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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish (US)
Title of host publicationMachine Learning in Medical Imaging - 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Proceedings
EditorsMingxia Liu, Chunfeng Lian, Pingkun Yan, Xiaohuan Cao
PublisherSpringer Science and Business Media Deutschland GmbH
Pages552-562
Number of pages11
ISBN (Print)9783030598600
DOIs
StatePublished - 2020
Event11th 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 2020Oct 4 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12436 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th 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/TerritoryPeru
CityLima
Period10/4/2010/4/20

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

  • Colonoscopy video
  • Convolutional Neural Network
  • Medical image classification
  • Ulcerative colitis severity

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