The DOI matches its Crossref record; 87 of the 87 references it cites were found in a registry or an index.
Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images
Open accessAbstract
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.
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Cites
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Self-citationDeep learning based multimodal biomedical data fusion: an overview and comparative review
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Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier
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COVID-19 image classification using deep learning: Advances, challenges and opportunities
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Metadata
- Authors
- Fan, D.-P., Zhou, T., Ji, G.-P., Zhou, Y., Chen, G., Fu, H., Shen, J., & Shao, L.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2020-08
- Volume
- 39
- Issue
- 8
- Pages
- 2626-2637
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W3027763298
- Cited by
- 1,259
- References
- 87
- FWCI
- 97.27
- Type
- journal article
- Language
- en
- Open access
- green
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