SELDNet: Sequenced encoder and lightweight decoder network for COVID-19 infection region segmentation

Abstract

Segmenting regions of lung infection from computed tomography (CT) images shows excellent potential for rapid and accurate quantifying of Coronavirus disease 2019 (COVID-19) infection and determining disease development and treatment approaches. However, a number of challenges remain, including the complexity of imaging features and their variability with disease progression, as well as the high similarity to other lung diseases, which makes feature extraction difficult. To answer the above challenges, we propose a new sequence encoder and lightweight decoder network for medical image segmentation model (SELDNet). (i) Construct sequence encoders and lightweight decoders based on Transformer and deep separable convolution, respectively, to achieve different fine-grained feature extraction. (ii) Design a semantic association module based on cross-attention mechanism between encoder and decoder to enhance the fusion of different levels of semantics. The experimental results showed that the network can effectively achieve segmentation of COVID-19 infected regions. The dice of the segmentation result was 79.1%, the sensitivity was 76.3%, and the specificity was 96.7%. Compared with several state-of-the-art image segmentation models, our proposed SELDNet model achieves better results in the segmentation task of COVID-19 infected regions.