Thyroid Nodule Segmentation Model Integrating Global Reasoning and MLP Architecture
LI Binrong1, XIE Jun1, LI Gang2, XU Xinying3, LAN Zijun1
1.College of Information and Computer, Taiyuan University of Technology, Jinzhong 030600; 2.College of Software, Taiyuan University of Technology, Jin-zhong 030600; 3.College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024
Abstract：To address the problems of large noise interference in ultrasound images and variable nodule size and high computational complexity of the existing thyroid nodule segmentation methods, a segmentation model combining global reasoning and multi-layer perception(MLP) architecture is proposed. The model is based on the axial shift MLP module, and hence the interaction between different spatial location features is realized with less computational complexity. The end-to-end global reasoning unit is integrated into the encoder and the global information interaction is conducted based on graph convolutional networks to alleviate the interference of image noise. The pyramid feature layer is introduced into the decoder and multi-scale feature interaction is performed to deal with the problem of variable nodule size. Experimental results on DDIT datasets show that the proposed model yields better performance, and it can be applied to other medical image segmentation task, such as breast nodule segmentation and retinal vessel segmentation.
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2022, Vol. 35 
