1):38–59
Park H, Bland PH, Meyer CR (2003) Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE Trans Med Imaging 22(4):483–492
Okada T, Yokota K, Hori M, Nakamoto M, Nakamura H, Sato Y (2008) Construction of hierarchical multi-organ statistical atlases and their application to multi-organ segmentation from CT images. In: MICCAI. Springer, pp 502–509
Oda M, Nakaoka T, Kitasaka T, Furukawa K, Misawa K, Fujiwara M, Mori K (2012) Organ segmentation from 3D abdominal CT images based on atlas selection and graph cut. In: Abdominal Imaging. Computational and clinical applications. Springer, pp 181–188
Shimizu A, Ohno R, Ikegami T, Kobatake H, Nawano S, Smutek D (2007) Segmentation of multiple organs in non-contrast 3D abdominal CT images. Int J Comput Assist Radiol Surg 2(3–4):135–142
Wolz R, Chu C, Misawa K, Mori K, Rueckert D (2012) Multi-organ abdominal CT segmentation using hierarchically weighted subject-specific atlases. In: MICCAI. Springer, pp 10–17
Oliveira DA, Feitosa RQ, Correia MM (2011) Segmentation of liver, its vessels and lesions from CT images for surgical planning. Biomed Eng Online 10(1):30
Yang J, Duncan JS (2004) 3D image segmentation of deformable objects with joint shape–intensity prior models using level sets. Med Image Anal 8(3):285–294
Li BN, Chui CK, Chang S, Ong SH (2011) Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation. Comput in Biol Med 41(1):1–10
Linguraru MG, Richbourg WJ, Watt JM, Pamulapati V, Summers RM (2012) Liver and tumor segmentation and analysis from CT of diseased patients via a generic affine invariant shape parameterization and graph cuts. In: Abdominal imaging. Computational and clinical applications. Springer, pp 198–206
Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639
Van Ginneken B, Heimann T, Styner M (2007) 3D segmentation in the clinic: a grand challenge. 3D segmentation in the clinic: a grand challenge, pp 7–15
Chi Y, Zhou J, Venkatesh SK, Huang S, Tian Q, Hennedige T, Liu J (2013) Computer-aided focal liver lesion detection. Int J Comput Assist Radiol Surg 8(4):511–525
Heimann T, Van Ginneken B, Styner M et al (2009) Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans Med Imaging 28(8):1251–1265
Zhou X, Kitagawa T, Hara T, Fujita H, Zhang X, Yokoyama R, Kondo H, Kanematsu M, Hoshi H (2006) Constructing a probabilistic model for automated liver region segmentation using non-contrast X-ray torso CT images. In: MICCAI. Springer, pp 856–863
Li C, Wang X, Li J, Eberl S, Fulham M, Yin Y, Feng DD (2013) Joint probabilistic model of shape and intensity for multiple abdominal organ segmentation from volumetric CT images. IEEE Trans Inf Technol Biomed 17(1):92–102
Acknowledgments
The authors would like to thank the anonymous reviewers for their valuable comments and help suggestions that greatly improved the papers quality. This work was supported in part by the National Natural Science Foundation of China under Grant No. 61571158; the Scientific Research Fund of Heilongjiang Provincial Education Department (No. 12541164), and the Nature Science Foundation of Heilongjiang Province of China (No. F2015005).
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Wang, J., Cheng, Y., Guo, C. et al. Shape–intensity prior level set combining probabilistic atlas and probability map constrains for automatic liver segmentation from abdominal CT images. Int J CARS 11, 817–826 (2016). https://doi.org/10.1007/s11548-015-1332-9
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DOI: https://doi.org/10.1007/s11548-015-1332-9