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Related Experiment Video

Updated: Feb 21, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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PlaNet-S: an Automatic Semantic Segmentation Model for Placenta Using U-Net and SegNeXt.

Isso Saito1, Shinnosuke Yamamoto1, Eichi Takaya2,3

  • 1Department of Clinical Imaging, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan.

Journal of Imaging Informatics in Medicine
|May 27, 2025
PubMed
Summary
This summary is machine-generated.

A novel deep learning model, Placental Segmentation Network (PlaNet-S), was developed for automated placenta segmentation in MRI scans. PlaNet-S demonstrates superior accuracy in segmenting placental structures compared to existing models, improving analysis of placental abnormalities.

Keywords:
Automatic semantic segmentationDeep learningMagnetic resonance imagingPlacentaVision transformer

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Deep Learning

Background:

  • Accurate placenta segmentation is crucial for diagnosing placental abnormalities using MRI.
  • Manual segmentation is time-consuming and subjective.
  • Deep learning models offer potential for automated and objective analysis.

Purpose of the Study:

  • To develop and evaluate a fully automated semantic placenta segmentation model using ensemble learning.
  • To integrate U-Net and SegNeXt architectures for enhanced segmentation performance.
  • To compare the developed model against existing state-of-the-art methods.

Main Methods:

  • Developed Placental Segmentation Network (PlaNet-S) by ensembling U-Net and SegNeXt architectures.
  • Utilized a dataset of 1090 annotated MRI images from 218 pregnant women with suspected placental abnormalities.
  • Assessed performance using Intersection over Union (IoU) and Counting Connected Components (CCC) metrics.

Main Results:

  • PlaNet-S achieved a significantly higher IoU (0.78) than U-Net (0.73) and DS-transUNet (0.64) (p<0.005).
  • PlaNet-S showed comparable IoU to U-Net++ (0.77).
  • PlaNet-S significantly outperformed all compared models in CCC, matching ground truth in 86.0% of cases.

Conclusions:

  • The developed PlaNet-S model provides accurate and automated placenta segmentation.
  • Ensemble learning integrating U-Net and SegNeXt enhances segmentation performance.
  • PlaNet-S offers a promising tool for clinical applications in placental imaging analysis.