Dynamic-Threshold Level Set Method for Volumetry of Porcine Kidney in CT Images. In Vivo and Ex Vivo Assessment of the Accuracy of Volume Measurement

Academic Radiology

Volumn 14, Issue 7, 2007, Pages 890-896

  Cai, Wenli ^a   Holalkere, Nagaraj Setty ^a   Harris, Gordon ^a   Sahani, Dushyant ^a   Yoshida, Hiroyuki ^a  

^a MASSACHUSETTS GENERAL HOSPITAL   (United States)

Author keywords

Computerized volumetry; image segmentation; level set; renal volumetry; validation

Indexed keywords

IODINATED CONTRAST MEDIUM;

ACCURACY; ANIMAL TISSUE; ARTICLE; COMPUTED TOMOGRAPHY SCANNER; CONTROLLED STUDY; CORRELATION COEFFICIENT; FEMALE; IMAGE ANALYSIS; KIDNEY SIZE; MALE; MEASUREMENT; MULTIDETECTOR COMPUTED TOMOGRAPHY; NONHUMAN; PRIORITY JOURNAL; RADIOLOGIST; STANDARD; VOLUMETRY;

ANIMALS; CONTRAST MEDIA; FEMALE; IMAGING, THREE-DIMENSIONAL; IOPAMIDOL; KIDNEY; MALE; ORGAN SIZE; RADIOGRAPHIC IMAGE ENHANCEMENT; REFERENCE STANDARDS; REPRODUCIBILITY OF RESULTS; SWINE; TOMOGRAPHY, X-RAY COMPUTED;

EID: 34250203270     PISSN: 10766332     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.acra.2007.03.005     Document Type: Article

References (25)

1. Yonemura Y., Taketomi A., Soejima Y., et al. Validity of preoperative volumetric analysis of congestion volume in living donor liver transplantation using three-dimensional computed tomography. Liver Transpl 11 (2005) 1556-1562
2. Dempsey M.F., and Condon BR D.M.H. Measurement of tumor "size" in recurrent malignant glioma: 1D, 2D, or 3D?. AJNR Am J Neuroradiol 26 (2005) 770-776
3. Herts B. Role of three-dimensional imaging in surgical planning for kidney surgery. BJU Int 95 (2005) 16-20
4. Sise C., Kusaka M., Wetzel L.H., et al. Volumetric determination of progression in autosomal dominant polycystic kidney disease by computed tomography. Kidney Int 58 (2000) 2492-2501
5. Kim D., and Park J. Computer-aided detection of kidney tumor on abdominal computed tomography scans. Acta Radiol 45 (2004) 791-795
6. Ozono S., Miyao N., Igarashi T., et al. Tumor doubling time of renal cell carcinoma measured by CT: Collaboration of Japanese Society of Renal Cancer. Jpn J Clin Oncol 34 (2004) 82-85
7. Yoo T.S. Inight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis (2004), A K Peters, Ltd, Wellesey
8. Sonka M., and Fitzpatrick J.M. Medical Imaging Processing and Analysis (2004), SPIE-The International Society for Optical Engineering, Bellingham
9. Kass M., Witkin A., and Terzoopoulos D. Snakes: active contour models. Int J Comput Vision 1 (1988) 321-331
10. Kim S., Yoo S., Kim S., Kim J., and Park J. Segmentation of kidney without using contrast medium on abdominal CT image (2000) 1147-1152 In: 5th Int Conf Signal Processing. Beijing, China
11. Lin D., Lei C., and Hung S. Computer-aided kidney segmentation on abdominal CT images. IEEE Trans Inf Technol Biomed 10 (2006) 59-65
12. Pohle R., and Toennies K.D. Segmentation of medical images using adaptive region growing. Proc SPIE (2001) 1337-1346
13. Rao M., Stough J., Chi Y.Y., et al. Comparison of human and automatic segmentations of kidneys from CT images. Int J Radiat Oncol Biol Phys 61 (2005) 954-960
14. Tsagaan B., Shimizu A., Kobatake H., and Miyakawa K. An automated segmentation method of kidney using statistical information (2000) 556-563 In: Proc Medical Image Computing and Computer Assisted Intervention
15. Wang X., He L., and Wee W. Deformable contour method: A constrained optimization approach. Int J Comput Vision 59 (2004) 87-108
16. Kobashi M., and Shapiro L. Knowledge-based organ identification from CT images. Pattern Recogn 28 (1995) 475-491
17. Brown M., Feng W., Hall T., et al. Knowledge-based segmentation of pediatric kidneys in CT for measurement of parenchymal volume. J Comput Assist Tomogr 25 (2001) 639-648
18. Osher S., and Sethian J. Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations. J Comput Phys 79 (1988) 12-49
19. Sethian J. Level Set Methods: Evolving Interfaces in Geometry, Fluid Mechanics, Computer Vision and Material Sciences (1996), Cambridge University Press
20. Osher S., and Fedkiw R. Level Set Methods and Dynamic Implicit Surface (2002), Springer-Verlag, New York
21. Sean S., Bullitt E., and Gerig G. Level set evolution with region competition: automatic 3D segmentation of brain tumors (2002) 532-535 In: International Conference on Pattern Recognition
22. Moss A.A., Friedman M.A., and Brito A.C. Determination of liver, kidney, and spleen volumes by computed tomography: An experimental study in dogs. J Comput Assist Tomogr 5 (1981) 12-14
23. Brenner D., Whitley N., Houk T., et al. Volume determinations in computed tomography. JAMA 247 (1982) 1299-1302
24. Kim J. Animal study of renal volume measurement on abdominal CT using digital image processing: Preliminary report. Clin Imaging 28 (2004) 135-137
25. Cai W., Harris G., and Yoshida H. Computer-aided volumetrics of liver tumor in hepatic CT images (2006) 375-377 In: Computed Assisted Radiology and Surgery. Osaka