Automated pancreas segmentation from three-dimensional contrast-enhanced computed tomography

详细信息    查看全文

• 作者：Akinobu Shimizu (1)
  Tatsuya Kimoto (1)
  Hidefumi Kobatake (1)
  Shigeru Nawano (2)
  Kenji Shinozaki (3)
  
• 关键词：Pancreas segmentation ; Spatial standardization ; Patient ; specific probabilistic atlas ; Statistical model ; Classifier ensemble ; 3D ; CT volume
• 刊名：International Journal of Computer Assisted Radiology and Surgery
• 出版年：2010
• 出版时间：January 2010
• 年：2010
• 卷：5
• 期：1
• 页码：85-98
• 全文大小：1446KB
• 参考文献：1. Nakaguchi T, Okui M, Tsumura N et聽al (2004) Pancreas extraction using a deformable model on abdominal CT image. International Workshop on Nonlinear Circuits and Signal Processing, pp 387鈥?90
  2. Kitasaka T, Sakashita M, Mori K et聽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
  3. Shimizu A, Ohno R, Ikegami T et聽al (2007) Segmentation of multiple organs in non-contrast 3D abdominal CT images. Int J Comput Assist Radiol Surgery 2(3鈥?): 135鈥?42 CrossRef
  4. Shimizu A, Kubo M, Furukawa D et聽al (2008) Abdomen standardization for multi-organ segmentation of CT volumes. Int J Comput Assist Radiol Surg 3(Suppl 1): s195鈥搒196
  5. Shimizu A, Nawano S, Shinozaki K et聽al (2009) Medical imaging competitions in Japan. In: Proceedings of world congress on medical physics and biomedical engineering (to appear)
  6. Shimizu A, Kimoto T, Furukawa D et聽al (2008) Pancreas segmentation in three-phase abdominal CT volume data. Int J Comput Assist Radiol Surg 3(Suppl 1): s393鈥搒394
  7. Shimizu A, Kimoto T, Kobatake H et聽al (2009) Patient-specific atlas-guided pancreas segmentation from three-dimensional contrast-enhanced computed tomography. Int J Comput Assist Radiol Surg Suppl (to appear)
  8. 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鈥?83
  9. Shimizu A, Narihira T, Furukawa D et聽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/
  10. Niessen W, Walsum T, Schaap M et聽al (2008) 3D segmentation in the clinic: a grand challenge II. In: Proceedings of workshop in MICCAI2008. http://grand-challenge2008.bigr.nl/
  11. Maintz J, Viergever M (1998) A survey of medical image registration. Med Image Anal 2: 1鈥?6 CrossRef
  12. Fornefett M, Rohr K, Sprengel R et聽al (1998) Elastic medical image registration using orientation attributes at landmarks. In: Proceedings of the medical image understanding and analysis, pp 49鈥?2
  13. Feldmar J, Declerck J, Malandain G et聽al (1996) Extension of the ICP algorithm to nonrigid intensity-based registration of 3D volumes. Comput Vis Image Underst 66(2): 193鈥?06 CrossRef
  14. Cootes T, Taylor C, Cooper D et聽al (1995) Active shape models鈥攖heir training and application. Comput Vis Image Underst 61: 38鈥?9 CrossRef
  15. 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鈥?48 CrossRef
  16. Saito T, Toriwaki J (1994) New algorithms for euclidean distance transformation of an n-dimensional digitized picture with applications. Pattern Recognit 27(11): 1551鈥?565 CrossRef
  17. Murata N, Takenouchi T, Kanamori T et聽al (2004) Information geometry of U-Boost and Bregman divergence. Neural Comput 16(7): 1437鈥?481 CrossRef
  18. Narihira T, Shimizu A, Furukawa D et聽al (2009) Boosting algorithms for segmentation of metastatic liver tumors in contrast-enhanced computed tomography. Int J Comput Assist Radiol Surg (accepted)
  19. Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7): 629鈥?39 CrossRef
  20. 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
  
• 作者单位：Akinobu Shimizu (1)
  Tatsuya Kimoto (1)
  Hidefumi Kobatake (1)
  Shigeru Nawano (2)
  Kenji Shinozaki (3)
  
  1. Tokyo University of Agriculture and Technology, Naka-cho 2-24-16, Koganei, Tokyo, 184-8588, Japan
  2. Center for Radiological Sciences, International University of Health and Welfare, Mita 1-4-3, Minato-ku, Tokyo, 108-8329, Japan
  3. National Kyusyu Cancer Center, Notame 3-1-1, Minami-ku, Fukuoka-shi, Fukuoka, 811-1359, Japan
  

文摘

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.