Joint segmentation and detection of COVID-19 via a sequential region generation network.
Pattern Recognit
; 118: 108006, 2021 Oct.
Artículo
en Inglés
| MEDLINE | ID: covidwho-1230705
ABSTRACT
The fast pandemics of coronavirus disease (COVID-19) has led to a devastating influence on global public health. In order to treat the disease, medical imaging emerges as a useful tool for diagnosis. However, the computed tomography (CT) diagnosis of COVID-19 requires experts' extensive clinical experience. Therefore, it is essential to achieve rapid and accurate segmentation and detection of COVID-19. This paper proposes a simple yet efficient and general-purpose network, called Sequential Region Generation Network (SRGNet), to jointly detect and segment the lesion areas of COVID-19. SRGNet can make full use of the supervised segmentation information and then outputs multi-scale segmentation predictions. Through this, high-quality lesion-areas suggestions can be generated on the predicted segmentation maps, reducing the diagnosis cost. Simultaneously, the detection results conversely refine the segmentation map by a post-processing procedure, which significantly improves the segmentation accuracy. The superiorities of our SRGNet over the state-of-the-art methods are validated through extensive experiments on the built COVID-19 database.
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Tipo de estudio:
Estudio pronóstico
Idioma:
Inglés
Revista:
Pattern Recognit
Año:
2021
Tipo del documento:
Artículo
País de afiliación:
J.patcog.2021.108006
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