Abstract
Pixel-wise image segmentation is a highly demanding task in medical-image analysis. In practice, it is difficult to find annotated medical images with corresponding segmentation masks. In this paper, we present Kvasir-SEG: an open-access dataset of gastrointestinal polyp images and corresponding segmentation masks, manually annotated by a medical doctor and then verified by an experienced gastroenterologist. Moreover, we also generated the bounding boxes of the polyp regions with the help of segmentation masks. We demonstrate the use of our dataset with a traditional segmentation approach and a modern deep-learning based Convolutional Neural Network (CNN) approach. The dataset will be of value for researchers to reproduce results and compare methods. By adding segmentation masks to the Kvasir dataset, which only provide frame-wise annotations, we enable multimedia and computer vision researchers to contribute in the field of polyp segmentation and automatic analysis of colonoscopy images.
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References
Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: Proceeding of the ACM Symposium on Operating Systems Design and Implementation (SOSP), pp. 265–283 (2016)
Bernal, J., Sánchez, F.J., Fernández-Esparrach, G., Gil, D., Rodríguez, C., Vilariño, F.: WM-DOVA maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. Comput. Med. Imag. Graph. 43, 99–111 (2015)
Bernal, J., Sánchez, J., Vilarino, F.: Towards automatic polyp detection with a polyp appearance model. Pattern Recogn. 45(9), 3166–3182 (2012)
Bernal, J., et al.: Comparative validation of polyp detection methods in video colonoscopy: results from the MICCAI 2015 endoscopic vision challenge. IEEE Trans. Med. Imag. 36(6), 1231–1249 (2017)
Boccardi, M., et al.: Survey of protocols for the manual segmentation of the hippocampus: preparatory steps towards a joint EADC-ADNI harmonized protocol. J. Alzheimer’s Dis. 26(s3), 61–75 (2011)
Cai, W., Chen, S., Zhang, D.: Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recogn. 40(3), 825–838 (2007)
Chollet, F.: Building powerful image classification models using very little data. Keras Blog (2016)
Chollet, F.: Keras: The Python Deep Learning Library. Astrophysics Source Code Library (2018)
Dravid, A.: Employing deep networks for image processing on small research datasets. Microsc. Today 27(1), 18–23 (2019)
Goldbloom, A., Hamner, B., et al.: Kaggle: your home for data science. Competition, Kaggle Inc. (2019). https://www.kaggle.com. Accessed 12 July 2019
Haggar, F.A., Boushey, R.P.: Colorectal cancer epidemiology: incidence, mortality, survival, and risk factors. Clin. Colon Rectal Surg. 22(04), 191–197 (2009)
Kaminski, M.F., et al.: Increased rate of adenoma detection associates with reduced risk of colorectal cancer and death. Gastroenterology 153(1), 98–105 (2017)
Kang, J., Gwak, J.: Ensemble of instance segmentation models for polyp segmentation in colonoscopy images. IEEE Access 7, 26440–26447 (2019)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)
Pham, D.L., Xu, C., Prince, J.L.: Current methods in medical image segmentation. Ann. Rev. Biomed. Eng. 2(1), 315–337 (2000)
Pogorelov, K., et al.: Kvasir: a multi-class image dataset for computer aided gastrointestinal disease detection. In: Proceedings of Multimedia Systems Conference (MMSYS), pp. 164–169. ACM (2017)
Pogorelov, K., et al.: Medico multimedia task at MediaEval 2018. In: CEUR Workshop Proceedings - Multimedia Benchmark Workshop (MediaEval) (2018)
Pozdeev, A.A., Obukhova, N.A., Motyko, A.A.: Automatic analysis of endoscopic images for polyps detection and segmentation. In: IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), pp. 1216–1220. IEEE (2019)
Riegler, M., et al.: Multimedia and medicine: teammates for better disease detection and survival. In: Proceedings of ACM Multimedia (ACM MM), pp. 968–977. ACM (2016)
Riegler, M., et al.: Multimedia for medicine: the medico task at Mediaeval 2017. In: CEUR Workshop Proceedings - Multimedia Benchmark Workshop (MediaEval) (2017)
Rundle, A.G., Lebwohl, B., Vogel, R., Levine, S., Neugut, A.I.: Colonoscopic screening in average-risk individuals ages 40 to 49 vs 50 to 59 years. Gastroenterology 134(5), 1311–1315 (2008)
Sharma, M., Rasmuson, D., Rieger, B., Kjelkerud, D., et al.: Labelbox: the best way to create and manage training data. Software, LabelBox Inc. (2019). https://www.labelbox.com/. Accessed 21 May 2019
Silva, J., Histace, A., Romain, O., Dray, X., Granado, B.: Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer. Int. J. Comput. Assist. Radiol. Surg. 9(2), 283–293 (2014)
Tajbakhsh, N., Gurudu, S.R., Liang, J.: Automated polyp detection in colonoscopy videos using shape and context information. IEEE Trans. Med. Imag. 35(2), 630–644 (2015)
Torre, L.A., Bray, F., Siegel, R.L., Ferlay, J., Lortet-Tieulent, J., Jemal, A.: Global cancer statistics, 2012. CA: Cancer J. Clin. 65(2), 87–108 (2015)
Van Rijn, J.C., Reitsma, J.B., Stoker, J., Bossuyt, P.M., Van Deventer, S.J., Dekker, E.: Polyp miss rate determined by tandem colonoscopy: a systematic review. Am. J. Gastroenterol. 101(2), 343 (2006)
Visser, M., et al.: Inter-rater agreement in glioma segmentations on longitudinal MRI. NeuroImage: Clin. 22, 101727 (2019)
Zhang, Z., Liu, Q., Wang, Y.: Road extraction by deep residual U-net. IEEE Geosci. Rem. Sens. Lett. 15(5), 749–753 (2018)
Acknowledgements
This work is funded in part by the Research Council of Norway projects number 263248 (Privaton). We performed all computations in this paper on equipment provided by the Experimental Infrastructure for Exploration of Exascale Computing (\(eX^3\)), which is financially supported by the Research Council of Norway under contract 270053.
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Jha, D. et al. (2020). Kvasir-SEG: A Segmented Polyp Dataset. In: Ro, Y., et al. MultiMedia Modeling. MMM 2020. Lecture Notes in Computer Science(), vol 11962. Springer, Cham. https://doi.org/10.1007/978-3-030-37734-2_37
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DOI: https://doi.org/10.1007/978-3-030-37734-2_37
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