Effective Deep Learning for Semantic Segmentation Based Bleeding Zone Detection in Capsule Endoscopy Images

Tonmoy Ghosh, Linfeng Li, Jacob Chakareski

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

35 Scopus citations

Abstract

Capsule endoscopy (CE) is a non-invasive way to detect small intestinal abnormalities such as bleeding. It provides a direct vision of the patients entire gastrointestinal (GI) tract. However, a manual inspection of the huge number of images produced thereby is tedious and lengthy, and thus prone to human errors. This makes automated computer assisted decision-making appealing in this context. This paper introduces a novel deep-learning based semantic segmentation approach for bleeding zone detection in CE images. A bleeding image features three regions labeled as bleeding, non-bleeding, and background. Thus, a convolutional neural network (CNN) is trained using SegNet layers with three classes. A given CE image is segmented using our training network and the detected bleeding zones are marked. The proposed network architecture is tested on different color planes and best performance is achieved using the hue saturation and value (HSV) color space. Experimental performance evaluation is carried out on a publicly available clinical dataset, on which our framework achieves 94.42 % global accuracy and 90.69 % weighted intersection over union (IoU), two state-of-the-art classification metrics. Performance gains are demonstrated over several recent state-of-art competiting methods in terms of all performance measures we examined, including mean accuracy, mean IoU, global accuracy and weighted IoU.

Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
PublisherIEEE Computer Society
Pages3034-3038
Number of pages5
ISBN (Electronic)9781479970612
DOIs
StatePublished - Aug 29 2018
Externally publishedYes
Event25th IEEE International Conference on Image Processing, ICIP 2018 - Athens, Greece
Duration: Oct 7 2018Oct 10 2018

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference25th IEEE International Conference on Image Processing, ICIP 2018
Country/TerritoryGreece
CityAthens
Period10/7/1810/10/18

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

Keywords

  • Bleeding detection
  • Capsule endoscopy
  • Convolutional neural network
  • Deep learning
  • SegNet

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