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

• URL: http://arxiv.org/abs/2401.15513v1
• Date: Sat, 27 Jan 2024 21:53:05 GMT
• Title: MiTU-Net: A fine-tuned U-Net with SegFormer backbone for segmenting pubic symphysis-fetal head
• Authors: Fangyijie Wang, Guenole Silvestre, Kathleen Curran
• Abstract summary: We propose the Mix Transformer U-Net (MiTU-Net) network for automatic fetal head-pubic symphysis segmentation and AoP measurement. MiTU-Net is based on an encoder-decoder framework, utilizing a pre-trained efficient transformer to enhance feature representation. Our model achieves competitive performance, ranking 5th compared to existing approaches.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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