Abstract

Barrett’s esophagus (BE) and associated adenocarcinoma have emerged as a major health care problem. Endoscopic optical coherence tomography is a microscopic sub-surface imaging technology that has been shown to differentiate tissue layers of the gastrointestinal wall and identify dysplasia in the mucosa, and is proposed as a surveillance tool to aid in management of BE. In this work a computer-aided diagnosis (CAD) system has been demonstrated for classification of dysplasia in Barrett’s esophagus using EOCT. The system is composed of four modules: region of interest segmentation, dysplasia-related image feature extraction, feature selection, and site classification and validation. Multiple feature extraction and classification methods were evaluated and the process of developing the CAD system is described in detail. Use of multiple EOCT images to classify a single site was also investigated. A total of 96 EOCT image-biopsy pairs (63 non-dysplastic, 26 low-grade and 7 high-grade dysplastic biopsy sites) from a previously described clinical study were analyzed using the CAD system, yielding an accuracy of 84% for classification of non-dysplastic vs. dysplastic BE tissue. The results motivate continued development of CAD to potentially enable EOCT surveillance of large surface areas of Barrett’s mucosa to identify dysplasia.

© 2010 OSA

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2009 (1)

D. C. Adler, C. Zhou, T. H. Tsai, H. C. Lee, L. Becker, J. M. Schmitt, Q. Huang, J. G. Fujimoto, and H. Mashimo, “Three-dimensional optical coherence tomography of Barrett’s esophagus and buried glands beneath neosquamous epithelium following radiofrequency ablation,” Endoscopy 41(9), 773–776 (2009).
[CrossRef] [PubMed]

2007 (9)

Y. Chen, A. D. Aguirre, P. L. Hsiung, S. Desai, P. R. Herz, M. Pedrosa, Q. Huang, M. Figueiredo, S. W. Huang, A. Koski, J. M. Schmitt, J. G. Fujimoto, and H. Mashimo, “Ultrahigh resolution optical coherence tomography of Barrett’s esophagus: preliminary descriptive clinical study correlating images with histology,” Endoscopy 39(7), 599–605 (2007).
[CrossRef] [PubMed]

J. L. Hornick and R. D. Odze, “Neoplastic precursor lesions in Barrett’s esophagus,” Gastroenterol. Clin. North Am. 36(4), 775–796, v (2007).
[CrossRef] [PubMed]

H. Sayana, S. Wani, and P. Sharma, “Esophageal adenocarcinoma and Barrett’s esophagus,” Minerva Gastroenterol. Dietol. 53(2), 157–169 (2007).
[PubMed]

R. L. Ellis, A. A. Meade, M. A. Mathiason, K. M. Willison, and W. Logan-Young, “Evaluation of computer-aided detection systems in the detection of small invasive breast carcinoma,” Radiology 245(1), 88–94 (2007).
[CrossRef] [PubMed]

B. Sahiner, H. P. Chan, M. A. Roubidoux, L. M. Hadjiiski, M. A. Helvie, C. Paramagul, J. Bailey, A. V. Nees, and C. Blane, “Malignant and benign breast masses on 3D US volumetric images: effect of computer-aided diagnosis on radiologist accuracy,” Radiology 242(3), 716–724 (2007).
[CrossRef] [PubMed]

R. L. Van Uitert and R. M. Summers, “Automatic correction of level set based subvoxel precise centerlines for virtual colonoscopy using the colon outer wall,” IEEE Trans. Med. Imaging 26(8), 1069–1078 (2007).
[CrossRef] [PubMed]

S. Timp, C. Varela, and N. Karssemeijer, “Temporal change analysis for characterization of mass lesions in mammography,” IEEE Trans. Med. Imaging 26(7), 945–953 (2007).
[CrossRef] [PubMed]

S. H. Yun, G. J. Tearney, B. J. Vakoc, M. Shishkov, W. Y. Oh, A. E. Desjardins, M. J. Suter, R. C. Chan, J. A. Evans, I. K. Jang, N. S. Nishioka, J. F. de Boer, and B. E. Bouma, “Comprehensive volumetric optical microscopy in vivo,” Nat. Med. 12(12), 1429–1433 (2007).
[CrossRef] [PubMed]

B. J. Vakoc, M. Shishko, S. H. Yun, W. Y. Oh, M. J. Suter, A. E. Desjardins, J. A. Evans, N. S. Nishioka, G. J. Tearney, and B. E. Bouma, “Comprehensive esophageal microscopy by using optical frequency-domain imaging (with video),” Gastrointest. Endosc. 65(6), 898–905 (2007).
[CrossRef] [PubMed]

2006 (7)

S. H. Taplin, C. M. Rutter, and C. D. Lehman, “Testing the effect of computer-assisted detection on interpretive performance in screening mammography,” AJR Am. J. Roentgenol. 187(6), 1475–1482 (2006).
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[CrossRef] [PubMed]

C. C. Maley and A. K. Rustgi, “Barrett’s esophagus and its progression to adenocarcinoma,” J. Natl. Compr. Canc. Netw. 4(4), 367–374 (2006).
[PubMed]

M. Vieth, B. Schubert, K. Lang-Schwarz, and M. Stolte, “Frequency of Barrett’s neoplasia after initial negative endoscopy with biopsy: a long-term histopathological follow-up study,” Endoscopy 38(12), 1201–1205 (2006).
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J. A. Evans, J. M. Poneros, B. E. Bouma, J. Bressner, E. F. Halpern, M. Shishkov, G. Y. Lauwers, M. Mino-Kenudson, N. S. Nishioka, and G. J. Tearney, “Optical coherence tomography to identify intramucosal carcinoma and high-grade dysplasia in Barrett’s esophagus,” Clin. Gastroenterol. Hepatol. 4(1), 38–43 (2006).
[CrossRef] [PubMed]

X. Qi, M. V. Sivak, G. Isenberg, J. E. Willis, and A. M. Rollins, “Computer-aided diagnosis of dysplasia in Barrett’s esophagus using endoscopic optical coherence tomography,” J. Biomed. Opt. 11(4), 044010 (2006).
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A. M. Prasad, L. R. Iverson, and A. Liaw, “Newer classification and regression tree techniques: bagging and random forests for ecological prediction,” Ecosystems (N. Y.) 9(2), 181–199 (2006).
[CrossRef]

2005 (4)

V. Westphal, A. M. Rollins, J. Willis, M. V. Sivak, and J. A. Izatt, “Correlation of endoscopic optical coherence tomography with histology in the lower-GI tract,” Gastrointest. Endosc. 61(4), 537–546 (2005).
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M. Solaymani-Dodaran, R. F. Logan, J. West, and T. Card, “Mortality associated with Barrett’s esophagus and gastroesophageal reflux disease diagnoses-a population-based cohort study,” Am. J. Gastroenterol. 100(12), 2616–2621 (2005).
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K. Suzuki, F. Li, S. Sone, and K. Doi, “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(9), 1138–1150 (2005).
[CrossRef] [PubMed]

G. A. Isenberg, M. V. Sivak, A. Chak, R. C. K. Wong, J. E. Willis, B. Wolf, D. Y. Rowland, A. Das, and A. M. Rollins, “Accuracy of endoscopic optical coherence tomography in the detection of dysplasia in Barrett’s esophagus: a prospective, double-blinded study,” Gastrointest. Endosc. 62(6), 825–831 (2005).
[CrossRef] [PubMed]

2004 (10)

J. M. Poneros, “Diagnosis of Barrett’s esophagus using optical coherence tomography,” Gastrointest. Endosc. Clin. N. Am. 14(3), 573–588, x (2004).
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S. Joo, Y. S. Yang, W. K. Moon, and H. C. Kim, “Computer-aided diagnosis of solid breast nodules: use of an artificial neural network based on multiple sonographic features,” IEEE Trans. Med. Imaging 23(10), 1292–1300 (2004).
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K. Horsch, M. L. Giger, C. J. Vyborny, and L. A. Venta, “Performance of computer-aided diagnosis in the interpretation of lesions on breast sonography,” Acad. Radiol. 11(3), 272–280 (2004).
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P. Sharma, K. McQuaid, J. Dent, M. B. Fennerty, R. Sampliner, S. Spechler, A. Cameron, D. Corley, G. Falk, J. Goldblum, J. Hunter, J. Jankowski, L. Lundell, B. Reid, N. J. Shaheen, A. Sonnenberg, K. Wang, W. Weinstein, and AGA Chicago Workshop, “A critical review of the diagnosis and management of Barrett’s esophagus: the AGA Chicago Workshop,” Gastroenterology 127(1), 310–330 (2004).
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F. M. Hall, “Improved sensitivity of mammography with computer-aided detection,” AJR Am. J. Roentgenol. 182(6), 1598–1599 (2004).
[PubMed]

W. Chen, M. L. Giger, L. Lan, and U. Bick, “Computerized interpretation of breast MRI: investigation of enhancement-variance dynamics,” Med. Phys. 31(5), 1076–1082 (2004).
[CrossRef] [PubMed]

S. V. Destounis, P. DiNitto, W. Logan-Young, E. Bonaccio, M. L. Zuley, and K. M. Willison, “Can computer-aided detection with double reading of screening mammograms help decrease the false-negative rate? Initial experience,” Radiology 232(2), 578–584 (2004).
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D. Gur, J. H. Sumkin, H. E. Rockette, M. Ganott, C. Hakim, L. Hardesty, W. R. Poller, R. Shah, and L. Wallace, “Changes in breast cancer detection and mammography recall rates after the introduction of a computer-aided detection system,” J. Natl. Cancer Inst. 96(3), 185–190 (2004).
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X. Qi, M. V. Sivak, D. L. Wilson, and A. M. R. Rollins, “Computer Aided diagnosis (CAD) of dysplasia in Barrett's esophagus (BE) using endoscopic optical coherence tomography (EOCT),” Gastroenterology 126, A351 (2004).

L. Yu and H. Liu, “Efficient feature selection via analysis of relevant and redundancy,” J. Mach. Learn. Res. 5, 1205–1224 (2004).

2003 (5)

J. Reunanen, I. Guyon, and A. Elisseeff, “Overfitting in making comparisons between variable selection methods,” J. Mach. Learn. Res. 3(7-8), 1371–1382 (2003).
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R. Tutuian and D. O. Castell, “Barrett’s esophagus prevalence and epidemiology,” Gastrointest. Endosc. Clin. N. Am. 13(2), 227–232 (2003).
[CrossRef] [PubMed]

P. R. Pfau, M. V. Sivak, A. Chak, M. Kinnard, R. C. Wong, G. A. Isenberg, J. A. Izatt, A. Rollins, and V. Westphal, “Criteria for the diagnosis of dysplasia by endoscopic optical coherence tomography,” Gastrointest. Endosc. 58(2), 196–202 (2003).
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G. A. Isenberg and M. V. Sivak., “Gastrointestinal optical coherence tomography,” Tech. Gastrointest. Endosc. 5(2), 94–101 (2003).
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C. M. Chen, Y. H. Chou, K. C. Han, G. S. Hung, C. M. Tiu, H. J. Chiou, and S. Y. Chiou, “Breast lesions on sonograms: computer-aided diagnosis with nearly setting-independent features and artificial neural networks,” Radiology 226(2), 504–514 (2003).
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2002 (5)

K. Horsch, M. L. Giger, L. A. Venta, and C. J. Vyborny, “Computerized diagnosis of breast lesions on ultrasound,” Med. Phys. 29(2), 157–164 (2002).
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R. M. Summers, “Challenges for computer-aided diagnosis for CT colonography,” Abdom. Imaging 27(3), 268–274 (2002).
[PubMed]

R. E. Sampliner and Practice Parameters Committee of the American College of Gastroenterology, “Updated guidelines for the diagnosis, surveillance, and therapy of Barrett’s esophagus,” Am. J. Gastroenterol. 97(8), 1888–1895 (2002).
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M. H. Horng, Y. N. Sun, and X. Z. Lin, “Texture feature coding method for classification of liver sonography,” Comput. Med. Imaging Graph. 26(1), 33–42 (2002).
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B. van Ginneken, S. Katsuragawa, B. M. ter Haar Romeny, K. Doi, and M. A. Viergever, “Automatic detection of abnormalities in chest radiographs using local texture analysis,” IEEE Trans. Med. Imaging 21(2), 139–149 (2002).
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2001 (7)

J. Park and D. W. Edington, “A sequential neural network model for diabetes prediction,” Artif. Intell. Med. 23(3), 277–293 (2001).
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G. Zuccaro, N. Gladkova, J. Vargo, F. Feldchtein, E. Zagaynova, D. Conwell, G. Falk, J. Goldblum, J. Dumot, J. Ponsky, G. Gelikonov, B. Davros, E. Donchenko, and J. Richter, “Optical coherence tomography of the esophagus and proximal stomach in health and disease,” Am. J. Gastroenterol. 96(9), 2633–2639 (2001).
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M. L. Giger, N. Karssemeijer, and S. G. Armato, “Computer-aided diagnosis in medical imaging,” IEEE Trans. Med. Imaging 20(12), 1205–1208 (2001).
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P. M. White, E. M. Teasdale, J. M. Wardlaw, and V. Easton, “Intracranial aneurysms: CT angiography and MR angiography for detection prospective blinded comparison in a large patient cohort,” Radiology 219(3), 739–749 (2001).
[PubMed]

B. Sahiner, N. Petrick, H. P. Chan, L. M. Hadjiiski, C. Paramagul, M. A. Helvie, and M. N. Gurcan, “Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization,” IEEE Trans. Med. Imaging 20(12), 1275–1284 (2001).
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S. B. Göktürk, C. Tomasi, B. Acar, C. F. Beaulieu, D. S. Paik, R. B. Jeffrey, J. Yee, and S. Napel, “A statistical 3-D pattern processing method for computer-aided detection of polyps in CT colonography,” IEEE Trans. Med. Imaging 20(12), 1251–1260 (2001).
[CrossRef] [PubMed]

J. M. Poneros, S. Brand, B. E. Bouma, G. J. Tearney, C. C. Compton, and N. S. Nishioka, “Diagnosis of specialized intestinal metaplasia by optical coherence tomography,” Gastroenterology 120(1), 7–12 (2001).
[CrossRef] [PubMed]

2000 (10)

L. J. Warren Burhenne, S. A. Wood, C. J. D’Orsi, S. A. Feig, D. B. Kopans, K. F. O’Shaughnessy, E. A. Sickles, L. Tabar, C. J. Vyborny, and R. A. Castellino, “Potential contribution of computer-aided detection to the sensitivity of screening mammography,” Radiology 215(2), 554–562 (2000).
[PubMed]

D. G. Vince, K. J. Dixon, R. M. Cothren, and J. F. Cornhill, “Comparison of texture analysis methods for the characterization of coronary plaques in intravascular ultrasound images,” Comput. Med. Imaging Graph. 24(4), 221–229 (2000).
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K. R. DeVault, “Epidemiology and significance of Barrett’s esophagus,” Dig. Dis. 18(4), 195–202 (2000).
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B. E. Bouma, G. J. Tearney, C. C. Compton, and N. S. Nishioka, “High-resolution imaging of the human esophagus and stomach in vivo using optical coherence tomography,” Gastrointest. Endosc. 51(4), 467–474 (2000).
[CrossRef] [PubMed]

M. V. J. Sivak, K. Kobayashi, J. A. Izatt, A. M. Rollins, R. Ung-Runyawee, A. Chak, R. C. Wong, G. A. Isenberg, and J. Willis, “High-resolution endoscopic imaging of the GI tract using optical coherence tomography,” Gastrointest. Endosc. 51(4), 474–479 (2000).
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X. D. Li, S. A. Boppart, J. Van Dam, H. Mashimo, M. Mutinga, W. Drexler, M. Klein, C. Pitris, M. L. Krinsky, M. E. Brezinski, and J. G. Fujimoto, “Optical Coherence Tomography: Advanced Technology for the Endoscopic Imaging of Barrett’s Esophagus,” Endoscopy 32(12), 921–930 (2000).
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S. Jäckle, N. Gladkova, F. Feldchtein, A. Terentieva, B. Brand, G. Gelikonov, V. Gelikonov, A. Sergeev, A. Fritscher-Ravens, J. Freund, U. Seitz, S. Schröder, and N. Soehendra, “In vivo endoscopic optical coherence tomography of esophagitis, Barrett’s esophagus, and adenocarcinoma of the esophagus,” Endoscopy 32(10), 750–755 (2000).
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C. Pitris, C. Jesser, S. A. Boppart, D. Stamper, M. E. Brezinski, and J. G. Fujimoto, “Feasibility of optical coherence tomography for high-resolution imaging of human gastrointestinal tract malignancies,” J. Gastroenterol. 35(2), 87–92 (2000).
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Z. Huo, M. L. Giger, D. E. Wolverton, W. Zhong, S. Cumming, and O. I. Olopade, “Computerized analysis of mammographic parenchymal patterns for breast cancer risk assessment: feature selection,” Med. Phys. 27(1), 4–12 (2000).
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A. Das, M. V. Sivak, A. Chak, R.C.K. Wong, V. Westphal, A.M. Rollins, J. Izatt, G.A. Isenberg, and J. Willis, “Role of high-resolution endoscopic imaging using optical coherence tomography (OCT) in patients with Barrett's esophagus (BE),” Gastrointest. Endosc. 51, AB93, Part92 (2000).

1999 (2)

1998 (5)

M. A. Kupinski and M. L. Giger, “Automated seeded lesion segmentation on digital mammograms,” IEEE Trans. Med. Imaging 17(4), 510–517 (1998).
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W. Karlon, J. W. Covell, A. D. Mcculloch, J. J. Hunter, and J. H. Omens, “Automated measurement of myofiber disarray in transgenic mice with ventricular expression of ras,” Anat. Rec. 252(4), 612–625 (1998).
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K. G. A. Gilhuijs, M. L. Giger, and U. Bick, “Computerized analysis of breast lesions in three dimensions using dynamic magnetic-resonance imaging,” Med. Phys. 25(9), 1647–1654 (1998).
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L. Breiman, “Arcing classifiers,” Ann. Stat. 26, 801–849 (1998).

A. M. Rollins, S. Yazdanfar, M. Kulkarni, R. Ung-Arunyawee, and J. A. Izatt, “In vivo video rate optical coherence tomography,” Opt. Express 3(6), 219–229 (1998).
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1996 (2)

L. Breiman, “Bagging predictors,” Mach. Learn. 24(2), 123–140 (1996).
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N. Petrick, H. P. Chan, D. Wei, B. Sahiner, M. A. Helvie, and D. D. Adler, “Automated detection of breast masses on mammograms using adaptive contrast enhancement and texture classification,” Med. Phys. 23(10), 1685–1696 (1996).
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1995 (1)

D. Harwood, T. Ojala, M. Pietikainen, S. Kelman, and L. Davis, “Texture classification by center-symmetric auto-correlation using Kullback discrimination of distributions,” Pattern Recognit. Lett. 16(1), 1–10 (1995).
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1994 (1)

J. M. Schmitt, A. Knüttel, M. Yadlowsky, and M. A. Eckhaus, “Optical-coherence tomography of a dense tissue: statistics of attenuation and backscattering,” Phys. Med. Biol. 39(10), 1705–1720 (1994).
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1993 (2)

B. B. Chaudhuri, P. Kundu, and N. Sarkar, “Detection and gradation of oriented texture,” Pattern Recognit. Lett. 14(2), 147–153 (1993).
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J. M. Schmitt, A. Knüttel, and R. F. Bonner, “Measurement of optical properties of biological tissues by low-coherence reflectometry,” Appl. Opt. 32(30), 6032–6042 (1993).
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1980 (1)

K. Laws, “Rapid texture identification,” Proc. SPIE 238, 376–380 (1980).

1979 (2)

R. M. Haralick, “Statistical and structural approaches to texture,” Proc. SPIE 67, 786–804 (1979).

B. Efron, “Bootstrap methods: another look at the jackknife,” Ann. Stat. 7(1), 1–26 (1979).
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1973 (1)

R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973).
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1967 (1)

T. M. Cover and P. E. Hart, “Nearest neighbor pattern classification,” IEEE Trans. Inf. Theory 13(1), 21–27 (1967).
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1936 (1)

R. A. Fisher, “The use of multiple measurements in taxonomic problems,” Ann. Eugen. 7, 179–188 (1936).

Acar, B.

S. B. Göktürk, C. Tomasi, B. Acar, C. F. Beaulieu, D. S. Paik, R. B. Jeffrey, J. Yee, and S. Napel, “A statistical 3-D pattern processing method for computer-aided detection of polyps in CT colonography,” IEEE Trans. Med. Imaging 20(12), 1251–1260 (2001).
[CrossRef] [PubMed]

Adler, D. C.

D. C. Adler, C. Zhou, T. H. Tsai, H. C. Lee, L. Becker, J. M. Schmitt, Q. Huang, J. G. Fujimoto, and H. Mashimo, “Three-dimensional optical coherence tomography of Barrett’s esophagus and buried glands beneath neosquamous epithelium following radiofrequency ablation,” Endoscopy 41(9), 773–776 (2009).
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Adler, D. D.

N. Petrick, H. P. Chan, D. Wei, B. Sahiner, M. A. Helvie, and D. D. Adler, “Automated detection of breast masses on mammograms using adaptive contrast enhancement and texture classification,” Med. Phys. 23(10), 1685–1696 (1996).
[CrossRef] [PubMed]

Aguirre, A. D.

Y. Chen, A. D. Aguirre, P. L. Hsiung, S. Desai, P. R. Herz, M. Pedrosa, Q. Huang, M. Figueiredo, S. W. Huang, A. Koski, J. M. Schmitt, J. G. Fujimoto, and H. Mashimo, “Ultrahigh resolution optical coherence tomography of Barrett’s esophagus: preliminary descriptive clinical study correlating images with histology,” Endoscopy 39(7), 599–605 (2007).
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Armato, S. G.

M. L. Giger, N. Karssemeijer, and S. G. Armato, “Computer-aided diagnosis in medical imaging,” IEEE Trans. Med. Imaging 20(12), 1205–1208 (2001).
[CrossRef] [PubMed]

Bailey, J.

B. Sahiner, H. P. Chan, M. A. Roubidoux, L. M. Hadjiiski, M. A. Helvie, C. Paramagul, J. Bailey, A. V. Nees, and C. Blane, “Malignant and benign breast masses on 3D US volumetric images: effect of computer-aided diagnosis on radiologist accuracy,” Radiology 242(3), 716–724 (2007).
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Beaulieu, C. F.

S. B. Göktürk, C. Tomasi, B. Acar, C. F. Beaulieu, D. S. Paik, R. B. Jeffrey, J. Yee, and S. Napel, “A statistical 3-D pattern processing method for computer-aided detection of polyps in CT colonography,” IEEE Trans. Med. Imaging 20(12), 1251–1260 (2001).
[CrossRef] [PubMed]

Becker, L.

D. C. Adler, C. Zhou, T. H. Tsai, H. C. Lee, L. Becker, J. M. Schmitt, Q. Huang, J. G. Fujimoto, and H. Mashimo, “Three-dimensional optical coherence tomography of Barrett’s esophagus and buried glands beneath neosquamous epithelium following radiofrequency ablation,” Endoscopy 41(9), 773–776 (2009).
[CrossRef] [PubMed]

Bick, U.

W. Chen, M. L. Giger, L. Lan, and U. Bick, “Computerized interpretation of breast MRI: investigation of enhancement-variance dynamics,” Med. Phys. 31(5), 1076–1082 (2004).
[CrossRef] [PubMed]

K. G. A. Gilhuijs, M. L. Giger, and U. Bick, “Computerized analysis of breast lesions in three dimensions using dynamic magnetic-resonance imaging,” Med. Phys. 25(9), 1647–1654 (1998).
[CrossRef] [PubMed]

Blane, C.

B. Sahiner, H. P. Chan, M. A. Roubidoux, L. M. Hadjiiski, M. A. Helvie, C. Paramagul, J. Bailey, A. V. Nees, and C. Blane, “Malignant and benign breast masses on 3D US volumetric images: effect of computer-aided diagnosis on radiologist accuracy,” Radiology 242(3), 716–724 (2007).
[CrossRef] [PubMed]

Bonaccio, E.

S. V. Destounis, P. DiNitto, W. Logan-Young, E. Bonaccio, M. L. Zuley, and K. M. Willison, “Can computer-aided detection with double reading of screening mammograms help decrease the false-negative rate? Initial experience,” Radiology 232(2), 578–584 (2004).
[CrossRef] [PubMed]

Bonner, R. F.

Boppart, S. A.

C. Pitris, C. Jesser, S. A. Boppart, D. Stamper, M. E. Brezinski, and J. G. Fujimoto, “Feasibility of optical coherence tomography for high-resolution imaging of human gastrointestinal tract malignancies,” J. Gastroenterol. 35(2), 87–92 (2000).
[CrossRef] [PubMed]

X. D. Li, S. A. Boppart, J. Van Dam, H. Mashimo, M. Mutinga, W. Drexler, M. Klein, C. Pitris, M. L. Krinsky, M. E. Brezinski, and J. G. Fujimoto, “Optical Coherence Tomography: Advanced Technology for the Endoscopic Imaging of Barrett’s Esophagus,” Endoscopy 32(12), 921–930 (2000).
[CrossRef] [PubMed]

Bouma, B. E.

B. J. Vakoc, M. Shishko, S. H. Yun, W. Y. Oh, M. J. Suter, A. E. Desjardins, J. A. Evans, N. S. Nishioka, G. J. Tearney, and B. E. Bouma, “Comprehensive esophageal microscopy by using optical frequency-domain imaging (with video),” Gastrointest. Endosc. 65(6), 898–905 (2007).
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S. H. Yun, G. J. Tearney, B. J. Vakoc, M. Shishkov, W. Y. Oh, A. E. Desjardins, M. J. Suter, R. C. Chan, J. A. Evans, I. K. Jang, N. S. Nishioka, J. F. de Boer, and B. E. Bouma, “Comprehensive volumetric optical microscopy in vivo,” Nat. Med. 12(12), 1429–1433 (2007).
[CrossRef] [PubMed]

J. A. Evans, J. M. Poneros, B. E. Bouma, J. Bressner, E. F. Halpern, M. Shishkov, G. Y. Lauwers, M. Mino-Kenudson, N. S. Nishioka, and G. J. Tearney, “Optical coherence tomography to identify intramucosal carcinoma and high-grade dysplasia in Barrett’s esophagus,” Clin. Gastroenterol. Hepatol. 4(1), 38–43 (2006).
[CrossRef] [PubMed]

J. M. Poneros, S. Brand, B. E. Bouma, G. J. Tearney, C. C. Compton, and N. S. Nishioka, “Diagnosis of specialized intestinal metaplasia by optical coherence tomography,” Gastroenterology 120(1), 7–12 (2001).
[CrossRef] [PubMed]

B. E. Bouma, G. J. Tearney, C. C. Compton, and N. S. Nishioka, “High-resolution imaging of the human esophagus and stomach in vivo using optical coherence tomography,” Gastrointest. Endosc. 51(4), 467–474 (2000).
[CrossRef] [PubMed]

Brand, B.

S. Jäckle, N. Gladkova, F. Feldchtein, A. Terentieva, B. Brand, G. Gelikonov, V. Gelikonov, A. Sergeev, A. Fritscher-Ravens, J. Freund, U. Seitz, S. Schröder, and N. Soehendra, “In vivo endoscopic optical coherence tomography of esophagitis, Barrett’s esophagus, and adenocarcinoma of the esophagus,” Endoscopy 32(10), 750–755 (2000).
[CrossRef] [PubMed]

Brand, S.

J. M. Poneros, S. Brand, B. E. Bouma, G. J. Tearney, C. C. Compton, and N. S. Nishioka, “Diagnosis of specialized intestinal metaplasia by optical coherence tomography,” Gastroenterology 120(1), 7–12 (2001).
[CrossRef] [PubMed]

Breiman, L.

L. Breiman, “Arcing classifiers,” Ann. Stat. 26, 801–849 (1998).

L. Breiman, “Bagging predictors,” Mach. Learn. 24(2), 123–140 (1996).
[CrossRef]

Bressner, J.

J. A. Evans, J. M. Poneros, B. E. Bouma, J. Bressner, E. F. Halpern, M. Shishkov, G. Y. Lauwers, M. Mino-Kenudson, N. S. Nishioka, and G. J. Tearney, “Optical coherence tomography to identify intramucosal carcinoma and high-grade dysplasia in Barrett’s esophagus,” Clin. Gastroenterol. Hepatol. 4(1), 38–43 (2006).
[CrossRef] [PubMed]

Brezinski, M. E.

C. Pitris, C. Jesser, S. A. Boppart, D. Stamper, M. E. Brezinski, and J. G. Fujimoto, “Feasibility of optical coherence tomography for high-resolution imaging of human gastrointestinal tract malignancies,” J. Gastroenterol. 35(2), 87–92 (2000).
[CrossRef] [PubMed]

X. D. Li, S. A. Boppart, J. Van Dam, H. Mashimo, M. Mutinga, W. Drexler, M. Klein, C. Pitris, M. L. Krinsky, M. E. Brezinski, and J. G. Fujimoto, “Optical Coherence Tomography: Advanced Technology for the Endoscopic Imaging of Barrett’s Esophagus,” Endoscopy 32(12), 921–930 (2000).
[CrossRef] [PubMed]

Cameron, A.

P. Sharma, K. McQuaid, J. Dent, M. B. Fennerty, R. Sampliner, S. Spechler, A. Cameron, D. Corley, G. Falk, J. Goldblum, J. Hunter, J. Jankowski, L. Lundell, B. Reid, N. J. Shaheen, A. Sonnenberg, K. Wang, W. Weinstein, and AGA Chicago Workshop, “A critical review of the diagnosis and management of Barrett’s esophagus: the AGA Chicago Workshop,” Gastroenterology 127(1), 310–330 (2004).
[CrossRef] [PubMed]

Card, T.

M. Solaymani-Dodaran, R. F. Logan, J. West, and T. Card, “Mortality associated with Barrett’s esophagus and gastroesophageal reflux disease diagnoses-a population-based cohort study,” Am. J. Gastroenterol. 100(12), 2616–2621 (2005).
[CrossRef] [PubMed]

Castell, D. O.

R. Tutuian and D. O. Castell, “Barrett’s esophagus prevalence and epidemiology,” Gastrointest. Endosc. Clin. N. Am. 13(2), 227–232 (2003).
[CrossRef] [PubMed]

Castellino, R. A.

L. J. Warren Burhenne, S. A. Wood, C. J. D’Orsi, S. A. Feig, D. B. Kopans, K. F. O’Shaughnessy, E. A. Sickles, L. Tabar, C. J. Vyborny, and R. A. Castellino, “Potential contribution of computer-aided detection to the sensitivity of screening mammography,” Radiology 215(2), 554–562 (2000).
[PubMed]

Chak, A.

G. A. Isenberg, M. V. Sivak, A. Chak, R. C. K. Wong, J. E. Willis, B. Wolf, D. Y. Rowland, A. Das, and A. M. Rollins, “Accuracy of endoscopic optical coherence tomography in the detection of dysplasia in Barrett’s esophagus: a prospective, double-blinded study,” Gastrointest. Endosc. 62(6), 825–831 (2005).
[CrossRef] [PubMed]

P. R. Pfau, M. V. Sivak, A. Chak, M. Kinnard, R. C. Wong, G. A. Isenberg, J. A. Izatt, A. Rollins, and V. Westphal, “Criteria for the diagnosis of dysplasia by endoscopic optical coherence tomography,” Gastrointest. Endosc. 58(2), 196–202 (2003).
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Figures (5)

Fig. 1
Fig. 1

Representative EOCT images of Barrett’s esophagus without dysplasia (A), with low grade dysplasia (B), and with high grade dysplasia (C). Artifacts from the EMR cap are indicated by narrow arrows. Broad arrows indicate the tissue under analysis. Non-dysplastic tissue in Fig. 1(A) shows high intensity, more stripes and more local structure within the ROI. Low-grade dysplastic tissue in Fig. 1(B) shows lower intensity, less striping, and less local structure compared with ROI in Fig. 1(A). High-grade dysplastic tissue in Fig. 1(C) shows still lower intensity, no apparent stripes and still less local structure compared with Fig. 1(A) and 1(B).

Fig. 2
Fig. 2

EOCT image of Barrett’s esophagus (A), segmented ROI (B) and its binary mask (C). Figure 2(A). shows an unprocessed EOCT image of Barrett’s esophagus including noise and cap artifacts (narrow arrows), tissue outside the cap (dashed arrow) and tissue under analysis (broad arrow); Fig. 2(B). shows the segmented ROI with the background noise and artifacts removed; Fig. 2(C). shows the segmented ROI represented as a binary mask.

Fig. 3
Fig. 3

Stripe-like patterns in BE. Figure 3(A) shows the obvious stripe pattern within a non-dysplastic BE EOCT image; Fig. 3(B) shows no obvious stripe pattern within a high-grade dysplastic BE EOCT image

Fig. 4
Fig. 4

Loadings plot of the first five principal components from PCA. It can be seen that the image features that contribute most to the variance present in the data are: Energy 1350 from COOC, CE 00 from TFCM, contrast 00 from COOC, mean intercept from intensity model and number of stripes per unit length from stripe detection.

Fig. 5
Fig. 5

ROC curves of the first 5 principal components of 18 selected features and each corresponding area under its ROC curve.

Tables (9)

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Table 1 Extracted image features and corresponding areas under the ROC curve. Highlighted features are those for which the areas under ROC curve were larger than 0.70

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Table 2 Classification of dysplasia in BE using EOCT using leave-one-patient-out cross-validation by linear and quadratic discriminant analysis (LDA & QLA), (single-hidden-layer NN (SLNN) and learning vector quantization (LVQ) network. ND: non-dysplastic; D: dysplastic; PPV: positive predictive value; NPV: negative predictive value

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Table 3 k-nearest neighbor classification results for various choices of k using leave-one-patient-out cross-validation. ND: non-dysplastic; D: dysplastic; PPV: positive predictive value; NPV: negative predictive value

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Table 4 Classification tree results using one image per biopsy site without bagging, and with 10, 30, 50, 70 and 100 times bagging using leave-one-patient-out cross-validation. ND: non-dysplastic; D: dysplastic; PPV: positive predictive value; NPV: negative predictive value

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Table 5 Sensitivity and specificity of binary classification of non-dysplastic sites vs. dysplastic sites in BE for varying numbers of images per site used for classification (V) and numbers of frames out of V that must be classified as positive for the biopsy site to be classified as positive (R) using leave-one-patient-out cross validation

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Table 6 Sensitivity and specificity of binary classification of non-high-grade dysplastic sites vs. high-grade dysplastic sites in BE for varying numbers of images per site used for classification (V) and numbers of frames out of V that must be classified as positive for the biopsy site to be classified as positive (R) using leave-one-patient-out cross validation

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Table 7 Positive predictive value (PPV) and negative predictive value (NPV) for ND vs. D classification and NHD vs. HD classification using multiple frames per biopsy site. Shown are the V = 1, R = 1 case and the best case

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Table 8 Three-category site membership (by most conservative criteria) using leave-one-patient-out cross-validation. LD: low-grade dysplasia; ND: non-dysplasia

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Table 9 Calculation time of each module using MATLAB R2006b on an Intel CPU with 3.25 GB of RAM

Equations (1)

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