JOM KITA KE POLITEKNIK

Placenta segmentation in ultrasound imaging: Addressing sources of uncertainty and limited field-of-view (Record no. 2079)

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Personal name Zimmer, Veronika A.
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Title Placenta segmentation in ultrasound imaging: Addressing sources of uncertainty and limited field-of-view
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Date of publication, distribution, etc. 2022-09-28.
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General note /pmc/articles/PMC7614009/
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General note /pubmed/36257132
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Summary, etc. Automatic segmentation of the placenta in fetal ultrasound (US) is challenging due to the (i) high diversity of placenta appearance, (ii) the restricted quality in US resulting in highly variable reference annotations, and (iii) the limited field-of-view of US prohibiting whole placenta assessment at late gestation. In this work, we address these three challenges with a multi-task learning approach that combines the classification of placental location (e.g., anterior, posterior) and semantic placenta segmentation in a single convolutional neural network. Through the classification task the model can learn from larger and more diverse datasets while improving the accuracy of the segmentation task in particular in limited training set conditions. With this approach we investigate the variability in annotations from multiple raters and show that our automatic segmentations (Dice of 0.86 for anterior and 0.83 for posterior placentas) achieve human-level performance as compared to intra- and inter-observer variability. Lastly, our approach can deliver whole placenta segmentation using a multi-view US acquisition pipeline consisting of three stages: multi-probe image acquisition, image fusion and image segmentation. This results in high quality segmentation of larger structures such as the placenta in US with reduced image artifacts which are beyond the field-of-view of single probes.
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Terms governing use and reproduction
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Terms governing use and reproduction https://creativecommons.org/licenses/by/4.0/This work is licensed under a CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/) International license.
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Language note en
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Topical term or geographic name as entry element Article
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Genre/form data or focus term Text
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700 10 - ADDED ENTRY--PERSONAL NAME
Personal name Gomez, Alberto
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Personal name Skelton, Emily
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Personal name Wright, Robert
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Personal name Wheeler, Gavin
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700 10 - ADDED ENTRY--PERSONAL NAME
Personal name Deng, Shujie
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Personal name Ghavami, Nooshin
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9 (RLIN) 1887
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Personal name Lloyd, Karen
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9 (RLIN) 1888
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Personal name Matthew, Jacqueline
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9 (RLIN) 1889
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Personal name Kainz, Bernhard
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Personal name Rueckert, Daniel
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9 (RLIN) 1891
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Personal name Hajnal, Joseph V.
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Personal name Schnabel, Julia A.
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Note Med Image Anal
856 41 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="http://dx.doi.org/10.1016/j.media.2022.102639">http://dx.doi.org/10.1016/j.media.2022.102639</a>
Public note Connect to this object online.

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