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

小波 计算机科学 分割 人工智能 卷积神经网络 模式识别(心理学) 比例(比率) 小波变换 编码器 计算机视觉 地图学 地理 操作系统
作者
Kai Hu,Hui Yuan Tan,Yuan Zhang,Wei Huang,Xieping Gao
出处
期刊:IEEE Transactions on Instrumentation and Measurement [Institute of Electrical and Electronics Engineers]
卷期号:72: 1-15 被引量:4
标识
DOI:10.1109/tim.2023.3265100
摘要

Recently, accurate segmentation of COVID-19 infection from computed tomography (CT) scans is critical for 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 Multi-scale Wavelet Guidance Network (MWG-Net) for COVID-19 lung infection by integrating the multi-scale 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 & Edge Guidance Module (WEGM). Among them, the WGM guides the encoder to extract infection details through the multi-scale spatial and frequency features in the wavelet domain, while the WEGM guides the decoder to recover infection details through the multi-scale wavelet representations and multi-scale infection edge information. Besides, a Progressive Fusion Module (PFM) is further developed to aggregate and explore multi-scale 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.
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