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Gland segmentation in gastric histology images: detection of intestinal metaplasia

Barmpoutis, P; Waddingham, W; Ross, C; Hamzeh, K; Alexander, DC; Jansen, M; (2022) Gland segmentation in gastric histology images: detection of intestinal metaplasia. In: 2022 30th European Signal Processing Conference (EUSIPCO). (pp. pp. 1338-1342). IEEE: Belgrade, Serbia. Green open access

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Abstract

Gastric cancer is one of the most frequent causes of cancer-related deaths worldwide. Gastric intestinal metaplasia (IM) of the mucosa of the stomach has been found to increase the risk of gastric cancer and is considered as one of the precancerous lesions. Therefore, early detection of IM may have a valuable role in histopathological risk assessment regarding the possibility of progression to cancer. Accurate segmentation and analysis of gastric glands from the histological images plays an important role in the diagnostic confirmation of IM. Thus, in this paper, we propose a framework for segmentation of gastric glands and detection of IM. More specifically, we propose the GAGL-Net for the segmentation of glands. Then, based on two features of the extracted glands we classify the tissues into normal and IM cases. The results showed that the proposed gland segmentation approach achieves an F1 score equal to 0.914. Furthermore, the proposed methodology shows great potential for the IM detection achieving an accuracy score equal to 96.6%. To evaluate the efficiency of the proposed methodology we used a publicly available dataset and we created the GAGL dataset consisting of 59 Whole Slide Images (WSI) including both IM and normal cases.

Type: Proceedings paper
Title: Gland segmentation in gastric histology images: detection of intestinal metaplasia
Event: 2022 30th European Signal Processing Conference (EUSIPCO)
ISBN-13: 9789082797091
Open access status: An open access version is available from UCL Discovery
DOI: 10.23919/EUSIPCO55093.2022.9909628
Publisher version: https://ieeexplore.ieee.org/document/9909628
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Support vector machines, Image segmentation, Stomach, Histopathology, Glands, Signal processing, Feature extraction
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute > Research Department of Pathology
URI: https://discovery.ucl.ac.uk/id/eprint/10162875
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