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Placenta segmentation in ultrasound imaging: Addressing sources of uncertainty and limited field-of-view

Zimmer, V. A., Gomez, A., Skelton, E. ORCID: 0000-0003-0132-7948 , Wright, R., Wheeler, G., Deng, S., Ghavami, N., Lloyd, K., Matthew, J., Kainz, B., Rueckert, D., Hajnal, J. V. & Schnabel, J. A. (2023). Placenta segmentation in ultrasound imaging: Addressing sources of uncertainty and limited field-of-view. Medical Image Analysis, 83, article number 102639. doi: 10.1016/


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.

Publication Type: Article
Additional Information: This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher Keywords: Ultrasound placenta segmentation, Multi-task learning, Multi-view imaging, Uncertainty/variability
Subjects: R Medicine > R Medicine (General)
R Medicine > RG Gynecology and obstetrics
T Technology > T Technology (General)
Departments: School of Health & Psychological Sciences > Midwifery & Radiography
SWORD Depositor:
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