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

By Deng-Ping Fan, Tao Zhou, Ge-Peng Ji, Yi Zhou, Geng Chen, Huan Fu, Jianbing Shen, Ling Shao. IEEE Transactions on Medical Imaging, 2020

1,137 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/22f4683316a6b4144d4fe025372dc4024d334148

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detection (2022, background): “Recently, it has attracted ever-growing research interest from the computer vision community and facilitates many valuable real-life applications, such as search and rescue [6], species discovery [36], and medical image analysis [8, 9, 12, 18, 55].”
JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation (2020, background): “More works can refer to the reviews of [18], [19], [32].”
Efficient Test-Time Model Adaptation without Forgetting (2022, background): “Deep neural networks (DNNs) have achieved excellent performance in many challenging tasks, including image clas-siﬁcation (He et al., 2016; Tan & Le, 2019), video recognition (Wang et al., 2018; Chen et al., 2021), and many other areas (Choi et al., 2018; Fan et al., 2020; Xu et al., 2021).”
PraNet: Parallel Reverse Attention Network for Polyp Segmentation (2020, methodology): “We hope this study will oﬀer the community an opportunity to explore more powerful models on the related topics such as lung infection segmentation [9]/classiﬁcation [28], or even on the upstream task, etc .”
Camouflaged Object Detection (2020, background): “, polyp segmentation [14], lung infection segmentation [18, 67]), agriculture (e.”
Cross-level Feature Aggregation Network for Polyp Segmentation (2023, background): “Speciﬁcally, according to several previous works [43,44] , only low-level features preserve suﬃcient boundary information, thus the two low-level layers are integrated, including F 1 and F 2 .”
Camouflaged Object Segmentation with Distraction Mining (2021, background): “…range of valuable applications in different ﬁelds, ranging from medical diagnosis ( e.g. , polyp segmentation [13] and lung infection segmentation [14]), industry ( e.g. , inspection of unqualiﬁed products on the automatic production line), agriculture ( e.g. , Xin Yang (xinyang@dlut.edu.cn) and…”
RGB-D salient object detection: A survey (2020, background): “SOD plays a key role in a range of real-world applications, such as stereo matching [95], image understanding [192], co-saliency detection [33], action recognition [110], video detection and segmentation [35, 118, 140, 141], semantic segmentation [115, 153], medical image segmentation [32, 37, 145],”
Concealed Object Detection (2021, background): “More details on this application can be found in the recent segmentation model [8] and survey paper [92].”
Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model (2021, neutral): “Then, given the speciﬁed states, the transition probability matrix of states R ( m ) , m = 1 , was obtained per each indicator as follows: The tran siti on prob abil ity matr ix of stat es for the numb er of disc harg ed cases”
Simultaneously Localize, Segment and Rank the Camouflaged Objects (2021, background): “Further, an effective camouflaged object detection model has potential to be applied in the field of agriculture for insect control, or in medical image segmentation to detect an infection or tumor area [11, 12].”
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks (2019, background): “, PDC in [112]) or training strategy [113] are promising to increase the performance.”
Res2Net: A New Multi-Scale Backbone Architecture (2019, background): “segmentation [46], salient object detection [21], interactive image segmentation [41], video recognition [37], concealed object detection [18], and medical segmentation [19], [20], [66].”
Recent advances and clinical applications of deep learning in medical image analysis (2021, background): “…and Hossain, 2021; Zhang et al., 2020a; Frid-Adar et al., 2018a; Kumar et al., 2016), ROI segmentation (Alom et al., 2018; Yu et al., 2019; Fan et al., 2020), medical object detection (Rijthoven et al., 2018; Mei et al., 2021; Nair et al., 2020; Zheng et al., 2015), and cross-modality…”