ArticleProceedings of SPIE--the International Society for Optical Engineering2020
Segmentation of uterus and placenta in MR images using a fully convolutional neural network.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 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
- 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
- Ensemble of fine-tuned machine learning models for hysterectomy prediction in pregnant women using magnetic resonance images.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Artificial intelligence applied to fetal MRI: A scoping review of current research.The British journal of radiology · 2023Article
- Review of deep learning and artificial intelligence models in fetal brain magnetic resonance imaging.World journal of clinical cases · 2023Review
- Evaluation of Spatial Attentive Deep Learning for Automatic Placental Segmentation on Longitudinal MRI.Journal of magnetic resonance imaging : JMRI · 2023Article
- 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
- Placenta segmentation in ultrasound imaging: Addressing sources of uncertainty and limited field-of-view.Medical image analysis · 2023Article
- SPIE Computer-Aided Diagnosis conference anniversary review.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Deep learning-based segmentation of the placenta and uterus on MR images.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Enhancing Reproductive Organ Segmentation in Pediatric CT via Adversarial Learning.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
- Assessing reproducibility in Magnetic Resonance (MR) Radiomics features between Deep-Learning segmented and Expert Manual segmented data and evaluating their diagnostic performance in Pregnant Women with suspected Placenta Accreta Spectrum (PAS).Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
- Placenta Accreta Spectrum and Hysterectomy Prediction Using MRI Radiomic Features.Proceedings of SPIE--the International Society for Optical EngineeringArticle
- Automatic Segmentation of Uterine Cavity and Placenta on MR Images Using Deep Learning.Proceedings of SPIE--the International Society for Optical EngineeringArticle
- Semi-automated three-dimensional segmentation for cardiac CT images using deep learning and randomly distributed points.Proceedings of SPIE--the International Society for Optical EngineeringArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Segmentation of the uterine cavity and placenta in fetal magnetic resonance (MR) imaging is useful for the detection of abnormalities that affect maternal and fetal health. In this study, we used a fully convolutional neural network for 3D segmentation of the uterine cavity and placenta while a minimal operator interaction was incorporated for training and testing the network. The user interaction guided the network to localize the placenta more accurately. We trained the network with 70 training and 10 validation MRI cases and evaluated the algorithm segmentation performance using 20 cases. The average Dice similarity coefficient was 92% and 82% for the uterine cavity and placenta, respectively. The algorithm could estimate the volume of the uterine cavity and placenta with average errors of 2% and 9%, respectively. The results demonstrate that the deep learning-based segmentation and volume estimation is possible and can potentially be useful for clinical applications of human placental imaging.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.