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Updated: Sep 12, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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OXSeg: Multidimensional Attention UNet-Based Lip Segmentation Using Semi-Supervised Lip Contours.

Hanie Moghaddasi1,2, Christina Chambers3, Sarah N Mattson4

  • 1Nuffield Department of Women's and Reproductive Health, University of Oxford, OX3 9DU Oxford, U.K.

IEEE Access : Practical Innovations, Open Solutions
|August 6, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel lip segmentation technique using attention UNet and multidimensional inputs, improving accuracy for lip analysis and fetal alcohol syndrome (FAS) identification.

Keywords:
Attention UNetfetal alcohol syndromelip segmentationmask generationmultidimensional inputssequential networks

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Area of Science:

  • Computer Vision
  • Medical Imaging Analysis
  • Biomedical Engineering

Background:

  • Supervised lip segmentation accuracy is limited by training data availability and image quality variations.
  • Inaccuracies in lip boundary detection affect applications like lip-reading and diagnostics.
  • Existing methods struggle with variations in lighting, skin tone, and image quality.

Purpose of the Study:

  • To develop an improved lip segmentation method addressing limitations of current techniques.
  • To enhance the accuracy of lip contour reconstruction using novel input features.
  • To evaluate the method's effectiveness in identifying lip-related anomalies in fetal alcohol syndrome (FAS).

Main Methods:

  • Proposed a sequential lip segmentation approach integrating attention UNet and multidimensional inputs.
  • Utilized local binary patterns to extract micro-patterns for creating multidimensional inputs.
  • Developed a mask generation method using anatomical landmarks to refine lip contour estimation.

Main Results:

  • Achieved a mean dice score of 84.75% and 99.77% pixel accuracy for upper lip segmentation.
  • Demonstrated high accuracy (98.55%) in identifying fetal alcohol syndrome (FAS) using a generative adversarial network (GAN) classifier.
  • The method showed improved segmentation, particularly around the Cupid's bow.

Conclusions:

  • The proposed method significantly enhances lip segmentation accuracy, overcoming challenges related to image quality and data limitations.
  • This technique offers a robust tool for analyzing lip morphology and identifying distinct lip characteristics associated with FAS.
  • The integration of attention UNet and multidimensional inputs provides a promising direction for medical image analysis applications.