AUTHORS: Ashkan Tashk, Jürgen Herp, Esmaeil Nadimi
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ABSTRACT: Polyp is the name of a colorectal lesion which is created by cells clumping on the lining of the colon. The colorectal polyps can lead to severe illnesses like colon cancer if they are not treated at the early stage of their development. In current days, there are very many different polyp detection strategies based on biomedical imageries such colon capsule endoscopy (CCE) and optical colonoscopy (OC). The CCE imagery is non-invasive but the quality and resolution of acquired images are low. Moreover, it costs more than OC. So, today OC is the most desired method for detecting colorectal polyps and other lesions besides of its invasiveness. To assist physicians in detecting polyps more accurately and faster, machine learning with biomedical image processing aspect emerges. One of the most the state-of-the-art strategies for polyp detection based on artificial intelligence approach are deep learning (DL) convolutional neural networks (CNNs). As the categorization and grading of polyps need significant information about their specular highlights like their exact shape, size, texture and in general heir morphological features, therefore it is very demanded to employ semantic segmentation strategies for detecting polyps and discriminating them from the background. According to this fact, a novel and innovative method for polyp detection based on their semantic segmentation is proposed in this paper. The proposed segmentation classifier is in fact a modified CNN network named as U-Net. The proposed U-Net provides an advanced and developed semantic segmentation ability for polyp detection from OC images. For evaluating the proposed network, accredited and well-known OC image databases with polyps annotated by professional gastroenterologists known as CVC-ClinicDB, CVC-ColonDB and ETIS-Larib, are employed. The results of implementation demonstrate that the proposed method can outperform the other competitive methods for polyp detection from OC images up to an accuracy of 99% which means that the life lasting hopes could be increased to a considerable ratio.
KEYWORDS: Optical colonoscopy (OC); Polyp semantic segmentation; modified U-Net; Validation Criteria
REFERENCES:
[1] Special issue: NORDCAN Cancer data from the Nordic countries.
[2] Cancer statistics – specific cancers: Source: Statistics Explained (http://ec.europa.eu/eurostat/statisticsexplained) - 17/10/2018.
[3] A. Tresca, “The Stages of Colon and Rectal Cancer,” New York Times , About.com, 2010.
[4] J. P. Hassinger, S. D. Holubar and et al. “Effectiveness of a Multimedia-Based Educational Intervention for Improving Colon Cancer Literacy in Screening Colonoscopy Patients,” Diseases of the Colon & Rectum, vol. 53, no. 1301, 2010.
[5] J. Bernal, J. Sánchez, F. Vilariño, “Towards Automatic Polyp Detection with a Polyp Appearance Model,” Elsevier Pattern Recognition, vol. 45, no. 9, pp. 3166-82, September, 2012.
[6] J. Bernal, J. Sánchez, G. Fernández-Esparrach, D. Gil, C. Rodríguez and F. Vilariño, “WMDOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians,” Elsevier Computerized Medical Imaging and Graphics, vol. 43, pp. 99-111, July 2015.
[7] Q. Li, G. Yang, Z. Chen, B. Huang, L. Chen, D. Xu, X. Zhou, S. Zhong, H. Zhang and T. Wang, “Colorectal Polyp Segmentation Using A Fully Convolutional Neural Network,” 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI2017), Shanghai, China, 14-16 October, 2017.
[8] P. Brandao, E. Mazomenos, G. Ciuti, R. Caliò, F. Bianchi, A. Menciassi, P. Dario, A. Koulaouzidis, A. Arezzo, and D. Stoyanov1, “Fully Convolutional Neural Networks for Polyp Segmentation in Colonoscopy,” SPIE Medical Imaging - Computer-Aided Diagnosis Proceedings, vol. 10134, Orlando, Florida, United States, 2017.
[9] M. Akbari, M. Mohrekesh, E. Nasr-Esfahani, S.M. Reza Soroushmehr, N. Karimi, S. Samavi, K. Najarian, “Polyp Segmentation in Colonoscopy Images Using Fully Convolutional Network,” 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA, 18-21 July, 2018.
[10] J. Bernal, J. Sánchez, F. Vilariño, Cvc-colondb: A database for assessment of polyp detection, Database, 2012.
[11] http://mv.cvc.uab.es.
[12] Y. Jia et al., “Caffe: Convolutional architecture for fast feature embedding,” 22nd ACM international conference on Multimedia, pp. 675–8, Orlando, FL., 3-7 November, 2014.
[13] O. Ronneberger, P. Fischer, and T. Brox, “UNet: Convolutional Networks for Biomedical Image Segmentation,” International Conference on Medical Image Computing and ComputerAssisted Intervention (MICCAI2015), pp. 234- 41, 2015.
[14] R. Lguensat, M. Sun, R. Fablet, E. Mason, P. Tandeo and Ge Chen, “EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddie,” 10 November, 2017.
[15] J. Silva, A. Histace, O. Romain, X. Dray, and B. Granado, “Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer,” International Journal of Computer Assisted Radiology and Surgery, vol. 9, no. 2, pp. 283–293, 2014.
