MWG-Net: Multiscale Wavelet Guidance Network for COVID-19 Lung Infection Segmentation From CT Images

Recently, accurate segmentation of the coronavirus disease 2019 (COVID-19) infection from computed tomography (CT) scans is critical to the diagnosis and treatment of COVID-19. However, infection segmentation is a challenging task due to various textures, sizes and locations of infections, low contrast, and blurred boundaries. To address these problems, we propose a novel multiscale wavelet guidance network (MWG-Net) for COVID-19 lung infection by integrating the multiscale information of wavelet domain into the encoder and decoder of the convolutional neural network (CNN). In particular, we propose the wavelet guidance module (WGM) and wavelet and edge guidance module (WEGM). Among them, the WGM guides the encoder to extract infection details through the multiscale spatial and frequency features in the wavelet domain, while the WEGM guides the decoder to recover infection details through the multiscale wavelet representations and multiscale infection edge information. Besides, a progressive fusion module (PFM) is further developed to aggregate and explore multiscale features of the encoder and decoder. Notably, we establish a COVID-19 segmentation dataset (named COVID-Seg-100) containing 5800+ annotated slices for performance evaluation. Furthermore, we conduct extensive experiments to compare our method with other state-of-the-art approaches on our COVID-19-Seg-100 and two publicly available datasets, i.e., MosMedData and COVID-SemiSeg. The results show that our MWG-Net outperforms state-of-the-art methods on different datasets and can achieve more accurate and promising COVID-19 lung infection segmentation.