An Automatic Method for Renal Cortex Segmentation on CT Images. Evaluation on Kidney Donors

Academic Radiology

Volumn 19, Issue 5, 2012, Pages 562-570

  Chen, Xinjian ^a   Summers, Ronald M ^a   Cho, Monique ^b   Bagci, Ulas ^a   Yao, Jianhua ^a  

^a NATIONAL INSTITUTES OF HEALTH   (United States)

^b NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES   (United States)

Author keywords

Automatic renal cortex segmentation; Kidney; Kidney donors; Renal cortex

Indexed keywords

ABDOMINAL RADIOGRAPHY; ADULT; ARTICLE; AUTOMATION; CLINICAL ARTICLE; CONTRAST ENHANCEMENT; FEMALE; HUMAN; IMAGE ANALYSIS; KIDNEY CORTEX; KIDNEY DONOR; KIDNEY MASS; MALE; NEPHRECTOMY; PRIORITY JOURNAL; RETROSPECTIVE STUDY; THREE DIMENSIONAL IMAGING; VALIDATION STUDY;

ADULT; ALGORITHMS; FEMALE; HUMANS; IMAGING, THREE-DIMENSIONAL; KIDNEY; KIDNEY TRANSPLANTATION; LIVING DONORS; MALE; MIDDLE AGED; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84862792489     PISSN: 10766332     EISSN: 18784046     Source Type: Journal    
DOI: 10.1016/j.acra.2012.01.005     Document Type: Article

References (28)

1. Lee V.S., Rusinek H., Noz M.E., et al. Dynamic three-dimensional MR renography for the measurement of single kidney function: initial experience. Radiology 2003, 227:289-294.
2. Van den Dool S.W., Wasser M.N., de Fijter J.W., et al. Functional renal volume: quantitative analysis at gadolinium-enhanced MR angiography-feasibility study in healthy potential kidney donors. Radiology 2005, 236:189-195.
3. Holden A., Smith A., Dukes P., et al. Assessment of 100 live potential renal donors for laparoscopic nephrectomy with multi-detector row helical CT. Radiology 2005, 237:973-980.
4. Halpern E.J., Mitchell D.G., Wechsler R.J., et al. Preoperative evaluation of living renal donors: comparison of CT angiography and MR angiography. Radiology 2000, 216:434-439.
5. Sahani D.V., Rastogi N., Greenfield A.C., et al. Multi-detector row CT in evaluation of 94 living renal donors by readers with varied experience. Radiology 2005, 235:905-910.
6. Low R.N., Martinez A.G., Steinberg S.M., et al. Potential renal transplant donors: evaluation with gadolinium-enhanced MR angiography and MR urography. Radiology 1998, 207:165-172.
7. Shen W., Kassim A.A., Koh H.K., et al. Segmentation of kidney cortex in MRI studies: a constrained morphological 3D h-maxima transform approach. Int J Med Eng Inform 2009, 1:330-341.
8. Chevaillier B, Ponvianne Y, Collette JL, et al. Functional semi-automated segmentation of renal DCE-MRI sequences. In: Proceedings of the International Conference on Acoustics, Speech, and Signal Processing; 2008:525-528.
9. Lin D.T., Lei D.T., Hung S.W. Computer-aided kidney segmentation on abdominal CT images. IEEE Trans Inform Technol Biomed 2006, 10:59-65.
10. Li X., Chen X., Yao J., et al. Renal cortex segmentation using optimal surface search with novel graph construction. Lecture Notes Comput Sci 2011, 6893:387-394.
11. de Priester J.A., Kessels A.G., Giele E.L., et al. MR renography by semiautomated image analysis: performance in renal transplant recipients. J Magn Reson Imaging 2001, 14:134-140.
12. Shim H., Chang S., Tao C., et al. Semiautomated segmentation of kidney from high-resolution multidetector computed tomography images using a graph-cuts technique. J Comput Assist Tomogr 2009, 33:893-901.
13. Ali AM, Farag AA, El-Baz AS. Graph cuts framework for kidney segmentation with prior shape constraints. In: Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention; 2007:384-392.
14. Tang Y., Jackson H.A., De Filippo R.E., et al. Automatic renal segmentation applied in pediatric MR urography. Int J Intel Inform Process 2010, 1:12-19.
15. Hugen C.M., Polcari A.J., Farooq A.V., et al. Size does matter: donor renal volume predicts recipient function following live donor renal transplantation. J Urol 2011, 185:605-609.
16. Jeon H.G., Lee S.R., Joo D.J., et al. Predictors of kidney volume change and delayed kidney function recovery after donor nephrectomy. J Urol 2010, 184:1057-1063.
17. Boykov Y., Kolmogorov V. An experimental comparison of min-cut/max-flow algorithms. IEEE Trans Patt Anal Machine Intel 2004, 26:1124-1137.
18. Kolmogorov V., Zabih R. What energy functions can be minimized via graph cuts?. IEEE Trans Patt Anal Machine Intel 2004, 26:147-159.
19. Cootes T., Edwards G., Taylor C. Active appearance models. IEEE Trans Patt Anal Machine Intel 2001, 23:681-685.
20. Mitchell S.C., Lelieveldt B.P.F., van der Geest R.J., et al. Multistage hybrid active appearance model matching: segmentation of left and right ventricles in cardiac MR images. IEEE Trans Med Imaging 2001, 20:415-423.
21. Chen X, Yao J, Zhuge Y, et al. 3D automatic image segmentation based on Graph Cut-Oriented Active Appearance Models. ICIP; 2010. p. 3653-3656.
22. Falcao A.X., Udupa J.K., Samarasekera S., et al. User-steered image segmentation paradigms: live wire and live lane. Graph Models Image Process 1998, 60:233-260.
23. Chen X., Udupa J.K., Alavi A., et al. Automatic anatomy recognition via multi-object oriented active shape models. Med Phys 2010, 37:6390-6401.
24. Ciesielski K.C., Chen X., Udupa J.K., et al. Linear time algorithms for exact distance transform. J Math Imaging Vision 2011, 39:193-209.
25. Udupa J.K., Leblanc V.R., Zhuge Y., et al. A framework for evaluating image segmentation algorithms. Comput Medl Imaging Graphics 2006, 30:75-87.
26. Cox D.R., Hinkley D.V. Theoretical Statistics 1974, Chapman & Hall, New York.
27. Bland J.M., Altman D.G. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986, 1:307-310.
28. Bland J.M., Altman D.G. Measuring agreement in method comparison studies. Stat Methods Med Res 1999, 8:135-160.