MLWF-Net: Multiple lung windows based fusion network for segmentation of small infected areas in COVID-19 CT slices

Automatic infected segmentation on CT enables rapid quantitative analysis of lung involvement in COVID-19 infections. However, it is a challenge to use limited information for segmentation of small infected regions. To deal with the problem, a Multi-Lung Window Fusion Network (MLWF-Net) is proposed to sufficiently utilize CT information. In the first part of MLWF-Net, the multi-lung window block is introduced to address the long-tailed distribution of CT Hounsfield Unit for more features. Then, the adaptive feature aggregation block which combines the attention mechanism with the message passing mechanism is developed to fuse the infection features adaptively from different lung windows CT. The MLWF-Net model is evaluated on a publicly available dataset and a private dataset, compared with a variety of widely-used segmentation models. The MLWF-Net holds on better performance with some benchmarks, especially for small infected areas. The proposed model is an efficient method to alleviate the long tail problem of CT Hounsfield Unit and improve the performance of small infected areas, which can be used for the grading assessment and predictive treatment of COVID-19 patients, with significant clinical value.