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Updated: Sep 10, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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TransSeg: Leveraging Transformer With Channel-Wise Attention and Semantic Memory for Semi-Supervised Ultrasound

Jun Lyu, Liangjiang Li, Selwa A F Al-Hazzaa

IEEE Journal of Biomedical and Health Informatics

|August 25, 2025

Summary

This summary is machine-generated.

This study introduces TransSeg, a novel semi-supervised segmentation network for analyzing fetal head progression during labor using ultrasound. TransSeg reduces reliance on manual data annotation, improving real-time clinical applications.

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Area of Science:

Medical Imaging
Artificial Intelligence
Obstetrics

Background:

Transperineal ultrasound provides real-time midsagittal images during labor for fetal head assessment.
Accurate segmentation of pubic symphysis and fetal head is crucial for calculating the angle of progression (AoP).
Current deep learning segmentation methods require extensive manual annotations, limiting practical use.

Purpose of the Study:

To develop an innovative semi-supervised segmentation network (TransSeg) for fetal head position analysis.
To overcome the data dependency of existing segmentation models in clinical settings.
To enhance the utilization of unlabeled ultrasound data for improved segmentation accuracy.

Main Methods:

Developed TransSeg, a Transformer-based network utilizing a Vision Transformer backbone.
Introduced a Channel-wise Cross Attention (CCA) mechanism to integrate unlabeled sample features into the labeled feature space.
Implemented a Semantic Information Storage (S-InfoStore) module and Channel Semantic Update (CSU) strategy for dynamic feature representation.

Main Results:

TransSeg demonstrated superior performance across all evaluation metrics on the FH-PS-AoP dataset.
The CCA mechanism effectively reconstructed unlabeled features, advancing semi-supervised segmentation.
The S-InfoStore and CSU strategies significantly improved the model's utilization of unlabeled data.

Conclusions:

TransSeg offers an effective and advanced solution for semi-supervised semantic segmentation in medical imaging.
The proposed method reduces the need for large-scale annotated datasets in clinical ultrasound analysis.
TransSeg shows significant potential for improving real-time quantitative evaluation of fetal head descent during labor.