  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.
