Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using Vascular Pattern Detection

Md Farhad Mokter; Jung Hwan Oh; Wallapak Tavanapong; Johnny Wong; Piet C. de Groen

doi:10.1007/978-3-030-59861-7_56

Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using Vascular Pattern Detection

Md Farhad Mokter, Jung Hwan Oh, Wallapak Tavanapong, Johnny Wong, Piet C. de Groen

Medicine - Gastro, Hepatology, Nutrition Division

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Scopus citations

Abstract

Endoscopic measurement of ulcerative colitis (UC) severity is important since endoscopic disease severity may better predict future outcomes in UC than symptoms. However, it is difficult to evaluate the endoscopic severity of UC objectively because of the non-uniform nature of endoscopic features associated with UC, and large variations in their patterns. In this paper, we propose a method to classify UC severity in colonoscopy videos by detecting the vascular (vein) patterns which are defined specifically in this paper as the amounts of blood vessels in the video frames. To detect these vascular patterns, we use Convolutional Neural Network (CNN) and image preprocessing methods. The experiments show that the proposed method for classifying UC severity by detecting these vascular patterns increases classification effectiveness significantly.

Original language	English (US)
Title of host publication	Machine Learning in Medical Imaging - 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Proceedings
Editors	Mingxia Liu, Chunfeng Lian, Pingkun Yan, Xiaohuan Cao
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	552-562
Number of pages	11
ISBN (Print)	9783030598600
DOIs	https://doi.org/10.1007/978-3-030-59861-7_56
State	Published - 2020
Event	11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020 - Lima, Peru Duration: Oct 4 2020 → Oct 4 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12436 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020
Country/Territory	Peru
City	Lima
Period	10/4/20 → 10/4/20

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

Colonoscopy video
Convolutional Neural Network
Medical image classification
Ulcerative colitis severity

Access

10.1007/978-3-030-59861-7_56

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Cite this

Mokter, M. F., Oh, J. H., Tavanapong, W., Wong, J., & de Groen, P. C. (2020). Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using Vascular Pattern Detection. In M. Liu, C. Lian, P. Yan, & X. Cao (Eds.), Machine Learning in Medical Imaging - 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Proceedings (pp. 552-562). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12436 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-59861-7_56

Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using Vascular Pattern Detection. / Mokter, Md Farhad; Oh, Jung Hwan; Tavanapong, Wallapak et al.
Machine Learning in Medical Imaging - 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Proceedings. ed. / Mingxia Liu; Chunfeng Lian; Pingkun Yan; Xiaohuan Cao. Springer Science and Business Media Deutschland GmbH, 2020. p. 552-562 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12436 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Mokter, MF, Oh, JH, Tavanapong, W, Wong, J & de Groen, PC 2020, Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using Vascular Pattern Detection. in M Liu, C Lian, P Yan & X Cao (eds), Machine Learning in Medical Imaging - 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12436 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 552-562, 11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020, Lima, Peru, 10/4/20. https://doi.org/10.1007/978-3-030-59861-7_56

Mokter MF, Oh JH, Tavanapong W, Wong J, de Groen PC. Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using Vascular Pattern Detection. In Liu M, Lian C, Yan P, Cao X, editors, Machine Learning in Medical Imaging - 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 552-562. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-59861-7_56

Mokter, Md Farhad ; Oh, Jung Hwan ; Tavanapong, Wallapak et al. / Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using Vascular Pattern Detection. Machine Learning in Medical Imaging - 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Proceedings. editor / Mingxia Liu ; Chunfeng Lian ; Pingkun Yan ; Xiaohuan Cao. Springer Science and Business Media Deutschland GmbH, 2020. pp. 552-562 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Endoscopic measurement of ulcerative colitis (UC) severity is important since endoscopic disease severity may better predict future outcomes in UC than symptoms. However, it is difficult to evaluate the endoscopic severity of UC objectively because of the non-uniform nature of endoscopic features associated with UC, and large variations in their patterns. In this paper, we propose a method to classify UC severity in colonoscopy videos by detecting the vascular (vein) patterns which are defined specifically in this paper as the amounts of blood vessels in the video frames. To detect these vascular patterns, we use Convolutional Neural Network (CNN) and image preprocessing methods. The experiments show that the proposed method for classifying UC severity by detecting these vascular patterns increases classification effectiveness significantly.",

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Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using Vascular Pattern Detection

Abstract

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