Abstract
Early diagnoses of esophageal cancer can greatly improve the survival rate of patients. At present, the lesion annotation of early esophageal cancers (EEC) in gastroscopic images is generally performed by medical personnel in a clinic. To reduce the effect of subjectivity and fatigue in manual annotation, computer-aided annotation is required. However, automated annotation of EEC lesions using images is a challenging task owing to the fine-grained variability in the appearance of EEC lesions. This study modifies the traditional EEC annotation framework and utilizes visual salient information to develop a two saliency levels-based lesion annotation (TSL-BLA) for EEC annotations on gastroscopic images. Unlike existing methods, the proposed framework has a strong ability of constraining false positive outputs. What is more, TSL-BLA is also placed an additional emphasis on the annotation of small EEC lesions. A total of 871 gastroscopic images from 231 patients were used to validate TSL-BLA. 365 of those images contain 434 EEC lesions and 506 images do not contain any lesions. 101 small lesion regions are extracted from the 434 lesions to further validate the performance of TSL-BLA. The experimental results show that the mean detection rate and Dice similarity coefficients of TSL-BLA were 97.24 and 75.15%, respectively. Compared with other state-of-the-art methods, TSL-BLA shows better performance. Moreover, it shows strong superiority when annotating small EEC lesions. It also produces fewer false positive outputs and has a fast running speed. Therefore, The proposed method has good application prospects in aiding clinical EEC diagnoses.
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References
Whiteman, D. C., Esophageal cancer: Priorities for prevention. Curr. Epidemiol. 1(3):138–148, 2014. https://doi.org/10.1007/s40471-014-0015-3.
Zhang, S. W., Zheng, R. S., Zuo, T. T., Zeng, H. M., Wen, W. Q., and He, J., Mortality and survival analysis of esophageal cancer in China. Chin. J. Oncol. 38(9):709–715, 2016. https://doi.org/10.3760/cma.j.issn.0253-3766.2016.09.014.
Iakovidis, D. K., and Koulaouzidis, A., Software for enhanced video capsule endoscopy: Challenges for essential progress. Nat. Rev. Gastroenterol. Hepatol. 12(3):172–186, 2015. https://doi.org/10.1038/nrgastro.2015.13.
Liedlgruber, M., and Uhl, A., Computer-aided decision support systems for endoscopy in the gastrointestinal tract: A review. IEEE Rev. Biomed. Eng. 4(4):73–88, 2011. https://doi.org/10.1109/RBME.2011.2175445.
Chen, Y., and Lee, J., A review of machine-vision-based analysis of wireless capsule endoscopy video. Diagn. Ther. Endosc. 2012:1–9, 2012. https://doi.org/10.1155/2012/418037.
Mahmud, N., Cohen, J., Tsourides, K., and Tyler, M. B., Computer vision and augmented reality in gastrointestinal endoscopy. Gastroenterol. Rep. 3(3):179–184, 2015. https://doi.org/10.1093/gastro/gov027.
van der Sommen, F., Zinger, S., Schoon, E. J., and With, P. H. N. D., Computer-aided detection of early cancer in the esophagus using HD endoscopy images. SPIE Med. Imaging Int. Soc. Opt. Photon. 8670:86700V, 2013. https://doi.org/10.1117/12.2001068.
van der Sommen, F., Zinger, S., Schoon, E. J., and With, P. H. N. D., Supportive automatic annotation of early esophageal cancer using local gabor and color features. Neurocomputing 144(1):92–106, 2014. https://doi.org/10.1016/j.neucom.2014.02.066.
van der Sommen, F., Zinger, S., Curvers, W. L., Bisschops, R., Pech, O., Weusten, B. L., Bergman, J. J., With, P. H. N. D., and Schoon, E. J., Computer-aided detection of early neoplastic lesions in Barrett’s esophagus. Endoscopy 48(5):617–624, 2016. https://doi.org/10.1055/s-0042-105284.
Cong, Y., Wang, S., Liu, J., Cao, J., Yang, Y. S., and Luo, J. B., Deep sparse feature selection for computer aided endoscopy diagnosis. Pattern Recogn. 48(3):907–917, 2015. https://doi.org/10.1016/j.patcog.2014.09.010.
Nawarathna, R., Oh, J., Muthukudage, J., Tavanapong, W., Wong, J., de Groen, P. C., and Tang, S. J., Abnormal image detection in endoscopy videos using a filter Bank and local binary patterns. Neurocomputing 144(1):70–91, 2014. https://doi.org/10.1016/j.neucom.2014.02.064.
Yuan, Y., Wang, J., Li, B., Meng, Q. H., Saliency based ulcer detection for wireless capsule endoscopy diagnosis. IEEE Trans. Med. Imag. 34 (10): 2046–2057, 2015. https://doi.org/10.1109/TMI.2015.2418534.
Yuan, Y., and Meng, Q. H., Polyp Classification Based on Bag of Features and Saliency in Wireless Capsule Endoscopy. In Proc. IEEE Int. Conf. Robot. Autom., 3930–3935, 2014. https://doi.org/10.1109/ICRA.2014.6907429.
Yuan, Y., Li, B., and Meng, Q. H., Bleeding frame and region detection in the wireless capsule endoscopy video. IEEE J. Biomed. Health Inform. 20(2):624–630, 2015. https://doi.org/10.1109/JBHI.2015.2399502.
Iakovidis, D. K., and Koulaouzidis, A., Automatic lesion detection in capsule endoscopy based on color saliency: Closer to an essential adjunct for reviewing software. Gastrointest. Endosc. 80(5):877–883, 2014. https://doi.org/10.1016/j.gie.2014.06.026.
Chen, H., Wang, S., Ding, Y., and Qian, D., Saliency-based bleeding localization for wireless capsule endoscopy diagnosis. In. J. Biomed. imaging. 2017:1:8, 2017. https://doi.org/10.1155/2017/8147632.
Lee, S. M., Kim, K. M., and Jae, Y., Gastric carcinoma: Morphologic classifications and molecular changes. In gastric carcinoma-new insights into current management, InTech. Rijeka 7:130–175, 2013.
Kurihara, M., Shirakabe, H., Yarita, T., Lzumi, T., Miyasaka, K., Maruyama, T., and Kobayashi, S., Diagnosis of small early gastric cancer by X-ray, endoscopy, and biopsy. Cancer Detect. Prev. 4(1–4):377–383, 1981.
Liu, D. Y., Gan, T., Rao, N. N., Xu, G. G., Zeng, B., and Li, H. L., Automatic detection of early gastrointestinal Cancer lesions based on optimal feature extraction from gastroscopic images. J. Med. Imaging Health Inf. 5(2):296–302, 2015. https://doi.org/10.1166/jmihi.2015.1390.
Liu, D. Y., Gan, T., Rao, N. N., Xing, Y. W., Zheng, J., Li, S., Luo, C. S., Zhou, Z. J., and Wan, Y. L., Identification of lesion images from gastrointestinal endoscope based on feature extraction of combinational methods with and without learning process. Med. Image Anal. 32:281–294, 2016. https://doi.org/10.1016/j.media.2016.04.007.
Hou, X., Zhang, L., Saliency detection: A spectral residual approach. In IEEE Conference on Computer Vision and Pattern Recognition, 1–8, 2007. https://doi.org/10.1109/CVPR.2007.383267.
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., and Süsstrunk, S., SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11):2274–2282, 2012. https://doi.org/10.1109/TPAMI.2012.120.
Fu, Y., Zhang, W., Mandal, M., and Meng, M. Q., Computer-aided bleeding detection in WCE video. IEEE J. Biomed. Health Inform. 18(2):636–642, 2014. https://doi.org/10.1109/JBHI.2013.2257819.
Boschetto, D., Gambaretto, G., Grisan, E.. Automatic classification of endoscopic images for premalignant conditions of the esophagus. In Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging. International Society for Optics and Photonics, vol. 9788, pp. 978808, 2016. https://doi.org/10.1117/12.2216826.
Tao, X. M., Tong, Z. J., Liu, Y., and Fu, D. D., SVM classifier for unbalanced data based on combination of ODR and BSMOTE. Control. Decis. 26(10):1535–1541, 2011.
Dice, L. R., Measures of the amount of ecologic association between species. Ecology 26(3):297–302, 1945. https://doi.org/10.2307/1932409.
Abdelsamea, M. M., Gnecco, G., and Gabe, M. M., An efficient self-organizing active contour model for image segmentation. Neurocomputing 149(PB):820–835, 2015. https://doi.org/10.1016/j.neucom.2014.07.052.
Chang, C. C., Lin, C. J., “LIBSVM: A Library for Support Vector Machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm. 2007.
Bae, S. H., and Yoon, K. J., Polyp detection via imbalanced learning and discriminative feature learning. IEEE Trans. Med. Imag. 34(11):2379–2393, 2015. https://doi.org/10.1109/TMI.2015.2434398.
Ali, H., Yasmin, M., Sharif, M., and Rehmani, M. H., Computer assisted gastric abnormalities detection using hybrid texture descriptors for chromoendoscopy images. Comput. Methods Prog. Biomed. 157:39–47, 2018. https://doi.org/10.1016/j.cmpb.2018.01.013.
Ali, H., Sharif, M., Yasmin, M., and Rehmani, M. H., Computer-based classification of chromoendoscopy images using homogeneous texture descriptors. Comput. Methods Prog. Biomed. 88:84–92, 2017. https://doi.org/10.1016/j.compbiomed.2017.07.002.
Acknowledgments
This work was funded by the National Natural Science Foundation of China (61,872,405 and 61,720,106,004), the Sichuan Science and Technology Support Program (2015SZ0191), Key Project of Natural Science Foundation of Guangdong Province (2016A030311040), the Fundamental Research Funds for the Central Universities of China (ZYGX2016J189) and Scientific Platform Improvement Project of UESTC.
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Liu, D., Rao, N., Mei, X. et al. Annotating Early Esophageal Cancers Based on Two Saliency Levels of Gastroscopic Images. J Med Syst 42, 237 (2018). https://doi.org/10.1007/s10916-018-1063-x
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DOI: https://doi.org/10.1007/s10916-018-1063-x