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AFNet Algorithm for Automatic Amniotic Fluid Segmentation from Fetal MRI

Amniotic Fluid Volume (AFV) is a crucial fetal biomarker when diagnosing specific fetal abnormalities. This study proposes a novel Convolutional Neural Network (CNN) model, AFNet, for segmenting amniotic fluid (AF) to facilitate clinical AFV evaluation. AFNet was trained and tested on a manually seg...

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Detalles Bibliográficos
Autores principales: Costanzo, Alejo, Ertl-Wagner, Birgit, Sussman, Dafna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10376488/
https://www.ncbi.nlm.nih.gov/pubmed/37508809
http://dx.doi.org/10.3390/bioengineering10070783
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author Costanzo, Alejo
Ertl-Wagner, Birgit
Sussman, Dafna
author_facet Costanzo, Alejo
Ertl-Wagner, Birgit
Sussman, Dafna
author_sort Costanzo, Alejo
collection PubMed
description Amniotic Fluid Volume (AFV) is a crucial fetal biomarker when diagnosing specific fetal abnormalities. This study proposes a novel Convolutional Neural Network (CNN) model, AFNet, for segmenting amniotic fluid (AF) to facilitate clinical AFV evaluation. AFNet was trained and tested on a manually segmented and radiologist-validated AF dataset. AFNet outperforms ResUNet++ by using efficient feature mapping in the attention block and transposing convolutions in the decoder. Our experimental results show that AFNet achieved a mean Intersection over Union (mIoU) of 93.38% on our dataset, thereby outperforming other state-of-the-art models. While AFNet achieves performance scores similar to those of the UNet++ model, it does so while utilizing merely less than half the number of parameters. By creating a detailed AF dataset with an improved CNN architecture, we enable the quantification of AFV in clinical practice, which can aid in diagnosing AF disorders during gestation.
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spelling pubmed-103764882023-07-29 AFNet Algorithm for Automatic Amniotic Fluid Segmentation from Fetal MRI Costanzo, Alejo Ertl-Wagner, Birgit Sussman, Dafna Bioengineering (Basel) Article Amniotic Fluid Volume (AFV) is a crucial fetal biomarker when diagnosing specific fetal abnormalities. This study proposes a novel Convolutional Neural Network (CNN) model, AFNet, for segmenting amniotic fluid (AF) to facilitate clinical AFV evaluation. AFNet was trained and tested on a manually segmented and radiologist-validated AF dataset. AFNet outperforms ResUNet++ by using efficient feature mapping in the attention block and transposing convolutions in the decoder. Our experimental results show that AFNet achieved a mean Intersection over Union (mIoU) of 93.38% on our dataset, thereby outperforming other state-of-the-art models. While AFNet achieves performance scores similar to those of the UNet++ model, it does so while utilizing merely less than half the number of parameters. By creating a detailed AF dataset with an improved CNN architecture, we enable the quantification of AFV in clinical practice, which can aid in diagnosing AF disorders during gestation. MDPI 2023-06-30 /pmc/articles/PMC10376488/ /pubmed/37508809 http://dx.doi.org/10.3390/bioengineering10070783 Text en © 2023 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
Costanzo, Alejo
Ertl-Wagner, Birgit
Sussman, Dafna
AFNet Algorithm for Automatic Amniotic Fluid Segmentation from Fetal MRI
title AFNet Algorithm for Automatic Amniotic Fluid Segmentation from Fetal MRI
title_full AFNet Algorithm for Automatic Amniotic Fluid Segmentation from Fetal MRI
title_fullStr AFNet Algorithm for Automatic Amniotic Fluid Segmentation from Fetal MRI
title_full_unstemmed AFNet Algorithm for Automatic Amniotic Fluid Segmentation from Fetal MRI
title_short AFNet Algorithm for Automatic Amniotic Fluid Segmentation from Fetal MRI
title_sort afnet algorithm for automatic amniotic fluid segmentation from fetal mri
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10376488/
https://www.ncbi.nlm.nih.gov/pubmed/37508809
http://dx.doi.org/10.3390/bioengineering10070783
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