Focus, Fusion, and Rectify: Context-Aware Learning for COVID-19 Lung Infection Segmentation.
IEEE Trans Neural Netw Learn Syst
; 33(1): 12-24, 2022 01.
Article
in English
| MEDLINE | ID: covidwho-1528340
ABSTRACT
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Tomography, X-Ray Computed
/
COVID-19
Type of study:
Cohort study
/
Diagnostic study
/
Prognostic study
/
Randomized controlled trials
Topics:
Long Covid
Limits:
Humans
Language:
English
Journal:
IEEE Trans Neural Netw Learn Syst
Year:
2022
Document Type:
Article
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