Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images release_227q3yiporecdjxeixcj4jemhe

by Deng-Ping Fan, Tao Zhou, Ge-Peng Ji, Yi Zhou, Geng Chen, Huazhu Fu, Jianbing Shen, Ling Shao

Published in IEEE Transactions on Medical Imaging by Institute of Electrical and Electronics Engineers (IEEE).

2020   Volume 39, Issue 8, p1-1

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.
In text/plain format

Archived Files and Locations

application/pdf  4.5 MB
file_iwuompitsjfixl7rs3q2753hli
ieeexplore.ieee.org (web)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article-journal
Stage   published
Year   2020
Language   en ?
Container Metadata
Not in DOAJ
In Keepers Registry
ISSN-L:  0278-0062
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: fec06a5e-7458-4242-b915-9bfbb9607d44
API URL: JSON