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HaTU-Net: Harmonic Attention Network for Automated Ovarian Ultrasound Quantification in Assisted Pregnancy
Antral follicle Count (AFC) is a non-invasive biomarker used to assess ovarian reserves through transvaginal ultrasound (TVUS) imaging. Antral follicles’ diameter is usually in the range of 2–10 mm. The primary aim of ovarian reserve monitoring is to measure the size of ovarian follicles and the num...
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/PMC9777827/ https://www.ncbi.nlm.nih.gov/pubmed/36553220 http://dx.doi.org/10.3390/diagnostics12123213 |
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author | Singh, Vivek Kumar Yousef Kalafi, Elham Cheah, Eugene Wang, Shuhang Wang, Jingchao Ozturk, Arinc Li, Qian Eldar, Yonina C. Samir, Anthony E. Kumar, Viksit |
author_facet | Singh, Vivek Kumar Yousef Kalafi, Elham Cheah, Eugene Wang, Shuhang Wang, Jingchao Ozturk, Arinc Li, Qian Eldar, Yonina C. Samir, Anthony E. Kumar, Viksit |
author_sort | Singh, Vivek Kumar |
collection | PubMed |
description | Antral follicle Count (AFC) is a non-invasive biomarker used to assess ovarian reserves through transvaginal ultrasound (TVUS) imaging. Antral follicles’ diameter is usually in the range of 2–10 mm. The primary aim of ovarian reserve monitoring is to measure the size of ovarian follicles and the number of antral follicles. Manual follicle measurement is inhibited by operator time, expertise and the subjectivity of delineating the two axes of the follicles. This necessitates an automated framework capable of quantifying follicle size and count in a clinical setting. This paper proposes a novel Harmonic Attention-based U-Net network, HaTU-Net, to precisely segment the ovary and follicles in ultrasound images. We replace the standard convolution operation with a harmonic block that convolves the features with a window-based discrete cosine transform (DCT). Additionally, we proposed a harmonic attention mechanism that helps to promote the extraction of rich features. The suggested technique allows for capturing the most relevant features, such as boundaries, shape, and textural patterns, in the presence of various noise sources (i.e., shadows, poor contrast between tissues, and speckle noise). We evaluated the proposed model on our in-house private dataset of 197 patients undergoing TransVaginal UltraSound (TVUS) exam. The experimental results on an independent test set confirm that HaTU-Net achieved a Dice coefficient score of [Formula: see text] for ovaries and [Formula: see text] for antral follicles, an improvement of [Formula: see text] and [Formula: see text] , respectively, when compared to a standard U-Net. Further, we accurately measure the follicle size, yielding the recall, and precision rates of [Formula: see text] and [Formula: see text] , respectively. |
format | Online Article Text |
id | pubmed-9777827 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97778272022-12-23 HaTU-Net: Harmonic Attention Network for Automated Ovarian Ultrasound Quantification in Assisted Pregnancy Singh, Vivek Kumar Yousef Kalafi, Elham Cheah, Eugene Wang, Shuhang Wang, Jingchao Ozturk, Arinc Li, Qian Eldar, Yonina C. Samir, Anthony E. Kumar, Viksit Diagnostics (Basel) Article Antral follicle Count (AFC) is a non-invasive biomarker used to assess ovarian reserves through transvaginal ultrasound (TVUS) imaging. Antral follicles’ diameter is usually in the range of 2–10 mm. The primary aim of ovarian reserve monitoring is to measure the size of ovarian follicles and the number of antral follicles. Manual follicle measurement is inhibited by operator time, expertise and the subjectivity of delineating the two axes of the follicles. This necessitates an automated framework capable of quantifying follicle size and count in a clinical setting. This paper proposes a novel Harmonic Attention-based U-Net network, HaTU-Net, to precisely segment the ovary and follicles in ultrasound images. We replace the standard convolution operation with a harmonic block that convolves the features with a window-based discrete cosine transform (DCT). Additionally, we proposed a harmonic attention mechanism that helps to promote the extraction of rich features. The suggested technique allows for capturing the most relevant features, such as boundaries, shape, and textural patterns, in the presence of various noise sources (i.e., shadows, poor contrast between tissues, and speckle noise). We evaluated the proposed model on our in-house private dataset of 197 patients undergoing TransVaginal UltraSound (TVUS) exam. The experimental results on an independent test set confirm that HaTU-Net achieved a Dice coefficient score of [Formula: see text] for ovaries and [Formula: see text] for antral follicles, an improvement of [Formula: see text] and [Formula: see text] , respectively, when compared to a standard U-Net. Further, we accurately measure the follicle size, yielding the recall, and precision rates of [Formula: see text] and [Formula: see text] , respectively. MDPI 2022-12-18 /pmc/articles/PMC9777827/ /pubmed/36553220 http://dx.doi.org/10.3390/diagnostics12123213 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 Singh, Vivek Kumar Yousef Kalafi, Elham Cheah, Eugene Wang, Shuhang Wang, Jingchao Ozturk, Arinc Li, Qian Eldar, Yonina C. Samir, Anthony E. Kumar, Viksit HaTU-Net: Harmonic Attention Network for Automated Ovarian Ultrasound Quantification in Assisted Pregnancy |
title | HaTU-Net: Harmonic Attention Network for Automated Ovarian Ultrasound Quantification in Assisted Pregnancy |
title_full | HaTU-Net: Harmonic Attention Network for Automated Ovarian Ultrasound Quantification in Assisted Pregnancy |
title_fullStr | HaTU-Net: Harmonic Attention Network for Automated Ovarian Ultrasound Quantification in Assisted Pregnancy |
title_full_unstemmed | HaTU-Net: Harmonic Attention Network for Automated Ovarian Ultrasound Quantification in Assisted Pregnancy |
title_short | HaTU-Net: Harmonic Attention Network for Automated Ovarian Ultrasound Quantification in Assisted Pregnancy |
title_sort | hatu-net: harmonic attention network for automated ovarian ultrasound quantification in assisted pregnancy |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9777827/ https://www.ncbi.nlm.nih.gov/pubmed/36553220 http://dx.doi.org/10.3390/diagnostics12123213 |
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