ArticleProceedings of SPIE--the International Society for Optical Engineering
Automatic Segmentation of Uterine Cavity and Placenta on MR Images Using Deep Learning.
Article in Proceedings of SPIE--the International Society for Optical Engineering. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Roles and applications of artificial intelligence in fetal and placental MRI: a literature review.BMC pregnancy and childbirth · 2026Pooled it
- MDIFN: a deep learning-based multi-dimensional interaction fusion network for placental MRI segmentation.Quantitative imaging in medicine and surgery · 2026Article
- A deep learning algorithm for automatic 3D segmentation and classification of the sheep placenta in magnetic resonance images.Physiological reports · 2026Article
- PlaNet-S: an Automatic Semantic Segmentation Model for Placenta Using U-Net and SegNeXt.Journal of imaging informatics in medicine · 2026Article
- Magnetic Resonance Imaging-Based Radiomics of Axial and Sagittal Orientation in Pregnant Patients with Suspected Placenta Accreta Spectrum.Academic radiology · 2025Article
- Review
- 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
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Magnetic resonance imaging (MRI) is useful for the detection of abnormalities affecting maternal and fetal health. In this study, we used a fully convolutional neural network for simultaneous segmentation of the uterine cavity and placenta on MR images. We trained the network with MR images of 181 patients, with 157 for training and 24 for validation. The segmentation performance of the algorithm was evaluated using MR images of 60 additional patients that were not involved in training. The average Dice similarity coefficients achieved for the uterine cavity and placenta were 92% and 80%, respectively. The algorithm could estimate the volume of the uterine cavity and placenta with average errors of less than 1.1% compared to manual estimations. Automated segmentation, when incorporated into clinical use, has the potential to quantify, standardize, and improve placental assessment, resulting in improved outcomes for mothers and fetuses.
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Registered trials
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