흉부 CT의 컴퓨터보조 진단

  • Published : 2005.10.01

Abstract

Keywords

References

  1. Doi K, MacMahon H, et al. Computer-aid-ed diagnosis in radiology : potential and pitfalls. Eur J radiol 1999:31:97-109
  2. Doi K. Current status and future potential of computer-aided diagnosis in medical imaging. Br J Radiol 2005:78:83-819
  3. Goo JM, Lee JW, et al. Automatic lung nodule detection at low-dose CT: prelimi nary expehence. Koran J radiol 2003:4: 211-216
  4. Hu S, Hoffman EA, Reinhardt JM. Auto-matic lung segmentation for accurate quan-titation of volumetric X-ray CT images. IEEE Trans Med Imaging. 2001 Jun;20 (6)-.490-8
  5. Boscolo R, Brown MS, McNitt-Gray MF. Medical Image Segmentation wlth Know ledgeguided Robust Active Contours. Radio-graphics. 2002:22:437-448
  6. Arimura H, Katsuragawa S, Suzuki K, Li F, Shiraishi J, Doi K. Computerized scheme for automated detection of lung nodules in low-dose CT images for lung cancer screening. Acad Radiol 2004:11: 617-29
  7. Takizawa H, Yamamoto S, Matsumoto T, et al. Recognition of lung nodules from X-ray CT images using 3D Markov random field models. Int Conf Pattern Rec 2002: 99-102
  8. Xu N, Ahuja N, Bansal R. Automated lung nodule segmentation using dynamic Pro-gramming and EM-based classification. Proc SPIE 2002:4684:666-676
  9. Giger ML, Bae KT, MacMahon H. Compu-terized detection of pulmonary nodules in CT images, Invest Radiol 1994:29:459-65
  10. Armato SG III, Li F, Giger ML, MacMahon H, Sone S, Doi K. Performance of auto-mated CT nodule detection on missed Can-cers from a lung cancer screening program.Radiology 2002:225:685-92
  11. Jiang H, Yamamoto S, lisaku S, et al. Computer-aided diagnosis system for lung cancer screening by CT. In: Doi K, et al editors. Computer-aided diagnosis in med-ical imaging. Amsterdam, The Neterlands: Elsevier, 1999:125-30
  12. Ukai Y, Niki N, Satoh H, et al. Computer aided diagnosis system for lung cancer based on retrospective helical CT image. Proc. SPIE 2000:3979:1028-39
  13. Armato III SG, Giger ML, Moran CJ, et al. Computerized detection of pulmonary nod-ules on CT scans. Radiographics 1999:19: 1303-11.Lawler LP, Wood SA, Paunu HS, Fishman EK. Computer-assisted detection of pulmonary nodules: preliminary Ob-servations using a prototype system with multi detector-row CT data sets. J DigitImaging 2003; 16:251-61
  14. Wormanns D, Fiebich M, Saidi M, et al. Automatic detection of pulmonary nodules at spiral CT: clinical application of a Com-Puter-aided diagnosis system. Eur Radiol 2002; 12:1052-7
  15. Gurcan MN, Sahiner B, Petrick N, et al. Lung nodule detection on thoracic Com-puted tomography images: Preliminary evaluation of a computer-aided diagnosis system. Med Phys 2002;29:2552-8
  16. Brown MS, Goldin JG, Suh RD, et al. Lung micronodules: automated method for detection at thin-section CT-initial experi-ence. Radiology 2003:226:256-62
  17. Suzuki K, Armato III SG, Li F, et al. Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose CT. Med Phys 2003:30:1602-17
  18. Okumura T, Miwa, T, Kako J, YamamotoS, Matsumoto M, Tateno Y, et al. Variable N-Quoit filter applied for automatic de-tection of lung cancer by X-ray CT. In: Lemke HU, Vannier MW, Inamura K, Farman A, editors. Computer-assisted radiology. Amsterdam, The Netherlands: Elsevier Science, 1998:242-7
  19. Lee Y, Hara T, Fujita H, Itoh S, Ishigaki T. Automated detection of pulmonary nod-ules in helical CT images based on an im-proved template-matching technique. IEEE Trans Med Imaging 2001:20:595-604
  20. Kanazawa K, Kawata Y, Niki N, Satoh H, Ohmatsu H, Kakinuma R, et al. Computer aided diagnostic system for pulmonary nodules based on helical CT images. In: Doi K, MacMahon H, Giger ML, Hoffmann KR, editors. Computer-aided diagnosis in medical imaging. Amsterdam, The Nether-lands: Elsevier Science, 1999:131-6
  21. Brown MS, McNitt-Gray MF, Goldin JG, Suh RD, Sayre JW, Aberle DR. Patient-specific models for lung nodule de-tection and surveillance in CT images. IEEE Trans Med Imaging 2001:20:1242-50
  22. Armato SG III, Giger ML, MacMahon H. Automated detection of lung nodules in CT scans: preliminary results. Med Phys 2001-,28:1552-61
  23. Lee JW, Goo JM, Lee HJ, Kim JH, Kim S, Kim YT. The potential contribution of a computer-aided detection system for lung nodule detection in multi-detector row computed tomography. Invest Radiol 2004:39:649-55
  24. Lee IJ, Gamsu G, Czum J. Wu N, Johnson R, Chakrapani S. Lung Nodule Detection on Chest CT: Evaluation of a Computer Aided Detection (CAD) System. Korean J Radiol. 2005 Apr-Jun;6(2):89-93
  25. Brown MS, Goldin JG, Rogers S, Kim HJ, et al. Computer-aided lung nodule de-tection in CT: results of large-scale Ob-server test. Acad Radiol. 2005 Jun;12(6): 681-6
  26. Henschke CI, Yankelevitz DF, Mirtcheva R, et al. CT screening for lung cancer: fre-quency and signiflcance of part-solid and nonsolid nodules. AJR Am J Roentgenol. 2002 May; 178(5): 1053-7
  27. Li F, Sone S, Abe H, MacMahon H, Armato SG 3rd, Doi K. Lung cancers missed at low-dose helical CT screening in a general population: comparison of Clin-ical, histopathologic, and imaging findings. Radiology. 2002:225(3):673-83
  28. Kim KG, Goo JM, Kim JH, Lee HJ, et al. Computer-aided detection of localized ground-glass opacity in the lung on CT images. Radiology in press
  29. Kaneko M, Eguchi K, Ohmatsu H, et al. Peripheral lung cancer: screening and de-tection with low-dose spiral CT versus radiography. Radiology 1996:201:798-802
  30. Sone S, Takashima S, Li F, et al. Mass screening for lung cancer with mobile Spi-ral computed tomography scanner. Lancet 998:351:1242-5
  31. Henschke CI, McCauley DI, Yankelevitz DF, et al. Early lung cancer action proj ect: overall design and findings from base-line screening. Lancet 1999:354:99-105
  32. Diederich S, Wormanns D, Semik M, Thomas M, Lenzen H, Roos N, et al. Screening for early lung cancer wlth low-dose spiral CT: prevalence in 817 asymptomatic smokers. Radiology 2002; 222:773-81
  33. Swensen SJ, Jett JR, Sloan JA, Midthun DE, Hartman TE, Sykes AM, et al. Screening for lung cancer with low-dose spiral computed tomography. Am J Respir Crit Care Med 2002:165:508-13
  34. McNitt-Gray MF, Hart EM, Wyckoff N, Sayre JW, Goldin JG, Aberle DR. A pat-tern classiflcation approach to characterizing solitary pulmonary nodules imaged on high resolution CT: preliminary results. Med Phys. 1999 Jun: 26(6): 880-8
  35. Kido S, Kuriyama K, Higashiyama M, Kasugai T, Kuroda C. Fractal analysis of internal and peripheral textures of small peripheral bronchoeeruc carcinomas in thin-section computed tomography: Com-parison of bronchioloalveolar cell Carcino-mas with nonbronchioloalveolar cell Carcinomas. J Comput Assist Tomogr. 2003 Jan-Feb;27(1):56-61
  36. Kawata Y, Niki N, Ohmatsu H, Moriyama N. Example-based assisting approach for pulmonary nodule classification in three-dimensional thoracic computed tomography images. Acad Radiol. 2003:10(12): 1402-15
  37. Aoyama M, Li Q, Katsuragawa S, Li F, Sone S, Doi K. Computerized scheme for determi-nation of the likelihood measure of malig-nancy for pulmonary nodules on low-dose CT images. Med Phys 2003:30: 387-94
  38. Li F, Aoyama M, Shiraishi J, Abe H, Li Q, Suzuki K, et al. Improvement in radiol ogists' performance for differentiating small benign from malignant lung nodules on high-resolution CT by using computer estimated likelihood of malignancy. AJR 2004:183:1209-15
  39. Zhao B, Yankelevitz D, Reeves A, Henschke C. Two-dimensional multi-crite-rion segmentation of pulmonary nodules on helical CT-images. Med Phys 1999:26: 889-95
  40. Wormanns D, Kohl G, Klotz E, Heindel W, Diederich S. Clinical evaluation of the re-producibility of volume measurements of pulmonary nodules. In: Proceedings of SPIE Medical Imaging 2002. SPIE 2002; 4684:316-22
  41. Fan L, Qian J, Odry B, Shen H, Naidich D, Kohl G, et al. Automatic segmentation of pulmonary nodules by using dynamic 3D cross-correlation for interactive CAD systems. In: Proceedings of SPIE Medical Imaeing 2002. SPIE 2002:4684: 1362-9
  42. Wiemker R, Rogalla P, Hein E, Blaffert TRosch P. Computer aided segmentation of pulmonary nodules: automated vascula ture cutoff in thick- and thinslice CT. In: Proceedings of Computer Assisted Radio-logy and Surgery, CARS 2003. Amster-dam, The Netherlands: Elsevier, 2003: 965-790
  43. Kostis W, Reeves A, Yankelewitz D, Henschke C. Three-dimensional Segmenta-tion of solitary pulmonary nodules from helical CT scans. In: Proceedings of Computer Assisted Radiology and Surgery CARS 1999. Amsterdam, The Netherlands: Elsevier, 1999:203-7
  44. Wiemker R, Zwartkruis A. Optimal thresholding for 3D segmentation of Pul-monary nodules in high resolution CT. In: Proceedings of International Conference on Computer Assisted Radiology and Surgery CARS 2001. Amsterdam, The Netherlands: Elsevier Publishers, 2001:653-8
  45. Goo JM, Tongdee T, Tongdee R, Yeo K, Hildebolt CF, Bae KT. Volumetric measurement of synthetic lung nodules wlthmulti-detector row CT: effect of variousimage reconstruction parameters and Seg-mentation thresholds on measurement ac-curacy. Radiology. 2005 Jun; 235(3): 850-6
  46. Yankelevitz DF, Reeves AP, Kostis WJ, Zhao B, Henschke CI. Small pulmonary nodules: volumetrically determined growth rates based on CT evaluation. Radiology. 2000 Oct:217(1):251-6
  47. Wormanns D, Kohl G, Klotz E, Marheme A, Beyer F, Heindel W, Diederich S. Volumetric measurements of pulmonary nodules at multi-row detector CT: in vivo reprodudbility. Eur Radiol. 2004 Jan;14 (l):86-92. Epub 2003 Nov 13