MiTU-Net: A fine-tuned U-Net with SegFormer backbone for segmenting pubic symphysis-fetal head

TL;DR

MiTU-Net is a novel deep learning model that automates fetal head and pubic symphysis segmentation in ultrasound images, enabling accurate and efficient angle of progression measurement to assist clinical decision-making.

Contribution

This paper introduces MiTU-Net, a transformer-based U-Net variant that improves segmentation accuracy and reduces parameters for fetal ultrasound analysis.

Findings

Achieves competitive segmentation performance, ranking 5th among tested methods.

Reduces training parameters significantly compared to traditional models.

Facilitates automatic AoP measurement, aiding clinical practice.

Abstract

Ultrasound measurements have been examined as potential tools for predicting the likelihood of successful vaginal delivery. The angle of progression (AoP) is a measurable parameter that can be obtained during the initial stage of labor. The AoP is defined as the angle between a straight line along the longitudinal axis of the pubic symphysis (PS) and a line from the inferior edge of the PS to the leading edge of the fetal head (FH). However, the process of measuring AoP on ultrasound images is time consuming and prone to errors. To address this challenge, we propose the Mix Transformer U-Net (MiTU-Net) network, for automatic fetal head-pubic symphysis segmentation and AoP measurement. The MiTU-Net model is based on an encoder-decoder framework, utilizing a pre-trained efficient transformer to enhance feature representation. Within the efficient transformer encoder, the model significantly reduces the trainable parameters of the encoder-decoder model. The effectiveness of the proposed method is demonstrated through experiments conducted on a recent transperineal ultrasound dataset. Our model achieves competitive performance, ranking 5th compared to existing approaches. The MiTU-Net presents an efficient method for automatic segmentation and AoP measurement, reducing errors and assisting sonographers in clinical practice. Reproducibility: Framework implementation and models available on https://github.com/13204942/MiTU-Net.