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Multi-centre deep learning for placenta segmentation in obstetric ultrasound with multi-observer and cross-country generalization

The placenta is crucial to fetal well-being and it plays a significant role in the pathogenesis of hypertensive pregnancy disorders. Moreover, a timely diagnosis of placenta previa may save lives. Ultrasound is the primary imaging modality in pregnancy, but high-quality imaging depends on the access...

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Autores principales: Andreasen, Lisbeth Anita, Feragen, Aasa, Christensen, Anders Nymark, Thybo, Jonathan Kistrup, Svendsen, Morten Bo S., Zepf, Kilian, Lekadir, Karim, Tolsgaard, Martin Grønnebæk
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9908915/
https://www.ncbi.nlm.nih.gov/pubmed/36755050
http://dx.doi.org/10.1038/s41598-023-29105-x
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author Andreasen, Lisbeth Anita
Feragen, Aasa
Christensen, Anders Nymark
Thybo, Jonathan Kistrup
Svendsen, Morten Bo S.
Zepf, Kilian
Lekadir, Karim
Tolsgaard, Martin Grønnebæk
author_facet Andreasen, Lisbeth Anita
Feragen, Aasa
Christensen, Anders Nymark
Thybo, Jonathan Kistrup
Svendsen, Morten Bo S.
Zepf, Kilian
Lekadir, Karim
Tolsgaard, Martin Grønnebæk
author_sort Andreasen, Lisbeth Anita
collection PubMed
description The placenta is crucial to fetal well-being and it plays a significant role in the pathogenesis of hypertensive pregnancy disorders. Moreover, a timely diagnosis of placenta previa may save lives. Ultrasound is the primary imaging modality in pregnancy, but high-quality imaging depends on the access to equipment and staff, which is not possible in all settings. Convolutional neural networks may help standardize the acquisition of images for fetal diagnostics. Our aim was to develop a deep learning based model for classification and segmentation of the placenta in ultrasound images. We trained a model based on manual annotations of 7,500 ultrasound images to identify and segment the placenta. The model's performance was compared to annotations made by 25 clinicians (experts, trainees, midwives). The overall image classification accuracy was 81%. The average intersection over union score (IoU) reached 0.78. The model’s accuracy was lower than experts’ and trainees’, but it outperformed all clinicians at delineating the placenta, IoU = 0.75 vs 0.69, 0.66, 0.59. The model was cross validated on 100 2nd trimester images from Barcelona, yielding an accuracy of 76%, IoU 0.68. In conclusion, we developed a model for automatic classification and segmentation of the placenta with consistent performance across different patient populations. It may be used for automated detection of placenta previa and enable future deep learning research in placental dysfunction.
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spelling pubmed-99089152023-02-10 Multi-centre deep learning for placenta segmentation in obstetric ultrasound with multi-observer and cross-country generalization Andreasen, Lisbeth Anita Feragen, Aasa Christensen, Anders Nymark Thybo, Jonathan Kistrup Svendsen, Morten Bo S. Zepf, Kilian Lekadir, Karim Tolsgaard, Martin Grønnebæk Sci Rep Article The placenta is crucial to fetal well-being and it plays a significant role in the pathogenesis of hypertensive pregnancy disorders. Moreover, a timely diagnosis of placenta previa may save lives. Ultrasound is the primary imaging modality in pregnancy, but high-quality imaging depends on the access to equipment and staff, which is not possible in all settings. Convolutional neural networks may help standardize the acquisition of images for fetal diagnostics. Our aim was to develop a deep learning based model for classification and segmentation of the placenta in ultrasound images. We trained a model based on manual annotations of 7,500 ultrasound images to identify and segment the placenta. The model's performance was compared to annotations made by 25 clinicians (experts, trainees, midwives). The overall image classification accuracy was 81%. The average intersection over union score (IoU) reached 0.78. The model’s accuracy was lower than experts’ and trainees’, but it outperformed all clinicians at delineating the placenta, IoU = 0.75 vs 0.69, 0.66, 0.59. The model was cross validated on 100 2nd trimester images from Barcelona, yielding an accuracy of 76%, IoU 0.68. In conclusion, we developed a model for automatic classification and segmentation of the placenta with consistent performance across different patient populations. It may be used for automated detection of placenta previa and enable future deep learning research in placental dysfunction. Nature Publishing Group UK 2023-02-08 /pmc/articles/PMC9908915/ /pubmed/36755050 http://dx.doi.org/10.1038/s41598-023-29105-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Andreasen, Lisbeth Anita
Feragen, Aasa
Christensen, Anders Nymark
Thybo, Jonathan Kistrup
Svendsen, Morten Bo S.
Zepf, Kilian
Lekadir, Karim
Tolsgaard, Martin Grønnebæk
Multi-centre deep learning for placenta segmentation in obstetric ultrasound with multi-observer and cross-country generalization
title Multi-centre deep learning for placenta segmentation in obstetric ultrasound with multi-observer and cross-country generalization
title_full Multi-centre deep learning for placenta segmentation in obstetric ultrasound with multi-observer and cross-country generalization
title_fullStr Multi-centre deep learning for placenta segmentation in obstetric ultrasound with multi-observer and cross-country generalization
title_full_unstemmed Multi-centre deep learning for placenta segmentation in obstetric ultrasound with multi-observer and cross-country generalization
title_short Multi-centre deep learning for placenta segmentation in obstetric ultrasound with multi-observer and cross-country generalization
title_sort multi-centre deep learning for placenta segmentation in obstetric ultrasound with multi-observer and cross-country generalization
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9908915/
https://www.ncbi.nlm.nih.gov/pubmed/36755050
http://dx.doi.org/10.1038/s41598-023-29105-x
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