Staff View: Segmentation of Overlapping Cells in Cervical Cytology Images: A Survey

Segmentation of Overlapping Cells in Cervical Cytology Images: A Survey

Pap smear testing is crucial for early diagnosis of cervical cancer, but cell overlapping poses a significant challenge to diagnostic accuracy, as improper processing of overlapping cells can lead to misclassification. While significant research efforts have been devoted to segmenting overlapping ce...

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Main Authors:	Chen, E., Ting, Hua-Nong, Chuah, Joon Huang, Zhao, Jun
Format:	Article
Published:	Institute of Electrical and Electronics Engineers 2024
Subjects:	R Medicine (General) TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.um.edu.my/47077/ https://doi.org/10.1109/ACCESS.2024.3445371
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id	my.um.eprints.47077
record_format	eprints
spelling	my.um.eprints.470772025-01-03T08:37:47Z http://eprints.um.edu.my/47077/ Segmentation of Overlapping Cells in Cervical Cytology Images: A Survey Chen, E. Ting, Hua-Nong Chuah, Joon Huang Zhao, Jun R Medicine (General) TK Electrical engineering. Electronics Nuclear engineering Pap smear testing is crucial for early diagnosis of cervical cancer, but cell overlapping poses a significant challenge to diagnostic accuracy, as improper processing of overlapping cells can lead to misclassification. While significant research efforts have been devoted to segmenting overlapping cells, there is an absence of thorough reviews covering existing studies. This survey represents the first comprehensive exploration of technologies aiming to segment overlapping cells in cervical cytology images. Initially, we collected over 100 relevant papers from various open-source databases using diverse keywords. Subsequently, we conducted a thorough analysis covering various aspects, including datasets, evaluation methods, and data augmentation techniques. We then categorized the applications into conventional machine learning and deep learning approaches, further subdividing both methods into three groups. We summarized articles that utilized conventional machine learning methods and compared the outcomes with those employing deep learning methods. Finally, we provide insights into current challenges and prospects in this critical domain. Institute of Electrical and Electronics Engineers 2024 Article PeerReviewed Chen, E. and Ting, Hua-Nong and Chuah, Joon Huang and Zhao, Jun (2024) Segmentation of Overlapping Cells in Cervical Cytology Images: A Survey. IEEE Access, 12. pp. 114170-114189. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2024.3445371 <https://doi.org/10.1109/ACCESS.2024.3445371>. https://doi.org/10.1109/ACCESS.2024.3445371 10.1109/ACCESS.2024.3445371
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	R Medicine (General) TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	R Medicine (General) TK Electrical engineering. Electronics Nuclear engineering Chen, E. Ting, Hua-Nong Chuah, Joon Huang Zhao, Jun Segmentation of Overlapping Cells in Cervical Cytology Images: A Survey
description	Pap smear testing is crucial for early diagnosis of cervical cancer, but cell overlapping poses a significant challenge to diagnostic accuracy, as improper processing of overlapping cells can lead to misclassification. While significant research efforts have been devoted to segmenting overlapping cells, there is an absence of thorough reviews covering existing studies. This survey represents the first comprehensive exploration of technologies aiming to segment overlapping cells in cervical cytology images. Initially, we collected over 100 relevant papers from various open-source databases using diverse keywords. Subsequently, we conducted a thorough analysis covering various aspects, including datasets, evaluation methods, and data augmentation techniques. We then categorized the applications into conventional machine learning and deep learning approaches, further subdividing both methods into three groups. We summarized articles that utilized conventional machine learning methods and compared the outcomes with those employing deep learning methods. Finally, we provide insights into current challenges and prospects in this critical domain.
format	Article
author	Chen, E. Ting, Hua-Nong Chuah, Joon Huang Zhao, Jun
author_facet	Chen, E. Ting, Hua-Nong Chuah, Joon Huang Zhao, Jun
author_sort	Chen, E.
title	Segmentation of Overlapping Cells in Cervical Cytology Images: A Survey
title_short	Segmentation of Overlapping Cells in Cervical Cytology Images: A Survey
title_full	Segmentation of Overlapping Cells in Cervical Cytology Images: A Survey
title_fullStr	Segmentation of Overlapping Cells in Cervical Cytology Images: A Survey
title_full_unstemmed	Segmentation of Overlapping Cells in Cervical Cytology Images: A Survey
title_sort	segmentation of overlapping cells in cervical cytology images: a survey
publisher	Institute of Electrical and Electronics Engineers
publishDate	2024
url	http://eprints.um.edu.my/47077/ https://doi.org/10.1109/ACCESS.2024.3445371
_version_	1821001885222436864
score	13.23648

Segmentation of Overlapping Cells in Cervical Cytology Images: A Survey

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