ArticleMedical image analysis2016
Slic-Seg: A minimally interactive segmentation of the placenta from sparse and motion-corrupted fetal MRI in multiple views.
Article in Medical image analysis, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 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
- Developments in functional imaging of the placenta.The British journal of radiology · 2023Review
- Influence of maternal psychological distress during COVID-19 pandemic on placental morphometry and texture.Scientific reports · 2023Article
- 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
- Article
- Computer-assisted fetal laser surgery in the treatment of twin-to-twin transfusion syndrome: Recent trends and prospects.Prenatal diagnosis · 2022Review
- SM-SegNet: A Lightweight Squeeze M-SegNet for Tissue Segmentation in Brain MRI Scans.Sensors (Basel, Switzerland) · 2022Article
- Deep learning-based segmentation of the placenta and uterus on MR images.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- MIDeepSeg: Minimally interactive segmentation of unseen objects from medical images using deep learning.Medical image analysis · 2021Article
- Fully Automated 3-D Ultrasound Segmentation of the Placenta, Amniotic Fluid, and Fetus for Early Pregnancy Assessment.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2021Article
- Introducing Biomedisa as an open-source online platform for biomedical image segmentation.Nature communications · 2020Article
- Segmentation of the placenta and its vascular tree in Doppler ultrasound for fetal surgery planning.International journal of computer assisted radiology and surgery · 2020Article
- Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges.Brain sciences · 2020Review
- Segmentation of uterus and placenta in MR images using a fully convolutional neural network.Proceedings of SPIE--the International Society for Optical Engineering · 2020Article
- Automatic segmentation of brain MRI using a novel patch-wise U-net deep architecture.PloS one · 2020Article
- Placental MRI and its application to fetal intervention.Prenatal diagnosis · 2020Review
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Authors and funding
10 authors.
Funding
Abstract
Segmentation of the placenta from fetal MRI is challenging due to sparse acquisition, inter-slice motion, and the widely varying position and shape of the placenta between pregnant women. We propose a minimally interactive framework that combines multiple volumes acquired in different views to obtain accurate segmentation of the placenta. In the first phase, a minimally interactive slice-by-slice propagation method called Slic-Seg is used to obtain an initial segmentation from a single motion-corrupted sparse volume image. It combines high-level features, online Random Forests and Conditional Random Fields, and only needs user interactions in a single slice. In the second phase, to take advantage of the complementary resolution in multiple volumes acquired in different views, we further propose a probability-based 4D Graph Cuts method to refine the initial segmentations using inter-slice and inter-image consistency. We used our minimally interactive framework to examine the placentas of 16 mid-gestation patients from MRI acquired in axial and sagittal views respectively. The results show the proposed method has 1) a good performance even in cases where sparse scribbles provided by the user lead to poor results with the competitive propagation approaches; 2) a good interactivity with low intra- and inter-operator variability; 3) higher accuracy than state-of-the-art interactive segmentation methods; and 4) an improved accuracy due to the co-segmentation based refinement, which outperforms single volume or intensity-based Graph Cuts.
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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.