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Automatic Segmentation of Cervical Cells Based on Star-Convex Polygons in Pap Smear Images

Cervical cancer is one of the most common cancers that threaten women’s lives, and its early screening is of great significance for the prevention and treatment of cervical diseases. Pathologically, the accurate segmentation of cervical cells plays a crucial role in the diagnosis of cervical cancer....

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Autores principales: Zhao, Yanli, Fu, Chong, Zhang, Wenchao, Ye, Chen, Wang, Zhixiao, Ma, Hong-feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9854569/
https://www.ncbi.nlm.nih.gov/pubmed/36671619
http://dx.doi.org/10.3390/bioengineering10010047
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author Zhao, Yanli
Fu, Chong
Zhang, Wenchao
Ye, Chen
Wang, Zhixiao
Ma, Hong-feng
author_facet Zhao, Yanli
Fu, Chong
Zhang, Wenchao
Ye, Chen
Wang, Zhixiao
Ma, Hong-feng
author_sort Zhao, Yanli
collection PubMed
description Cervical cancer is one of the most common cancers that threaten women’s lives, and its early screening is of great significance for the prevention and treatment of cervical diseases. Pathologically, the accurate segmentation of cervical cells plays a crucial role in the diagnosis of cervical cancer. However, the frequent presence of adherent or overlapping cervical cells in Pap smear images makes separating them individually a difficult task. Currently, there are few studies on the segmentation of adherent cervical cells, and the existing methods commonly suffer from low segmentation accuracy and complex design processes. To address the above problems, we propose a novel star-convex polygon-based convolutional neural network with an encoder-decoder structure, called SPCNet. The model accomplishes the segmentation of adherent cells relying on three steps: automatic feature extraction, star-convex polygon detection, and non-maximal suppression (NMS). Concretely, a new residual-based attentional embedding (RAE) block is suggested for image feature extraction. It fuses the deep features from the attention-based convolutional layers with the shallow features from the original image through the residual connection, enhancing the network’s ability to extract the abundant image features. And then, a polygon-based adaptive NMS (PA-NMS) algorithm is adopted to screen the generated polygon proposals and further achieve the accurate detection of adherent cells, thus allowing the network to completely segment the cell instances in Pap smear images. Finally, the effectiveness of our method is evaluated on three independent datasets. Extensive experimental results demonstrate that the method obtains superior segmentation performance compared to other well-established algorithms.
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spelling pubmed-98545692023-01-21 Automatic Segmentation of Cervical Cells Based on Star-Convex Polygons in Pap Smear Images Zhao, Yanli Fu, Chong Zhang, Wenchao Ye, Chen Wang, Zhixiao Ma, Hong-feng Bioengineering (Basel) Article Cervical cancer is one of the most common cancers that threaten women’s lives, and its early screening is of great significance for the prevention and treatment of cervical diseases. Pathologically, the accurate segmentation of cervical cells plays a crucial role in the diagnosis of cervical cancer. However, the frequent presence of adherent or overlapping cervical cells in Pap smear images makes separating them individually a difficult task. Currently, there are few studies on the segmentation of adherent cervical cells, and the existing methods commonly suffer from low segmentation accuracy and complex design processes. To address the above problems, we propose a novel star-convex polygon-based convolutional neural network with an encoder-decoder structure, called SPCNet. The model accomplishes the segmentation of adherent cells relying on three steps: automatic feature extraction, star-convex polygon detection, and non-maximal suppression (NMS). Concretely, a new residual-based attentional embedding (RAE) block is suggested for image feature extraction. It fuses the deep features from the attention-based convolutional layers with the shallow features from the original image through the residual connection, enhancing the network’s ability to extract the abundant image features. And then, a polygon-based adaptive NMS (PA-NMS) algorithm is adopted to screen the generated polygon proposals and further achieve the accurate detection of adherent cells, thus allowing the network to completely segment the cell instances in Pap smear images. Finally, the effectiveness of our method is evaluated on three independent datasets. Extensive experimental results demonstrate that the method obtains superior segmentation performance compared to other well-established algorithms. MDPI 2022-12-30 /pmc/articles/PMC9854569/ /pubmed/36671619 http://dx.doi.org/10.3390/bioengineering10010047 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhao, Yanli
Fu, Chong
Zhang, Wenchao
Ye, Chen
Wang, Zhixiao
Ma, Hong-feng
Automatic Segmentation of Cervical Cells Based on Star-Convex Polygons in Pap Smear Images
title Automatic Segmentation of Cervical Cells Based on Star-Convex Polygons in Pap Smear Images
title_full Automatic Segmentation of Cervical Cells Based on Star-Convex Polygons in Pap Smear Images
title_fullStr Automatic Segmentation of Cervical Cells Based on Star-Convex Polygons in Pap Smear Images
title_full_unstemmed Automatic Segmentation of Cervical Cells Based on Star-Convex Polygons in Pap Smear Images
title_short Automatic Segmentation of Cervical Cells Based on Star-Convex Polygons in Pap Smear Images
title_sort automatic segmentation of cervical cells based on star-convex polygons in pap smear images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9854569/
https://www.ncbi.nlm.nih.gov/pubmed/36671619
http://dx.doi.org/10.3390/bioengineering10010047
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