COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet.
BMC Med Imaging
; 21(1): 19, 2021 02 09.
Artículo
en Inglés
| MEDLINE | ID: covidwho-1069551
ABSTRACT
BACKGROUND:
Currently, there is an urgent need for efficient tools to assess the diagnosis of COVID-19 patients. In this paper, we present feasible solutions for detecting and labeling infected tissues on CT lung images of such patients. Two structurally-different deep learning techniques, SegNet and U-NET, are investigated for semantically segmenting infected tissue regions in CT lung images.METHODS:
We propose to use two known deep learning networks, SegNet and U-NET, for image tissue classification. SegNet is characterized as a scene segmentation network and U-NET as a medical segmentation tool. Both networks were exploited as binary segmentors to discriminate between infected and healthy lung tissue, also as multi-class segmentors to learn the infection type on the lung. Each network is trained using seventy-two data images, validated on ten images, and tested against the left eighteen images. Several statistical scores are calculated for the results and tabulated accordingly.RESULTS:
The results show the superior ability of SegNet in classifying infected/non-infected tissues compared to the other methods (with 0.95 mean accuracy), while the U-NET shows better results as a multi-class segmentor (with 0.91 mean accuracy).CONCLUSION:
Semantically segmenting CT scan images of COVID-19 patients is a crucial goal because it would not only assist in disease diagnosis, also help in quantifying the severity of the illness, and hence, prioritize the population treatment accordingly. We propose computer-based techniques that prove to be reliable as detectors for infected tissue in lung CT scans. The availability of such a method in today's pandemic would help automate, prioritize, fasten, and broaden the treatment of COVID-19 patients globally.Palabras clave
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
Interpretación de Imagen Radiográfica Asistida por Computador
/
COVID-19
Tipo de estudio:
Estudio pronóstico
Límite:
Humanos
Idioma:
Inglés
Revista:
BMC Med Imaging
Asunto de la revista:
Diagnóstico por Imagen
Año:
2021
Tipo del documento:
Artículo
País de afiliación:
S12880-020-00529-5
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