Focus, Fusion, and Rectify: Context-Aware Learning for COVID-19 Lung Infection Segmentation

The coronavirus disease 2019 (COVID-19) pandemic is spreading worldwide. Considering the limited clinicians and resources and the evidence that computed tomography (CT) analysis can achieve comparable sensitivity, specificity, and accuracy with reverse-transcription polymerase chain reaction, the automatic segmentation of lung infection from CT scans supplies a rapid and effective strategy for COVID-19 diagnosis, treatment, and follow-up. It is challenging because the infection appearance has high intraclass variation and interclass indistinction in CT slices. Therefore, a new context-aware neural network is proposed for lung infection segmentation. Specifically, the autofocus and panorama modules are designed for extracting fine details and semantic knowledge and capturing the long-range dependencies of the context from both peer level and cross level. Also, a novel structure consistency rectification is proposed for calibration by depicting the structural relationship between foreground and background. Experimental results on multiclass and single-class COVID-19 CT images demonstrate the effectiveness of our work. In particular, our method obtains the mean intersection over union (mIoU) score of 64.8%, 65.2%, and 73.8% on three benchmark datasets for COVID-19 infection segmentation.

Automated summary

The proposed context-aware neural network for lung infection segmentation utilizes autofocus and panorama modules to extract fine details and semantic knowledge, as well as capturing long-range dependencies of the context. In addition, a novel structure consistency rectification method is introduced for calibration by illustrating the structural relationship between foreground and background. Experimental results demonstrate the effectiveness of the method on multiclass and single-class COVID-19 CT images.