Combing colour detection and neural networks for gland detection

Jie Shu, Jiang Lei, Qiyang Gao, Qian Zhang

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

Abstract

Glands are objects of interest which can be used for quantitatively analysis of histology images. Detecting glands from H&E staining histological images based on neural networks, may suffer stain variation problem. In this paper, we present a new method which combines a statistical colour detection model and a neural network to cover this problem. Colours shown at glands boundaries are pre-detected and enhanced in a pre-processing step. Then a neural network model based on Faster R-CNN is learned from these colour pixels to detect glands. This method has been tested on a Colon Histology Images Challenge Contest (GlaS) held at MICCAI 2015. The experimental results have shown the proposed method is superior to either Faster R-CNN or U-net without colour detection pre-processing. In addition, this proposed method can achieve Fl-score rank 8 in detecting benign glands and rank 5 in detecting malignant glands.

Original languageEnglish
Title of host publication2019 2nd International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2019
PublisherAssociation for Computing Machinery
Pages33-36
Number of pages4
ISBN (Electronic)9781450372299
DOIs
Publication statusPublished - 16 Aug 2019
Event2nd International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2019 - Beijing, China
Duration: 16 Aug 201918 Aug 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2nd International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2019
Country/TerritoryChina
CityBeijing
Period16/08/1918/08/19

Keywords

  • Colour detection
  • Faster R-CNN
  • Gland
  • Object detection

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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