research-article

A Comparison of Segmentation Methods in Gastric Histopathology Images

Authors:

Xiaoyan LiAuthors Info & Claims

ISICDM 2020: The Fourth International Symposium on Image Computing and Digital Medicine

Pages 75 - 79

https://doi.org/10.1145/3451421.3451438

Published: 27 August 2021 Publication History

Abstract

Gastric Cancer is one of the five most common types of malignant tumors among men and women worldwide and it is very important to make precise diagnosis for the early stage of gastric cancer. In this paper, we compare eight methods in Gastric Histopathology Image Segmentation (GHIS) including most classical and state-of-the-art ones. For estimating the segmentation result, we use seven evaluation indexes. Our study carries out that deep learning method shows the effectiveness in GHIS and the DenseCRF using the U-Net feature map performs best overall.

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Cited By

Jing YLi CDu TJiang TSun HYang JShi LGao MGrzegorzek MLi X(2023)A comprehensive survey of intestine histopathological image analysis using machine vision approachesComputers in Biology and Medicine10.1016/j.compbiomed.2023.107388165(107388)Online publication date: Oct-2023
https://doi.org/10.1016/j.compbiomed.2023.107388
Du TZhao H(2022)Texture Analysis of Enhanced MRI and Pathological Slides Predicts EGFR Mutation Status in Breast CancerBioMed Research International10.1155/2022/13766592022(1-15)Online publication date: 26-May-2022
https://doi.org/10.1155/2022/1376659

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cover image ACM Other conferences

ISICDM 2020: The Fourth International Symposium on Image Computing and Digital Medicine

December 2020

239 pages

ISBN:9781450389686

DOI:10.1145/3451421

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 27 August 2021

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ISICDM 2020: The Fourth International Symposium on Image Computing and Digital Medicine

December 5 - 7, 2020

Shenyang, China

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Cited By

Jing YLi CDu TJiang TSun HYang JShi LGao MGrzegorzek MLi X(2023)A comprehensive survey of intestine histopathological image analysis using machine vision approachesComputers in Biology and Medicine10.1016/j.compbiomed.2023.107388165(107388)Online publication date: Oct-2023
https://doi.org/10.1016/j.compbiomed.2023.107388
Du TZhao H(2022)Texture Analysis of Enhanced MRI and Pathological Slides Predicts EGFR Mutation Status in Breast CancerBioMed Research International10.1155/2022/13766592022(1-15)Online publication date: 26-May-2022
https://doi.org/10.1155/2022/1376659

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