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Recent Advances in Reduction of False Positives in Computerized Detection of Polyps in CT Colonography

  • Conference paper
Virtual Colonoscopy and Abdominal Imaging. Computational Challenges and Clinical Opportunities (ABD-MICCAI 2010)

Abstract

One of the major challenges in computer-aided detection (CADe) of polyps in CT colonography (CTC) is the reduction of false-positive detections (FPs) without a concomitant reduction in sensitivity. Major sources of FPs generated by CADe schemes include haustral folds, residual stool, rectal tubes, the ileocecal valve, and extra-colonic structures such as the small bowel and stomach. A large number of FPs is likely to confound the radiologist’s task of image interpretation, lower the radiologist’s efficiency, and cause radiologists to lose their confidence in CADe as a useful tool. Therefore, it is important to reduce the number of FPs as much as possible while maintaining a high sensitivity. In this paper, FP reduction techniques used in CADe schemes for detection of polyps in CTC are reviewed.

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References

  1. Jemal, A., Murray, T., Ward, E., Samuels, A., Tiwari, R.C., Ghafoor, A., Feuer, E.J., Thun, M.J.: Cancer statistics. CA. Cancer J. Clin. 30, 10–30 (2005)

    Article  Google Scholar 

  2. Winawer, S.J., Fletcher, R.H., Miller, L., Godlee, F., Stolar, M.H., Mulrow, C.D., Woolf, S.H., Glick, S.N., Ganiats, T.G., Bond, J.H., Rosen, L., Zapka, J.G., Olsen, S.J., Giardiello, F.M., Sisk, J.E., Van Antwerp, R., Brown-Davis, C., Marciniak, D.A., Mayer, R.J.: Colorectal cancer screening: clinical guidelines and rationale. Gastroenterology 112, 594–642 (1997)

    Article  Google Scholar 

  3. Dachman, A.H.: Atlas of Virtual Colonoscopy. Springer, New York (2003)

    Book  Google Scholar 

  4. Macari, M., Bini, E.J.: CT colonography: where have we been and where are we going? Radiology 237, 819–833 (2005)

    Article  Google Scholar 

  5. Fletcher, J.G., Booya, F., Johnson, C.D., Ahlquist, D.: CT colonography: unraveling the twists and turns. Curr. Opin. Gastroenterol 21, 90–98 (2005)

    Google Scholar 

  6. Yoshida, H., Dachman, A.H.: Computer-aided diagnosis for CT colonography. Semin Ultrasound CT MR 25, 419–431 (2004)

    Article  Google Scholar 

  7. Yoshida, H., Dachman, A.H.: CAD techniques, challenges, and controversies in computed tomographic colonography. Abdom Imaging 30, 26–41 (2005)

    Article  Google Scholar 

  8. Yoshida, H., Masutani, Y., MacEneaney, P., Rubin, D.T., Dachman, A.H.: Computerized detection of colonic polyps at CT colonography on the basis of volumetric features: pilot study. Radiology 222, 327–336 (2002)

    Article  Google Scholar 

  9. Yoshida, H., Näppi, J., MacEneaney, P., Rubin, D.T., Dachman, A.H.: Computer-aided diagnosis scheme for detection of polyps at CT colonography. Radiographics 22, 963–979 (2002)

    Article  Google Scholar 

  10. Yoshida, H., Näppi, J.: Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps. IEEE Trans. Med. Imaging 20, 1261–1274 (2001)

    Article  Google Scholar 

  11. Summers, R.M., Johnson, C.D., Pusanik, L.M., Malley, J.D., Youssef, A.M., Reed, J.E.: Automated polyp detection at CT colonography: feasibility assessment in a human population. Radiology 219, 51–59 (2001)

    Article  Google Scholar 

  12. Paik, D.S., Beaulieu, C.F., Rubin, G.D., Acar, B., Jeffrey, R.B., Yee Jr, J., Dey, J., Napel, S.: Surface normal overlap: a computer-aided detection algorithm with application to colonic polyps and lung nodules in helical CT. IEEE Trans. Med. Imaging 23, 661–675 (2004)

    Article  Google Scholar 

  13. Kiss, G., Van Cleynenbreugel, J., Thomeer, M., Suetens, P., Marchal, G.: Computer-aided diagnosis in virtual colonography via combination of surface normal and sphere fitting methods. Eur. Radiol. 12, 77–81 (2002)

    Article  Google Scholar 

  14. Summers, R.M., Yao, J., Pickhardt, P.J., Franaszek, M., Bitter, I., Brickman, D., Krishna, V., Choi, J.R.: Computed tomographic virtual colonoscopy computer-aided polyp detection in a screening population. Gastroenterology 129, 1832–1844 (2005)

    Article  Google Scholar 

  15. Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, San Diego (1990)

    MATH  Google Scholar 

  16. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986)

    Article  MATH  Google Scholar 

  17. Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, Berlin (1995)

    Book  MATH  Google Scholar 

  18. Nappi, J., Yoshida, H.: Automated detection of polyps with CT colonography: evaluation of volumetric features for reduction of false-positive findings. Acad. Radiol. 9, 386–397 (2002)

    Article  Google Scholar 

  19. Gokturk, S.B., Tomasi, C., Acar, B., Beaulieu, C.F., Paik, D.S., Jeffrey, R.B., Yee Jr, J., Napel, S.: A statistical 3-D pattern processing method for computer-aided detection of polyps in CT colonography. IEEE Transactions on Medical Imaging 20, 1251–1260 (2001)

    Article  Google Scholar 

  20. Acar, B., Beaulieu, C.F., Gokturk, S.B., Tomasi, C., Paik, D.S., Jeffrey, R.B., Yee Jr, J., Napel, S.: Edge displacement field-based classification for improved detection of polyps in CT colonography. IEEE Transactions on Medical Imaging 21, 1461–1467 (2002)

    Article  Google Scholar 

  21. Jerebko, A.K., Summers, R.M., Malley, J.D., Franaszek, M., Johnson, C.D.: Computer-assisted detection of colonic polyps with CT colonography using neural networks and binary classification trees. Medical Physics 30, 52–60 (2003)

    Article  Google Scholar 

  22. Jerebko, A.K., Malley, J.D., Franaszek, M., Summers, R.M.: Multiple neural network classification scheme for detection of colonic polyps in CT colonography data sets. Academic Radiology 10, 154–160 (2003)

    Article  Google Scholar 

  23. Jerebko, A.K., Malley, J.D., Franaszek, M., Summers, R.M.: Support vector machines committee classification method for computer-aided polyp detection in CT colonography. Academic Radiology 12, 479–486 (2005)

    Article  Google Scholar 

  24. Wang, Z., Liang, Z., Li, L., Li, X., Li, B., Anderson, J., Harrington, D.: Reduction of false positives by internal features for polyp detection in CT-based virtual colonoscopy. Med. Phys. 32, 3602–3616 (2005)

    Article  Google Scholar 

  25. Li, J., Van Uitert, R., Yao, J., Petrick, N., Franaszek, M., Huang, A., Summers, R.M.: Wavelet method for CT colonography computer-aided polyp detection. Med. Phys. 35, 3527–3538 (2008)

    Article  Google Scholar 

  26. Wang, S., Yao, J., Summers, R.M.: Improved classifier for computer-aided polyp detection in CT colonography by nonlinear dimensionality reduction. Med. Phys. 35, 1377–1386 (2008)

    Article  Google Scholar 

  27. Yao, J., Li, J., Summers, R.M.: Employing Topographical Height Map In Colonic Polyp Measurement And False Positive Reduction. Pattern Recognit. 42, 1029–1040 (2009)

    Article  Google Scholar 

  28. Hongbin, Z., Zhengrong, L., Perry, J.P., Matthew, A.B., Jiangsheng, Y., Yi, F., Hongbing, L., Erica, J.P., Robert, J.R., Harris, L.C.: Increasing computer-aided detection specificity by projection features for CT colonography. Medical Physics 37, 1468–1481 (2010)

    Article  Google Scholar 

  29. Suzuki, K., Horiba, I., Sugie, N.: Efficient approximation of neural filters for removing quantum noise from images. IEEE Trans. Signal Process. 50, 1787–1799 (2002)

    Article  Google Scholar 

  30. Suzuki, K., Horiba, I., Sugie, N.: Neural edge enhancer for supervised edge enhancement from noisy images. IEEE Trans. Pattern Anal. Mach. Intell. 25, 1582–1596 (2003)

    Article  Google Scholar 

  31. Suzuki, K., Horiba, I., Sugie, N., Nanki, M.: Neural filter with selection of input features and its application to image quality improvement of medical image sequences. IEICE Trans. Inf. Syst. E85-D, 1710–1718 (2002)

    Google Scholar 

  32. Suzuki, K., Horiba, I., Sugie, N., Nanki, M.: Extraction of left ventricular contours from left ventriculograms by means of a neural edge detector. IEEE Trans. Med. Imaging 23, 330–339 (2004)

    Article  Google Scholar 

  33. Suzuki, K., Armato, S.G., Li, F., Sone, S., Doi, K.: Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose CT. Med. Phys. 30, 1602–1617 (2003)

    Article  Google Scholar 

  34. Suzuki, K., Doi, K.: How can a massive training artificial neural network (MTANN) be trained with a small number of cases in the distinction between nodules and vessels in thoracic CT? Acad. Radiol. 12, 1333–1341 (2005)

    Article  Google Scholar 

  35. Arimura, H., Katsuragawa, S., Suzuki, K., Li, F., Shiraishi, J., Sone, S., Doi, K.: Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening. Acad. Radiol. 11, 617–629 (2004)

    Article  Google Scholar 

  36. Suzuki, K., Shiraishi, J., Abe, H., MacMahon, H., Doi, K.: False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network. Acad. Radiol. 12, 191–201 (2005)

    Article  Google Scholar 

  37. Oda, S., Awai, K., Suzuki, K., Yanaga, Y., Funama, Y., MacMahon, H., Yamashita, Y.: Performance of radiologists in detection of small pulmonary nodules on chest radiographs: effect of rib suppression with a massive-training artificial neural network. AJR. Am. J. Roentgenol. 193, W397–W402 (2009)

    Article  Google Scholar 

  38. Suzuki, K., Li, F., Sone, S., Doi, K.: Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network. IEEE Trans. Med. Imaging 24, 1138–1150 (2005)

    Article  Google Scholar 

  39. Suzuki, K., Abe, H., MacMahon, H., Doi, K.: Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN). IEEE Trans. Med. Imaging 25, 406–416 (2006)

    Article  Google Scholar 

  40. Suzuki, K.: A supervised ’lesion-enhancement’ filter by use of a massive-training artificial neural network (MTANN) in computer-aided diagnosis (CAD). Phys. Med. Biol. 54, S31–S45 (2009)

    Article  Google Scholar 

  41. Nappi, J., Okamura, A., Frimmel, H., Dachman, A., Yoshida, H.: Region-based supine-prone correspondence for the reduction of false-positive CAD polyp candidates in CT colonography. Acad. Radiol. 12, 695–707 (2005)

    Article  Google Scholar 

  42. Nappi, J., Yoshida, H.: Feature-guided analysis for reduction of false positives in CAD of polyps for computed tomographic colonography. Med. Phys. 30, 1592–1601 (2003)

    Article  Google Scholar 

  43. Suzuki, K., Yoshida, H., Nappi, J., Dachman, A.H.: Massive-training artificial neural network (MTANN) for reduction of false positives in computer-aided detection of polyps: Suppression of rectal tubes. Med. Phys. 33, 3814–3824 (2006)

    Article  Google Scholar 

  44. Iordanescu, G., Summers, R.M.: Reduction of false positives on the rectal tube in computer-aided detection for CT colonography. Medical Physics 31, 2855–2862 (2004)

    Article  Google Scholar 

  45. Summers, R.M., Yao, J., Johnson, C.D.: CT colonography with computer-aided detection: automated recognition of ileocecal valve to reduce number of false-positive detections. Radiology 233, 266–272 (2004)

    Article  Google Scholar 

  46. Suzuki, K., Rockey, D.C., Dachman, A.H.: CT colonography: Advanced computer-aided detection scheme utilizing MTANNs for detection of “missed” polyps in a multicenter clinical trial. Med. Phys. 30, 2–21 (2010)

    Google Scholar 

  47. Suzuki, K., Yoshida, H., Nappi, J., Armato 3rd, S.G., Dachman, A.H.: Mixture of expert 3D massive-training ANNs for reduction of multiple types of false positives in CAD for detection of polyps in CT colonography. Med. Phys. 35, 694–703 (2008)

    Article  Google Scholar 

  48. Suzuki, K., Zhang, J., Xu, J.: Massive-training artificial neural network coupled with Laplacian-eigenfunction-based dimensionality reduction for computer-aided detection of polyps in CT colonography. IEEE Trans. Med. Imaging 29, 1907–1917 (2010)

    Article  Google Scholar 

  49. Suzuki, K., Xu, J., Zhang, J., Sheu, I.: Principal-Component Massive-Training Machine-Learning Regression for False-Positive Reduction in Computer-Aided Detection of Polyps in CT Colonography. In: Wang, F., Yan, P., Suzuki, K., Shen, D. (eds.) MLMI 2010. LNCS, vol. 6357, pp. 182–189. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  50. Xu, J., Suzuki, K.: Massive-training support vector regression and Gaussian process for false-positive reduction in computer-aided detection of polyps in CT colonography. Med. Phys. 38, 1888–1902 (2011)

    Article  Google Scholar 

  51. Johnson, C.D., Dachman, A.H.: CT colonography: the next colon screening examination? Radiology 216, 331–341 (2000)

    Article  Google Scholar 

  52. Yoshida, H., Nappi, J.: Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps. IEEE Trans. Med. Imaging 20, 1261–1274 (2001)

    Article  Google Scholar 

  53. Frimmel, H., Nappi, J., Yoshida, H.: Fast and robust computation of colon centerline in CT colonography. Med. Phys. 31, 3046–3056 (2004)

    Article  Google Scholar 

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Suzuki, K. (2011). Recent Advances in Reduction of False Positives in Computerized Detection of Polyps in CT Colonography. In: Yoshida, H., Cai, W. (eds) Virtual Colonoscopy and Abdominal Imaging. Computational Challenges and Clinical Opportunities. ABD-MICCAI 2010. Lecture Notes in Computer Science, vol 6668. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25719-3_5

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  • DOI: https://doi.org/10.1007/978-3-642-25719-3_5

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