Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images

Abstract

Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. However, segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. Further, collecting a large amount of data is impractical within a short time period, inhibiting the training of a deep model. To address these challenges, a novel COVID-19 Lung Infection Segmentation Deep Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices. In our Inf-Net, a parallel partial decoder is used to aggregate the high-level features and generate a global map. Then, the implicit reverse attention and explicit edge-attention are utilized to model the boundaries and enhance the representations. Moreover, to alleviate the shortage of labeled data, we present a semi-supervised segmentation framework based on a randomly selected propagation strategy, which only requires a few labeled images and leverages primarily unlabeled data. Our semi-supervised framework can improve the learning ability and achieve a higher performance. Extensive experiments on our COVID-SemiSeg and real CT volumes demonstrate that the proposed Inf-Net outperforms most cutting-edge segmentation models and advances the state-of-the-art performance.

COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet

“…Researchers in [25] detailed the design of a novel DNN structures named Inf-Net and Semi-Inf-Net to semantically segment infected regions, and to segment GGO and consolidation. Their work utilized the same data set that this research is using.…”

Coronavirus disease (COVID-19) prevention and treatment methods and effective parameters: A systematic literature review

“…Fan et al Fan et al (2020) proposed an artificial intelligence-based method for detecting lower respiratory system infections caused by penetrating the virus into the lungs in chest CT. The study employed patent scale application and infection segmentation deep network (Inf-Net) for classifying and multi-class labeling of different types of lung infections to diagnose the disease accurately.…”

Fast deep learning computer-aided diagnosis of COVID-19 based on digital chest x-ray images

“…They achieved overall classification accuracies of 98.08% and 87.02% for the binary and multinomial classifications, respectively. Fan et al [17] proposed a deep learning model called Inf-Net that can be used to identify or segment suspicious regions indicative of COVID-19 on chest CT images. They used a parallel partial decoder to generate the global representation of the final segmented maps.…”