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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
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)
Dachman, A.H.: Atlas of Virtual Colonoscopy. Springer, New York (2003)
Macari, M., Bini, E.J.: CT colonography: where have we been and where are we going? Radiology 237, 819–833 (2005)
Fletcher, J.G., Booya, F., Johnson, C.D., Ahlquist, D.: CT colonography: unraveling the twists and turns. Curr. Opin. Gastroenterol 21, 90–98 (2005)
Yoshida, H., Dachman, A.H.: Computer-aided diagnosis for CT colonography. Semin Ultrasound CT MR 25, 419–431 (2004)
Yoshida, H., Dachman, A.H.: CAD techniques, challenges, and controversies in computed tomographic colonography. Abdom Imaging 30, 26–41 (2005)
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)
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)
Yoshida, H., Näppi, J.: Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps. IEEE Trans. Med. Imaging 20, 1261–1274 (2001)
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)
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)
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)
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)
Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, San Diego (1990)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, Berlin (1995)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Johnson, C.D., Dachman, A.H.: CT colonography: the next colon screening examination? Radiology 216, 331–341 (2000)
Yoshida, H., Nappi, J.: Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps. IEEE Trans. Med. Imaging 20, 1261–1274 (2001)
Frimmel, H., Nappi, J., Yoshida, H.: Fast and robust computation of colon centerline in CT colonography. Med. Phys. 31, 3046–3056 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-642-25719-3_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25718-6
Online ISBN: 978-3-642-25719-3
eBook Packages: Computer ScienceComputer Science (R0)