research-article

Automatic Early Gastric Cancer Segmentation in Gastroscopic Images Based on ResUnet

Authors:

Linlin ZhuAuthors Info & Claims

ICBBE '21: Proceedings of the 2021 8th International Conference on Biomedical and Bioinformatics Engineering

Pages 13 - 19

https://doi.org/10.1145/3502871.3502874

Published: 14 March 2022 Publication History

First page of PDF

References

[1]

Shiliang Ai, Chen Li, Xiaoyan Li, Tao Jiang, Marcin Grzegorzek, Changhao Sun, Md Mamunur Rahaman, Jinghua Zhang, Yudong Yao, and Hong Li. 2021. A State-of-the-Art Review for Gastric Histopathology Image Analysis Approaches and Future Development. Biomed Res. Int. 2021, (2021).

[2]

Freddie Bray, Jacques Ferlay, Isabelle Soerjomataram Rebecca L. Siegel, Lindsey A. Torre, and Ahmedin Jemal. 2018. Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA CANCER J CLIN 68, 6 (2018), 394–424.

[3]

Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam. 2017. Rethinking Atrous Convolution for Semantic Image Segmentation. (2017). Retrieved from http://arxiv.org/abs/1706.05587

[4]

Young Sin Cho, Il-Kwun Chung, Ji Hyun Kim, Yunho Jung, Tae Hoon Lee, Sang-Heum Park, and Sun-Joo Kim. 2015. Risk Factors of Developing Interval Early Gastric Cancer After Negative Endoscopy. Dig. Dis. Sci. 60, 4 (2015), 936–943.

[5]

Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. ImageNet: A large-scale hierarchical image database. In CVPR, 248–255.

[6]

Wenju Du, Nini Rao, Dingyun Liu, Hongxiu Jiang, Chengsi Luo, Zhengwen Li, Tao Gan, and Bing Zeng. 2019. Review on the Applications of Deep Learning in the Analysis of Gastrointestinal Endoscopy Images. IEEE Access 7, (2019), 142053–142069.

[7]

M. Gershman, G. and Thomson. 2021. Diagnostic upper gastrointestinal endoscopy. Springer Japan, Tokyo.

[8]

Ran Gu, Guotai Wang, Tao Song, Rui Huang, Michael Aertsen, Jan Deprest, Sebastien Ourselin, Tom Vercauteren, and Shaoting Zhang. 2021. CA-Net: Comprehensive attention convolutional neural networks for explainable medical image segmentation. IEEE Trans. Med. Imaging 40, 2 (2021), 699–711.

[9]

Kaiming He, Georgia Gkioxari, Piotr Dollar, and Ross Girshick. 2017. Mask R-CNN. In Proceedings of the IEEE International Conference on Computer Vision, 2980–2988.

[10]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proc. IEEE Int. Conf. Comput. Vis. 2015 Inter, (2015), 1026–1034.

Digital Library

[11]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, Las Vegas NV,USA, 770–778.

[12]

Geoffrey E Hinton. Rectified Linear Units Improve Restricted Boltzmann Machines. 3 .

[13]

Sergey Ioffe and Christian Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. 32nd Int. Conf. Mach. Learn. ICML 2015 1, (2015), 448–456.

[14]

Jiang Kailin, Jiang Xiaotao, Pan Jinglin, Wen Yi, Huang Yuanchen, Weng Senhui, Lan Shaoyang, Nie Kechao, Zheng Zhihua, Ji Shuling, Liu Peng, Li Peiwu, and Liu Fengbin. 2021. Current Evidence and Future Perspective of Accuracy of Artificial Intelligence Application for Early Gastric Cancer Diagnosis With Endoscopy: A Systematic and Meta-Analysis. Front. Med. 8, March (2021), 1–11.

[15]

Diederik P. Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. (2014), 1–15.

[16]

Ding Yun Liu, Tao Gan, Ni Ni Rao, Yao Wen Xing, Jie Zheng, Sang Li, Cheng Si Luo, Zhong Jun Zhou, and Yong Li Wan. 2016. Identification of lesion images from gastrointestinal endoscope based on feature extraction of combinational methods with and without learning process. Med. Image Anal. 32, (2016), 281–294.

[17]

Dingyun Liu, Hongxiu Jiang, Nini Rao, Wenju Du, Chengsi Luo, Zhengwen Li, Linlin Zhu, and Tao Gan. 2020. Depth Information-Based Automatic Annotation of Early Esophageal Cancers in Gastroscopic Images Using Deep Learning Techniques. IEEE Access 8, (2020), 97907–97919. 631

[18]

Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg. 2016. SSD: Single Shot MultiBox Detector. In Computer Vision – ECCV 2016, Springer International Publishing, Cham, 21–37.

[19]

Xiaoqi Liu, Chengliang Wang, Jianying Bai, and Guobin Liao. 2020. Fine-tuning pre-trained convolutional neural networks for gastric precancerous disease classification on magnification narrow-band imaging images. Neurocomputing 392, (2020), 253–267.

[20]

Jayan Mannath and Krish Ragunath. 2016. Role of endoscopy in early oesophageal cancer. Nature Reviews Gastroenterology and Hepatology 13, 720–730.

[21]

Ozan Oktay, Jo Schlemper, Loic Le Folgoc, Matthew Lee, Mattias Heinrich, Kazunari Misawa, Kensaku Mori, Steven McDonagh, Nils Y Hammerla, Bernhard Kainz, Ben Glocker, and Daniel Rueckert. 2018. Attention U-Net: Learning where to look for the pancreas. ar (2018). Retrieved from http://arxiv.org/abs/1804.03999

[22]

Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2016. U-net: Convolutional networks for biomedical image segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing, Cham, 234–241.

[23]

Tomoyuki Shibata, Atsushi Teramoto, Hyuga Yamada, Naoki Ohmiya, Kuniaki Saito, and Hiroshi Fujita. 2020. Automated Detection and Segmentation of Early Gastric Cancer from Endoscopic Images Using Mask R-CNN. Appl. Sci. 10, 11 (2020), 3842.

[24]

Iri Take, Qiang Shi, and Yun-shi Zhong. 2015. Progress with each passing day: role of endoscopy in early gastric cancer. Transl. Gastrointest. Cancer 4, 6 (2015), 423–428.

[25]

J Wayman, N Hayes, S A Raimes, and S M Griffin. 2000. Prescription of proton pump inhibitors before endoscopy. A potential cause of missed diagnosis of early gastric cancers. Arch. Fam. Med. 9, 4 (2000), 385–388.

[26]

Tomoyuki Yada, Chizu Yokoi, and Naomi Uemura. 2013. The current state of diagnosis and treatment for early gastric cancer. Diagn. Ther. Endosc. 2013, (2013).

[27]

Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang. 2018. UNet++: A nested U-Net architecture for medical image segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing, 3–11.

Digital Library

Cited By

Pornvoraphat PTiankanon KPittayanon RSunthornwetchapong PVateekul PRerknimitr R(2023)Real-time gastric intestinal metaplasia diagnosis tailored for bias and noisy-labeled data with multiple endoscopic imagingComputers in Biology and Medicine10.1016/j.compbiomed.2023.106582154:COnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.compbiomed.2023.106582

Recommendations

Early gastric cancer segmentation in gastroscopic images using a co-spatial attention and channel attention based triple-branch ResUnet
Highlights
- The correlation information among multiple gastroscopic images of the same lesion is confirmed to be able to improve the accuracy of EGC segmentation, but the correlation information is traditionally regarded as redundant information, ...
Abstract Background and objective
The artificial segmentation of early gastric cancer (EGC) lesions in gastroscopic images remains a challenging task due to reasons including the diversity of mucosal features, irregular edges of EGC lesions and nuances ...
Evaluation of deep learning methods for early gastric cancer detection using gastroscopic images
BACKGROUND:
A timely diagnosis of early gastric cancer (EGC) can greatly reduce the death rate of patients. However, the manual detection of EGC is a costly and low-accuracy task. The artificial intelligence (AI) method based ...
Annotating Early Esophageal Cancers Based on Two Saliency Levels of Gastroscopic Images
Abstract
Early diagnoses of esophageal cancer can greatly improve the survival rate of patients. At present, the lesion annotation of early esophageal cancers (EEC) in gastroscopic images is generally performed by medical personnel in a clinic. To reduce ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICBBE '21: Proceedings of the 2021 8th International Conference on Biomedical and Bioinformatics Engineering

November 2021

216 pages

ISBN:9781450385077

DOI:10.1145/3502871

Copyright © 2021 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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 March 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

National Natural Science Foundation of China
National Natural Science Foundation of China
Key R&D Project of Sichuan Province

Conference

ICBBE '21

ICBBE '21: 2021 8th International Conference on Biomedical and Bioinformatics Engineering

November 12 - 15, 2021

Kyoto, Japan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
76
Total Downloads

Downloads (Last 12 months)11
Downloads (Last 6 weeks)0

Reflects downloads up to 25 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Pornvoraphat PTiankanon KPittayanon RSunthornwetchapong PVateekul PRerknimitr R(2023)Real-time gastric intestinal metaplasia diagnosis tailored for bias and noisy-labeled data with multiple endoscopic imagingComputers in Biology and Medicine10.1016/j.compbiomed.2023.106582154:COnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.compbiomed.2023.106582

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Figures

Tables

Media

View Table of Conten