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Automated Precancerous Lesion Screening Using an Instance Segmentation Technique for Improving Accuracy
Precancerous screening using visual inspection with acetic acid (VIA) is suggested by the World Health Organization (WHO) for low–middle-income countries (LMICs). However, because of the limited number of gynecological oncologist clinicians in LMICs, VIA screening is primarily performed by general c...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9332449/ https://www.ncbi.nlm.nih.gov/pubmed/35897993 http://dx.doi.org/10.3390/s22155489 |
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author | Agustiansyah, Patiyus Nurmaini, Siti Nuranna, Laila Irfannuddin, Irfannuddin Sanif, Rizal Legiran, Legiran Rachmatullah, Muhammad Naufal Florina, Gavira Olipa Sapitri, Ade Iriani Darmawahyuni, Annisa |
author_facet | Agustiansyah, Patiyus Nurmaini, Siti Nuranna, Laila Irfannuddin, Irfannuddin Sanif, Rizal Legiran, Legiran Rachmatullah, Muhammad Naufal Florina, Gavira Olipa Sapitri, Ade Iriani Darmawahyuni, Annisa |
author_sort | Agustiansyah, Patiyus |
collection | PubMed |
description | Precancerous screening using visual inspection with acetic acid (VIA) is suggested by the World Health Organization (WHO) for low–middle-income countries (LMICs). However, because of the limited number of gynecological oncologist clinicians in LMICs, VIA screening is primarily performed by general clinicians, nurses, or midwives (called medical workers). However, not being able to recognize the significant pathophysiology of human papilloma virus (HPV) infection in terms of the columnar epithelial-cell, squamous epithelial-cell, and white-spot regions with abnormal blood vessels may be further aggravated by VIA screening, which achieves a wide range of sensitivity (49–98%) and specificity (75–91%); this might lead to a false result and high interobserver variances. Hence, the automated detection of the columnar area (CA), subepithelial region of the squamocolumnar junction (SCJ), and acetowhite (AW) lesions is needed to support an accurate diagnosis. This study proposes a mask-RCNN architecture to simultaneously segment, classify, and detect CA and AW lesions. We conducted several experiments using 262 images of VIA+ cervicograms, and 222 images of VIA−cervicograms. The proposed model provided a satisfactory intersection over union performance for the CA of about 63.60%, and AW lesions of about 73.98%. The dice similarity coefficient performance was about 75.67% for the CA and about 80.49% for the AW lesion. It also performed well in cervical-cancer precursor-lesion detection, with a mean average precision of about 86.90% for the CA and of about 100% for the AW lesion, while also achieving 100% sensitivity and 92% specificity. Our proposed model with the instance segmentation approach can segment, detect, and classify cervical-cancer precursor lesions with satisfying performance only from a VIA cervicogram. |
format | Online Article Text |
id | pubmed-9332449 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93324492022-07-29 Automated Precancerous Lesion Screening Using an Instance Segmentation Technique for Improving Accuracy Agustiansyah, Patiyus Nurmaini, Siti Nuranna, Laila Irfannuddin, Irfannuddin Sanif, Rizal Legiran, Legiran Rachmatullah, Muhammad Naufal Florina, Gavira Olipa Sapitri, Ade Iriani Darmawahyuni, Annisa Sensors (Basel) Article Precancerous screening using visual inspection with acetic acid (VIA) is suggested by the World Health Organization (WHO) for low–middle-income countries (LMICs). However, because of the limited number of gynecological oncologist clinicians in LMICs, VIA screening is primarily performed by general clinicians, nurses, or midwives (called medical workers). However, not being able to recognize the significant pathophysiology of human papilloma virus (HPV) infection in terms of the columnar epithelial-cell, squamous epithelial-cell, and white-spot regions with abnormal blood vessels may be further aggravated by VIA screening, which achieves a wide range of sensitivity (49–98%) and specificity (75–91%); this might lead to a false result and high interobserver variances. Hence, the automated detection of the columnar area (CA), subepithelial region of the squamocolumnar junction (SCJ), and acetowhite (AW) lesions is needed to support an accurate diagnosis. This study proposes a mask-RCNN architecture to simultaneously segment, classify, and detect CA and AW lesions. We conducted several experiments using 262 images of VIA+ cervicograms, and 222 images of VIA−cervicograms. The proposed model provided a satisfactory intersection over union performance for the CA of about 63.60%, and AW lesions of about 73.98%. The dice similarity coefficient performance was about 75.67% for the CA and about 80.49% for the AW lesion. It also performed well in cervical-cancer precursor-lesion detection, with a mean average precision of about 86.90% for the CA and of about 100% for the AW lesion, while also achieving 100% sensitivity and 92% specificity. Our proposed model with the instance segmentation approach can segment, detect, and classify cervical-cancer precursor lesions with satisfying performance only from a VIA cervicogram. MDPI 2022-07-22 /pmc/articles/PMC9332449/ /pubmed/35897993 http://dx.doi.org/10.3390/s22155489 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 Agustiansyah, Patiyus Nurmaini, Siti Nuranna, Laila Irfannuddin, Irfannuddin Sanif, Rizal Legiran, Legiran Rachmatullah, Muhammad Naufal Florina, Gavira Olipa Sapitri, Ade Iriani Darmawahyuni, Annisa Automated Precancerous Lesion Screening Using an Instance Segmentation Technique for Improving Accuracy |
title | Automated Precancerous Lesion Screening Using an Instance Segmentation Technique for Improving Accuracy |
title_full | Automated Precancerous Lesion Screening Using an Instance Segmentation Technique for Improving Accuracy |
title_fullStr | Automated Precancerous Lesion Screening Using an Instance Segmentation Technique for Improving Accuracy |
title_full_unstemmed | Automated Precancerous Lesion Screening Using an Instance Segmentation Technique for Improving Accuracy |
title_short | Automated Precancerous Lesion Screening Using an Instance Segmentation Technique for Improving Accuracy |
title_sort | automated precancerous lesion screening using an instance segmentation technique for improving accuracy |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9332449/ https://www.ncbi.nlm.nih.gov/pubmed/35897993 http://dx.doi.org/10.3390/s22155489 |
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