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arXiv · 2210.13012

CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation Network

Fenghe Tang·Lingtao Wang·Chunping Ning·Min Xian·Jianrui Ding
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Abstract

U-Net and its extensions have achieved great success in medical image segmentation. However, due to the inherent local characteristics of ordinary convolution operations, U-Net encoder cannot effectively extract global context information. In addition, simple skip connections cannot capture salient features. In this work, we propose a fully convolutional segmentation network (CMU-Net) which incorporates hybrid convolutions and multi-scale attention gate. The ConvMixer module extracts global context information by mixing features at distant spatial locations. Moreover, the multi-scale attention gate emphasizes valuable features and achieves efficient skip connections. We evaluate the proposed method using both breast ultrasound datasets and a thyroid ultrasound image dataset; and CMU-Net achieves average Intersection over Union (IoU) values of 73.27% and 84.75%, and F1 scores of 84.81% and 91.71%. The code is available at https://github.com/FengheTan9/CMU-Net.

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Fenghe Tang, Lingtao Wang, Chunping Ning, Min Xian, Jianrui Ding. 2022-10-24. CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation Network. https://doi.org/10.1109/isbi53787.2023.10230609

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