Abstract
Purpose
We propose an automated pancreas segmentation algorithm from contrast-enhanced multiphase computed tomography (CT) and verify its effectiveness in segmentation.
Methods
The algorithm is characterized by three unique ideas. First, a two-stage segmentation strategy with spatial standardization of pancreas was employed to reduce variations in the pancreas shape and location. Second, patient- specific probabilistic atlas guided segmentation was developed to cope with the remaining variability in shape and location. Finally, a classifier ensemble was incorporated to refine the rough segmentation results.
Results
The effectiveness of the proposed algorithm was validated with 20 unknown CT volumes, as well as three on-site CT volumes distributed in a competition of pancreas segmentation algorithms. The experimental results indicated that the segmentation performance was enhanced by the proposed algorithm, and the Jaccard index between an extracted pancreas and a true one was 57.9%.
Conclusions
This study verified the effectiveness of two-stage segmentation with spatial standardization of pancreas in delineating the pancreas region, patient-specific probabilistic atlas guided segmentation in reducing false negatives, and a classifier ensemble in boosting segmentation performance.
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References
Nakaguchi T, Okui M, Tsumura N et al (2004) Pancreas extraction using a deformable model on abdominal CT image. International Workshop on Nonlinear Circuits and Signal Processing, pp 387–390
Kitasaka T, Sakashita M, Mori K et al (2008) A method for extracting pancreas regions from four-phase contrasted 3D abdominal CT images. Int J Comput Assist Radiol Surg 3(Suppl 1): S40
Shimizu A, Ohno R, Ikegami T et al (2007) Segmentation of multiple organs in non-contrast 3D abdominal CT images. Int J Comput Assist Radiol Surgery 2(3–4): 135–142
Shimizu A, Kubo M, Furukawa D et al (2008) Abdomen standardization for multi-organ segmentation of CT volumes. Int J Comput Assist Radiol Surg 3(Suppl 1): s195–s196
Shimizu A, Nawano S, Shinozaki K et al (2009) Medical imaging competitions in Japan. In: Proceedings of world congress on medical physics and biomedical engineering (to appear)
Shimizu A, Kimoto T, Furukawa D et al (2008) Pancreas segmentation in three-phase abdominal CT volume data. Int J Comput Assist Radiol Surg 3(Suppl 1): s393–s394
Shimizu A, Kimoto T, Kobatake H et al (2009) Patient-specific atlas-guided pancreas segmentation from three-dimensional contrast-enhanced computed tomography. Int J Comput Assist Radiol Surg Suppl (to appear)
Ericsson A, Aljabar P, Rueckert D (2008) Construction of a patient-specific atlas of the brain: application to normal aging. In: IEEE International Symposium on Biomedical Imaging, pp 480–483
Shimizu A, Narihira T, Furukawa D et al (2008) Ensemble segmentation using AdaBoost with application to liver lesion extraction from a CT volume. In: Proceedings of workshop in MICCAI2008. http://grand-challenge2008.bigr.nl/proceedings/
Niessen W, Walsum T, Schaap M et al (2008) 3D segmentation in the clinic: a grand challenge II. In: Proceedings of workshop in MICCAI2008. http://grand-challenge2008.bigr.nl/
Maintz J, Viergever M (1998) A survey of medical image registration. Med Image Anal 2: 1–36
Fornefett M, Rohr K, Sprengel R et al (1998) Elastic medical image registration using orientation attributes at landmarks. In: Proceedings of the medical image understanding and analysis, pp 49–52
Feldmar J, Declerck J, Malandain G et al (1996) Extension of the ICP algorithm to nonrigid intensity-based registration of 3D volumes. Comput Vis Image Underst 66(2): 193–206
Cootes T, Taylor C, Cooper D et al (1995) Active shape models—their training and application. Comput Vis Image Underst 61: 38–59
Yang J, Staib L, Duncan J (2004) Neighbor-constrained segmentation with level set based 3-D deformable models. IEEE Trans Med Imaging 23(8): 940–948
Saito T, Toriwaki J (1994) New algorithms for euclidean distance transformation of an n-dimensional digitized picture with applications. Pattern Recognit 27(11): 1551–1565
Murata N, Takenouchi T, Kanamori T et al (2004) Information geometry of U-Boost and Bregman divergence. Neural Comput 16(7): 1437–1481
Narihira T, Shimizu A, Furukawa D et al (2009) Boosting algorithms for segmentation of metastatic liver tumors in contrast-enhanced computed tomography. Int J Comput Assist Radiol Surg (accepted)
Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7): 629–639
Ginneken B, Heimann T, Styner M (2007) 3D segmentation in the clinic: a grand challenge MICCAI workshop on 3D segmentation in the clinic. http://mbi.dkfz-heidelberg.de/grand-challenge2007/web/p7.pdf
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Shimizu, A., Kimoto, T., Kobatake, H. et al. Automated pancreas segmentation from three-dimensional contrast-enhanced computed tomography. Int J CARS 5, 85–98 (2010). https://doi.org/10.1007/s11548-009-0384-0
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DOI: https://doi.org/10.1007/s11548-009-0384-0