ArticleProceedings of SPIE--the International Society for Optical Engineering2023
Deep learning based automatic segmentation of the placenta and uterine cavity on prenatal MR images.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Semi-supervised Segmentation Network Based on Prototype-Oriented Local Contrastive Learning for Pregnancy Tissue in MR Images.Journal of imaging informatics in medicine · 2026Article
- A deep learning algorithm for automatic 3D segmentation and classification of the sheep placenta in magnetic resonance images.Physiological reports · 2026Article
- Fetal Assessment Suite (FetAS): a web-based platform for automatic fetal MRI analysis using AI.Scientific reports · 2025Article
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11 authors.
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Abstract
Magnetic resonance imaging (MRI) has potential benefits in understanding fetal and placental complications in pregnancy. An accurate segmentation of the uterine cavity and placenta can help facilitate fast and automated analyses of placenta accreta spectrum and other pregnancy complications. In this study, we trained a deep neural network for fully automatic segmentation of the uterine cavity and placenta from MR images of pregnant women with and without placental abnormalities. The two datasets were axial MRI data of 241 pregnant women, among whom, 101 patients also had sagittal MRI data. Our trained model was able to perform fully automatic 3D segmentation of MR image volumes and achieved an average Dice similarity coefficient (DSC) of 92% for uterine cavity and of 82% for placenta on the sagittal dataset and an average DSC of 87% for uterine cavity and of 82% for placenta on the axial dataset. Use of our automatic segmentation method is the first step in designing an analytics tool for to assess the risk of pregnant women with placenta accreta spectrum.
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