ArticleMedical image analysis2023
Placenta segmentation in ultrasound imaging: Addressing sources of uncertainty and limited field-of-view.
Article in Medical image analysis, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Development and External Validation of an Efficient Deep Learning Model for Lesion Segmentation and Subtyping of Hepatic Echinococcosis in Ultrasound Imaging.Health care science · 2026Article
- Uncertainty Quantification in Medical Image Segmentation: A Comprehensive Survey.Journal of imaging · 2026Review
- Adaptive patch sampling and location-aware reasoning for whole body PET-CT multi-organ segmentation.Scientific reports · 2026Article
- SS_CASE_UNet: an attention-enhanced semi-supervised framework for fetal cerebellum segmentation in ultrasound images.Scientific reports · 2025Article
- An AI method to predict pregnancy loss by extracting biological indicators from embryo ultrasound recordings in early pregnancy.Scientific reports · 2025Article
- Placenta segmentation redefined: review of deep learning integration of magnetic resonance imaging and ultrasound imaging.Visual computing for industry, biomedicine, and art · 2025Review
- Automated multivolume placental reconstruction using three-dimensional power Doppler ultrasound and infrared camera tracking.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2025Article
- PRISM Lite: A lightweight model for interactive 3D placenta segmentation in ultrasound.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Article
- Deep learning based automatic segmentation of the placenta and uterine cavity on prenatal MR images.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
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Authors and funding
13 authors.
Funding
Abstract
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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